| MCP server,Category,Description,Tool count,LAB-Bench subgraph,BioAgent-Bench graph | |
| abnumber,single_cell,AbNumber - Antibody numbering using ANARCI.,1,no,no | |
| abricate,transcriptomics,MCP wrapper for abricate.,6,no,yes | |
| abundancebin,single_cell,MCP wrapper for abundancebin.,1,no,no | |
| abyss,transcriptomics,MCP wrapper for abyss.,4,no,no | |
| adapterremoval,single_cell,The AdapterRemoval v2 tool for merging and clipping reads.,3,no,no | |
| alfred,genomics,"BAM alignment statistics, feature counting and feature annotation.",7,no,no | |
| anansescanpy,single_cell,implementation of scANANSE for scanpy objects in Python.,4,no,no | |
| anarci,single_cell,ANARCI: Antibody Numbering and Antigen Receptor ClassIfication.,3,no,no | |
| aragorn,single_cell,MCP wrapper for aragorn.,1,no,no | |
| aria2,transcriptomics,"aria2 is a lightweight multi-protocol & multi-source, cross platform download utility operated in command-line. It supports HTTP/HTTPS, FTP, SFTP, BitTorrent and Metalink.",5,no,no | |
| ariba,genomics,ARIBA: Antibiotic Resistance Identification By Assembly.,7,no,no | |
| arriba,transcriptomics,Fast and accurate gene fusion detection from RNA-Seq data.,2,no,no | |
| art,single_cell,MCP wrapper for art.,4,no,no | |
| arvados-cwl-runner,single_cell,Arvados Common Workflow Language runner.,5,no,no | |
| arvados-python-client,single_cell,"Arvados client library; Python API for Arvados, an open source platform for managing and.",5,no,no | |
| atropos,single_cell,trim adapters from high-throughput sequencing reads.,1,no,no | |
| augur,genomics,Process pathogen genome data for the Nextstrain platform.,1,no,no | |
| augustus,transcriptomics,MCP wrapper for augustus.,1,no,no | |
| auspice,transcriptomics,MCP wrapper for auspice.,7,no,no | |
| bactopia,single_cell,Bactopia is a flexible pipeline for complete analysis of bacterial genomes.,5,no,no | |
| bamtools,genomics,MCP wrapper for bamtools.,13,no,no | |
| barrnap,transcriptomics,MCP wrapper for barrnap.,1,no,no | |
| bbmap,genomics,MCP wrapper for bbmap.,2,no,no | |
| bcbio-gff,single_cell,A Python library to read and write Generic Feature Format (GFF).,4,no,no | |
| bcbio-nextgen,genomics,"Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis.",5,no,no | |
| bcftools,genomics,MCP wrapper for bcftools.,15,yes,yes | |
| bedops,transcriptomics,MCP wrapper for bedops.,10,no,no | |
| bedtools,genomics,MCP wrapper for bedtools.,9,yes,yes | |
| bin2cell,transcriptomics,Join subcellular Visium HD bins into cells.,4,no,no | |
| bioawk,single_cell,MCP wrapper for bioawk.,1,no,no | |
| biobambam,single_cell,Tools for early stage alignment file processing.,11,no,no | |
| bioconductor-affy,single_cell,MCP wrapper for Bioconductor affy.,2,no,no | |
| bioconductor-affyio,single_cell,MCP wrapper for Bioconductor affyio.,5,no,no | |
| bioconductor-affyio copy,general,Auto-indexed MCP server for bioconductor-affyio copy.,1,no,no | |
| bioconductor-alabaster,single_cell,"Umbrella for the Alabaster Framework; Umbrella for the alabaster suite, providing a single-line import for all alabaster.* packages. Installing this package ensures that all known alabaster.* packages are also installed, avoiding problems with missing packages when a staging method or loading function is dynamically requested. Obviously, this comes at the cost of needing to install more packages, so advanced users and application developers may prefer to install the required alabaster.* packages individually.",1,no,no | |
| bioconductor-alabaster.sfe,single_cell,"Language agnostic on disk serialization of SpatialFeatureExperiment; Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment.",2,no,no | |
| bioconductor-alabaster.spatial,single_cell,"Save and Load Spatial 'Omics Data to/from File; Save SpatialExperiment objects and their images into file artifacts, and load them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.",2,no,no | |
| bioconductor-ancombc,single_cell,"Microbiome differential abudance and correlation analyses with bias correction; ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators.",2,no,no | |
| bioconductor-annotate,single_cell,MCP wrapper for Bioconductor annotate.,1,no,no | |
| bioconductor-annotationdbi,single_cell,MCP wrapper for Bioconductor annotationdbi.,5,no,no | |
| bioconductor-annotationfilter,single_cell,"Facilities for Filtering Bioconductor Annotation Resources; This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages.",6,no,no | |
| bioconductor-annotationhub,single_cell,"Client to access AnnotationHub resources; This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access.",1,no,no | |
| bioconductor-apeglm,single_cell,MCP wrapper for Bioconductor apeglm.,1,no,no | |
| bioconductor-apl,single_cell,"Association Plots; APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.",1,no,no | |
| bioconductor-awaggregator,single_cell,"Attribute-Weighted Aggregation; This package implements an attribute-weighted aggregation algorithm which leverages peptide-spectrum match (PSM) attributes to provide a more accurate estimate of protein abundance compared to conventional aggregation methods. This algorithm employs pre-trained random forest models to predict the quantitative inaccuracy of PSMs based on their attributes. PSMs are then aggregated to the protein level using a weighted average, taking the predicted inaccuracy into account. Additionally, the package allows users to construct their own training sets that are more relevant to their specific experimental conditions if desired.",3,no,no | |
| bioconductor-banksy,single_cell,"Spatial transcriptomic clustering; Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.",4,no,no | |
| bioconductor-beachmat,single_cell,MCP wrapper for Bioconductor beachmat.,4,no,no | |
| bioconductor-benchdamic,transcriptomics,"Benchmark of differential abundance methods on microbiome data; Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.",5,no,no | |
| bioconductor-biobase,single_cell,MCP wrapper for Bioconductor biobase.,2,no,no | |
| bioconductor-biocbaseutils,single_cell,"General utility functions for developing Bioconductor packages; The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem.",1,no,no | |
| bioconductor-biocfilecache,single_cell,"Manage Files Across Sessions; This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions.",9,no,no | |
| bioconductor-biocgenerics,single_cell,MCP wrapper for Bioconductor biocgenerics.,2,no,no | |
| bioconductor-biocio,single_cell,"Standard Input and Output for Bioconductor Packages; The `BiocIO` package contains high-level abstract classes and generics used by developers to build IO funcionality within the Bioconductor suite of packages. Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`.",2,no,no | |
| bioconductor-biocneighbors,single_cell,MCP wrapper for Bioconductor biocneighbors.,1,no,no | |
| bioconductor-biocparallel,single_cell,MCP wrapper for Bioconductor biocparallel.,7,no,no | |
| bioconductor-biocsingular,single_cell,MCP wrapper for Bioconductor biocsingular.,3,no,no | |
| bioconductor-biomart,single_cell,MCP wrapper for Bioconductor biomart.,3,no,no | |
| bioconductor-biomformat,single_cell,MCP wrapper for Bioconductor biomformat.,2,no,no | |
| bioconductor-biostrings,single_cell,MCP wrapper for Bioconductor biostrings.,1,no,no | |
| bioconductor-biovizbase,single_cell,"Basic graphic utilities for visualization of genomic data; The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency.",7,no,no | |
| bioconductor-blase,single_cell,"Bulk Linking Analysis for Single-cell Experiments; BLASE is a method for finding where bulk RNA-seq data lies on a single-cell pseudotime trajectory. It uses a fast and understandable approach based on Spearman correlation, with bootstrapping to provide confidence. BLASE can be used to ""date"" bulk RNA-seq data, annotate cell types in scRNA-seq, and help correct for developmental phenotype differences in bulk RNA-seq experiments.",1,no,no | |
| bioconductor-bluster,single_cell,"Clustering Algorithms for Bioconductor; Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.",5,no,no | |
| bioconductor-bsgenome,single_cell,MCP wrapper for Bioconductor bsgenome.,2,no,no | |
| bioconductor-bulksignalr,single_cell,"Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics; Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.",3,no,no | |
| bioconductor-cardspa,single_cell,"Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics; CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.",1,no,no | |
| bioconductor-catscradle,single_cell,"This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters; This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.",4,no,no | |
| bioconductor-cdi,single_cell,"Clustering Deviation Index (CDI); Single-cell RNA-sequencing (scRNA-seq) is widely used to explore cellular variation. The analysis of scRNA-seq data often starts from clustering cells into subpopulations. This initial step has a high impact on downstream analyses, and hence it is important to be accurate. However, there have not been unsupervised metric designed for scRNA-seq to evaluate clustering performance. Hence, we propose clustering deviation index (CDI), an unsupervised metric based on the modeling of scRNA-seq UMI counts to evaluate clustering of cells.",4,no,no | |
| bioconductor-cellhashr,single_cell,An R package designed to demultiplex cell hashing data.,6,no,no | |
| bioconductor-cellid,single_cell,"Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis; CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.",6,no,no | |
| bioconductor-cellmigration,single_cell,"Track Cells, Analyze Cell Trajectories and Compute Migration Statistics; Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions.",1,no,no | |
| bioconductor-clusterfoldsimilarity,single_cell,Calculate similarity of clusters from different single cell samples using foldchanges; This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m.,2,no,no | |
| bioconductor-clusterprofiler,pathway_enrichment,"A universal enrichment tool for interpreting omics data; This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a universal interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct gene clusters.",5,yes,no | |
| bioconductor-clustifyr,single_cell,MCP wrapper for Bioconductor clustifyr.,1,no,no | |
| bioconductor-clustsignal,single_cell,"ClustSIGNAL: a spatial clustering method; clustSIGNAL: clustering of Spatially Informed Gene expression with Neighbourhood Adapted Learning. A tool for adaptively smoothing and clustering gene expression data. clustSIGNAL uses entropy to measure heterogeneity of cell neighbourhoods and performs a weighted, adaptive smoothing, where homogeneous neighbourhoods are smoothed more and heterogeneous neighbourhoods are smoothed less. This not only overcomes data sparsity but also incorporates spatial context into the gene expression data. The resulting smoothed gene expression data is used for clustering and could be used for other downstream analyses.",4,no,no | |
| bioconductor-complexheatmap,single_cell,Make Complex Heatmaps; Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics.,2,no,no | |
| bioconductor-concordexr,transcriptomics,"Identify Spatial Homogeneous Regions with concordex; Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.",2,no,no | |
| bioconductor-cotan,single_cell,"COexpression Tables ANalysis; Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.",1,no,no | |
| bioconductor-csoa,single_cell,"Calculate per-cell gene signature scores in scRNA-seq data using cell set overlaps; Cell Set Overlap Analysis (CSOA) is a tool for calculating per-cell gene signature scores in an scRNA-seq dataset. CSOA constructs a set for each gene in the signature, consisting of the cells that highly express the gene. Next, all overlaps of pairs of cell sets are computed, ranked, filtered and scored. The CSOA per-cell score is calculated by summing up all products of the overlap scores and the min-max-normalized expression of the two involved genes. CSOA can run on a Seurat object, a SingleCellExperiment object, a matrix and a dgCMatrix.",1,no,no | |
| bioconductor-ctsv,single_cell,"Identification of cell-type-specific spatially variable genes accounting for excess zeros; The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS.",2,no,no | |
| bioconductor-curatedatlasqueryr,single_cell,"Queries the Human Cell Atlas; Provides access to a copy of the Human Cell Atlas, but with harmonised metadata. This allows for uniform querying across numerous datasets within the Atlas using common fields such as cell type, tissue type, and patient ethnicity. Usage involves first querying the metadata table for cells of interest, and then downloading the corresponding cells into a SingleCellExperiment object.",6,yes,no | |
| bioconductor-cytomapper,single_cell,MCP wrapper for Bioconductor cytomapper.,2,no,no | |
| bioconductor-dada2,genomics,"Accurate, high-resolution sample inference from amplicon sequencing data; The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.",9,yes,no | |
| bioconductor-data-packages,single_cell,A package to enable downloading and installation of Bioconductor data packages.,6,no,no | |
| bioconductor-decipher,single_cell,"Tools for curating, analyzing, and manipulating biological sequences; A toolset for deciphering and managing biological sequences.",5,no,no | |
| bioconductor-decontam,single_cell,"Identify Contaminants in Marker-gene and Metagenomics Sequencing Data; Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples.",1,no,no | |
| bioconductor-decontx,single_cell,"Decontamination of single cell genomics data; This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.",2,no,no | |
| bioconductor-deconvobuddies,single_cell,"Helper Functions for LIBD Deconvolution; Funtions helpful for LIBD deconvolution project. Includes tools for marker finding with mean ratio, expression plotting, and plotting deconvolution results. Working to include DLPFC datasets.",6,no,no | |
| bioconductor-delayedarray,general,"A unified framework for working transparently with on-disk and in-memory array-like datasets; Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames.",1,no,no | |
| bioconductor-delayedmatrixstats,transcriptomics,"Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects; A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.",1,no,no | |
| bioconductor-deseq2,transcriptomics,Differential gene expression analysis based on the negative binomial distribution; Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.,4,no,yes | |
| bioconductor-despace,transcriptomics,"DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions; Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions.",3,no,no | |
| bioconductor-diffbind,transcriptomics,Differential Binding Analysis of ChIP-Seq Peak Data; Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.,1,no,no | |
| bioconductor-dino,single_cell,"Normalization of Single-Cell mRNA Sequencing Data; Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.",1,no,no | |
| bioconductor-dirichletmultinomial,single_cell,MCP wrapper for Bioconductor dirichletmultinomial.,2,no,no | |
| bioconductor-dnacopy,single_cell,MCP wrapper for Bioconductor dnacopy.,2,no,no | |
| bioconductor-dose,pathway_enrichment,"Disease Ontology Semantic and Enrichment analysis; This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.",6,yes,no | |
| bioconductor-ebseq,general,An R package for gene and isoform differential expression analysis of RNA-seq data; Differential Expression analysis at both gene and isoform level using RNA-seq data.,1,no,no | |
| bioconductor-edger,single_cell,MCP wrapper for Bioconductor edger.,2,no,no | |
| bioconductor-ensembldb,genomics,"Utilities to create and use Ensembl-based annotation databases; The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates.",6,yes,no | |
| bioconductor-erma,single_cell,epigenomic road map adventures; Software and data to support epigenomic road map adventures.,1,no,no | |
| bioconductor-escher,single_cell,"Unified multi-dimensional visualizations with Gestalt principles; The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.",4,no,no | |
| bioconductor-experimentsubset,single_cell,"Manages subsets of data with Bioconductor Experiment objects; Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset.",2,no,no | |
| bioconductor-fgsea,pathway_enrichment,"Fast Gene Set Enrichment Analysis; The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction.",3,yes,no | |
| bioconductor-genefilter,single_cell,MCP wrapper for Bioconductor genefilter.,3,no,no | |
| bioconductor-geneplotter,single_cell,MCP wrapper for Bioconductor geneplotter.,3,no,no | |
| bioconductor-genomeinfodb,single_cell,"Utilities for manipulating chromosome names, including modifying them to follow a particular naming style; Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., ""chr1"" versus ""1""), including a function that attempts to place sequence names in their natural, rather than lexicographic, order.",4,no,no | |
| bioconductor-genomeinfodbdata,single_cell,MCP wrapper for Bioconductor genomeinfodbdata.,2,no,no | |
| bioconductor-genomicalignments,single_cell,MCP wrapper for Bioconductor genomicalignments.,1,no,no | |
| bioconductor-genomicfeatures,single_cell,MCP wrapper for Bioconductor genomicfeatures.,1,no,no | |
| bioconductor-genomicranges,transcriptomics,"Representation and manipulation of genomic intervals; The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure.",11,no,no | |
| bioconductor-geomxtools,single_cell,NanoString GeoMx Tools; Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.,7,no,no | |
| bioconductor-ggsc,single_cell,"Visualizing Single Cell and Spatial Transcriptomics; Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.",5,no,no | |
| bioconductor-ggspavis,single_cell,"Visualization functions for spatial transcriptomics data; Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format.",4,no,no | |
| bioconductor-ggtree,single_cell,an R package for visualization of tree and annotation data; 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.,1,no,no | |
| bioconductor-glmgampoi,single_cell,Fit a Gamma-Poisson Generalized Linear Model; Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.,4,no,no | |
| bioconductor-go.db,pathway_enrichment,MCP wrapper for Bioconductor go.db.,2,no,no | |
| bioconductor-gosemsim,single_cell,MCP wrapper for Bioconductor gosemsim.,7,no,no | |
| bioconductor-graph,single_cell,MCP wrapper for Bioconductor graph.,1,no,no | |
| bioconductor-gsva,pathway_enrichment,MCP wrapper for Bioconductor gsva.,1,no,no | |
| bioconductor-hcatonsildata,single_cell,"Provide programmatic access to the tonsil cell atlas datasets; This package provides access to the scRNA-seq, scATAC-seq, multiome, CITE-seq and spatial transcriptomics (Visium) data generated by the tonsil cell atlas in the context of the Human Cell Atlas (HCA). The data is provided via the Bioconductor project in the form of SingleCellExperiments. Additionally, information on the whole compendium of identified cell types is provided in form of a glossary.",1,no,no | |
| bioconductor-hdf5array,transcriptomics,"HDF5 datasets as array-like objects in R; The HDF5Array package is an HDF5 backend for DelayedArray objects. It implements the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects.",4,no,no | |
| bioconductor-hoodscanr,single_cell,"Spatial cellular neighbourhood scanning in R; hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering.",6,no,no | |
| bioconductor-humanhippocampus2024,single_cell,Access to SRT and snRNA-seq data from spatial_HPC project; This is an ExperimentHub Data package that helps to access the spatially-resolved transcriptomics and single-nucleus RNA sequencing data. The datasets are generated from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. The datasets are based on [spatial_hpc](https: project by Lieber Institute for Brain Development (LIBD) researchers and collaborators.,2,no,no | |
| bioconductor-imcdatasets,single_cell,"Collection of publicly available imaging mass cytometry (IMC) datasets; The imcdatasets package provides access to publicly available IMC datasets. IMC is a technology that enables measurement of > 40 proteins from tissue sections. The generated images can be segmented to extract single cell data. Datasets typically consist of three elements: a SingleCellExperiment object containing single cell data, a CytoImageList object containing multichannel images and a CytoImageList object containing the cell masks that were used to extract the single cell data from the images.",2,no,no | |
| bioconductor-imcrtools,single_cell,"Methods for imaging mass cytometry data analysis; This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.",4,no,no | |
| bioconductor-impute,single_cell,MCP wrapper for Bioconductor impute.,2,no,no | |
| bioconductor-infercnv,single_cell,MCP wrapper for Bioconductor infercnv.,3,no,no | |
| bioconductor-interactivedisplaybase,single_cell,Base package for enabling powerful shiny web displays of Bioconductor objects; The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects.,1,no,no | |
| bioconductor-iranges,single_cell,MCP wrapper for Bioconductor iranges.,5,no,no | |
| bioconductor-irisfgm,single_cell,"Comprehensive Analysis of Gene Interactivity Networks Based on Single-Cell RNA-Seq; Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes.",1,no,no | |
| bioconductor-jazzpanda,single_cell,"Finding spatially relevant marker genes in image based spatial transcriptomics data; This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.",3,no,no | |
| bioconductor-keggrest,single_cell,MCP wrapper for Bioconductor keggrest.,6,no,no | |
| bioconductor-limma,single_cell,MCP wrapper for Bioconductor limma.,1,no,no | |
| bioconductor-lisaclust,single_cell,lisaClust: Clustering of Local Indicators of Spatial Association; lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.,5,no,no | |
| bioconductor-mastr,pathway_enrichment,"Markers Automated Screening Tool in R; mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of group markers in various biological and medical applications, including cancer research and developmental biology.",3,no,no | |
| bioconductor-matrixgenerics,single_cell,S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects; S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities.,2,no,no | |
| bioconductor-merfishdata,single_cell,Collection of public MERFISH datasets; MerfishData is an ExperimentHub package that serves publicly available datasets obtained with Multiplexed Error-Robust Fluorescence in situ Hybridization (MERFISH). MERFISH is a massively multiplexed single-molecule imaging technology capable of simultaneously measuring the copy number and spatial distribution of hundreds to tens of thousands of RNA species in individual cells. The scope of the package is to provide MERFISH data for benchmarking and analysis.,2,no,no | |
| bioconductor-metabolomicsworkbenchr,single_cell,MCP wrapper for Bioconductor metabolomicsworkbenchr.,1,no,no | |
| bioconductor-metapod,genomics,"Meta-Analyses on P-Values of Differential Analyses; Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate.",2,no,no | |
| bioconductor-mia,single_cell,"Microbiome analysis; mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.",1,no,no | |
| bioconductor-moleculeexperiment,utility,"Prioritising a molecule-level storage of Spatial Transcriptomics Data; MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors.",7,no,no | |
| bioconductor-mosim,transcriptomics,MCP wrapper for Bioconductor mosim.,1,no,no | |
| bioconductor-mousegastrulationdata,single_cell,"Single-Cell -omics Data across Mouse Gastrulation and Early Organogenesis; Provides processed and raw count data for single-cell RNA sequencing, single-cell ATAC-seq, and seqFISH (spatial transcriptomic) experiments performed along a timecourse of mouse gastrulation and early organogenesis.",3,no,no | |
| bioconductor-mspurity,single_cell,"Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics; msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the ""precursor ion purity"". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call ""Precursor ion purity"" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation.",6,no,no | |
| bioconductor-multiassayexperiment,single_cell,"Software for the integration of multi-omics experiments in Bioconductor; Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis.",5,no,no | |
| bioconductor-multtest,single_cell,MCP wrapper for Bioconductor multtest.,2,no,no | |
| bioconductor-mzr,general,"parser for netCDF, mzXML and mzML and mzIdentML files (mass spectrometry data); mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS.",1,no,no | |
| bioconductor-nebulosa,single_cell,MCP wrapper for Bioconductor nebulosa.,2,no,no | |
| bioconductor-nnsvg,single_cell,Scalable identification of spatially variable genes in spatially-resolved transcriptomics data; Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.,1,no,no | |
| bioconductor-noiseq,single_cell,MCP wrapper for Bioconductor noiseq.,2,no,no | |
| bioconductor-orfhunter,single_cell,"Predict open reading frames in nucleotide sequences; The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells.",5,no,no | |
| bioconductor-org.ce.eg.db,single_cell,MCP wrapper for Bioconductor org.ce.eg.db.,6,no,no | |
| bioconductor-org.hs.eg.db,single_cell,MCP wrapper for Bioconductor org.hs.eg.db.,8,no,no | |
| bioconductor-org.mm.eg.db,single_cell,MCP wrapper for Bioconductor org.mm.eg.db.,5,no,no | |
| bioconductor-partcnv,single_cell,"Infer locally aneuploid cells using single cell RNA-seq data; This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).",3,no,no | |
| bioconductor-phemd,single_cell,MCP wrapper for Bioconductor phemd.,1,no,no | |
| bioconductor-phyloseq,single_cell,"Handling and analysis of high-throughput microbiome census data; phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.",5,no,no | |
| bioconductor-pipecomp,single_cell,"pipeComp pipeline benchmarking framework; A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data.",1,no,no | |
| bioconductor-poem,single_cell,"POpulation-based Evaluation Metrics; This package provides a comprehensive set of external and internal evaluation metrics. It includes metrics for assessing partitions or fuzzy partitions derived from clustering results, as well as for evaluating subpopulation identification results within embeddings or graph representations. Additionally, it provides metrics for comparing spatial domain detection results against ground truth labels, and tools for visualizing spatial errors.",1,no,no | |
| bioconductor-preprocesscore,single_cell,MCP wrapper for Bioconductor preprocesscore.,8,no,no | |
| bioconductor-proteomicsannotationhubdata,single_cell,MCP wrapper for Bioconductor proteomicsannotationhubdata.,2,no,no | |
| bioconductor-protgenerics,single_cell,MCP wrapper for Bioconductor protgenerics.,1,no,no | |
| bioconductor-qvalue,single_cell,"Q-value estimation for false discovery rate control; This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.",2,no,no | |
| bioconductor-rbgl,single_cell,MCP wrapper for Bioconductor rbgl.,1,no,no | |
| bioconductor-reactomegsa.data,single_cell,Companion data package for the ReactomeGSA package; Companion data sets to showcase the functionality of the ReactomeGSA package. This package contains proteomics and RNA-seq data of the melanoma B-cell induction study by Griss et al. and scRNA-seq data from Jerby-Arnon et al.,1,no,no | |
| bioconductor-regionalst,pathway_enrichment,Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data; This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application.,4,no,no | |
| bioconductor-rforproteomics,single_cell,MCP wrapper for Bioconductor rforproteomics.,1,no,no | |
| bioconductor-rgraphviz,single_cell,MCP wrapper for Bioconductor rgraphviz.,1,no,no | |
| bioconductor-rhdf5,single_cell,MCP wrapper for Bioconductor rhdf5.,1,no,no | |
| bioconductor-rhdf5filters,single_cell,"HDF5 Compression Filters; Provides a collection of additional compression filters for HDF5 datasets. The package is intended to provide seemless integration with rhdf5, however the compiled filters can also be used with external applications.",1,no,no | |
| bioconductor-rhdf5lib,single_cell,hdf5 library as an R package; Provides C and C++ hdf5 libraries.,1,no,no | |
| bioconductor-rhtslib,single_cell,MCP wrapper for Bioconductor rhtslib.,1,no,no | |
| bioconductor-rsamtools,single_cell,MCP wrapper for Bioconductor rsamtools.,5,yes,no | |
| bioconductor-rsubread,transcriptomics,MCP wrapper for Bioconductor rsubread.,8,no,yes | |
| bioconductor-rtracklayer,single_cell,MCP wrapper for Bioconductor rtracklayer.,4,no,no | |
| bioconductor-rtracklayer copy,general,Auto-indexed MCP server for bioconductor-rtracklayer copy.,1,no,no | |
| bioconductor-s4vectors,single_cell,Auto-indexed MCP server for bioconductor-s4vectors.,1,no,no | |
| bioconductor-scaledmatrix,single_cell,"Creating a DelayedMatrix of Scaled and Centered Values; Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication.",6,no,no | |
| bioconductor-scalign,single_cell,"An alignment and integration method for single cell genomics; An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) <doi:10.1101/504944> for more details.",1,no,no | |
| bioconductor-scarray.sat,single_cell,"Large-scale single-cell RNA-seq data analysis using GDS files and Seurat; Extends the Seurat classes and functions to support Genomic Data Structure (GDS) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the SCArray package to represent single cell RNA-seq data. The common optimized algorithms leveraging GDS-based and single cell-specific DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package. SCArray.sat introduces a new SCArrayAssay class (derived from the Seurat Assay), which wraps raw counts, normalized expressions and scaled data matrix based on GDS-specific DelayedMatrix. It is designed to integrate seamlessly with the Seurat package to provide common data analysis in the SeuratObject-based workflow. Compared with Seurat, SCArray.sat significantly reduces the memory usage without downsampling and can be applied to very large datasets.",1,no,no | |
| bioconductor-scater,single_cell,"Single-Cell Analysis Toolkit for Gene Expression Data in R; A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.",1,no,no | |
| bioconductor-scbfa,single_cell,MCP wrapper for Bioconductor scbfa.,2,no,no | |
| bioconductor-scbubbletree,single_cell,"Quantitative visual exploration of scRNA-seq data; scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.",1,no,no | |
| bioconductor-sccb2,single_cell,MCP wrapper for Bioconductor sccb2.,1,no,no | |
| bioconductor-scclassifr,single_cell,Pretrained learning models for cell type prediction on single cell RNA-sequencing data; The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scClassifR also allows users to train their own models to predict new cell types based on specific research needs.,3,no,no | |
| bioconductor-scdataviz,single_cell,MCP wrapper for Bioconductor scdataviz.,3,no,no | |
| bioconductor-scdotplot,single_cell,Cluster a Single-cell RNA-seq Dot Plot; Dot plots of single-cell RNA-seq data allow for an examination of the relationships between cell groupings (e.g. clusters) and marker gene expression. The scDotPlot package offers a unified approach to perform a hierarchical clustering analysis and add annotations to the columns and/or rows of a scRNA-seq dot plot. It works with SingleCellExperiment and Seurat objects as well as data frames.,3,no,no | |
| bioconductor-scfeatures,pathway_enrichment,scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction; scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.,2,no,no | |
| bioconductor-scider,single_cell,"Spatial cell-type inter-correlation by density in R; scider is an user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis.",5,no,no | |
| bioconductor-scmageck,single_cell,MCP wrapper for Bioconductor scmageck.,4,no,no | |
| bioconductor-scqtltools,single_cell,"scQTLtools: an R/Bioconductor package for comprehensive identification and visualization of single-cell eQTLs; scQTLtools is a comprehensive R/Bioconductor package that facilitates end-to-end single-cell eQTL analysis, from preprocessing to visualization.",1,no,no | |
| bioconductor-scran,single_cell,"Methods for Single-Cell RNA-Seq Data Analysis; Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.",1,no,no | |
| bioconductor-screpertoire,single_cell,MCP wrapper for Bioconductor screpertoire.,1,no,no | |
| bioconductor-scrnaseq,transcriptomics,MCP wrapper for Bioconductor scrnaseq.,1,no,no | |
| bioconductor-sctreeviz,single_cell,R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations; scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters.,2,no,no | |
| bioconductor-scuttle,single_cell,"Single-Cell RNA-Seq Analysis Utilities; Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.",3,no,no | |
| bioconductor-scvir,single_cell,"experimental inferface from R to scvi-tools; This package defines interfaces from R to scvi-tools. A vignette works through the totalVI tutorial for analyzing CITE-seq data. Another vignette compares outputs of Chapter 12 of the OSCA book with analogous outputs based on totalVI quantifications. Future work will address other components of scvi-tools, with a focus on building understanding of probabilistic methods based on variational autoencoders.",5,no,no | |
| bioconductor-seraster,single_cell,"Rasterization Preprocessing Framework for Scalable Spatial Omics Data Analysis; SEraster is a rasterization preprocessing framework that aggregates cellular information into spatial pixels to reduce resource requirements for spatial omics data analysis. SEraster reduces the number of spatial points in spatial omics datasets for downstream analysis through a process of rasterization where single cells’ gene expression or cell-type labels are aggregated into equally sized pixels based on a user-defined resolution. SEraster is built on an R/Bioconductor S4 class called SpatialExperiment. SEraster can be incorporated with other packages to conduct downstream analyses for spatial omics datasets, such as detecting spatially variable genes.",3,no,no | |
| bioconductor-shortread,single_cell,MCP wrapper for Bioconductor shortread.,6,no,no | |
| bioconductor-signifinder,pathway_enrichment,"Collection and implementation of public transcriptional cancer signatures; signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures coming from public literature, based on gene expression values, and return single-sample (-cell/-spot) scores. Currently, signifinder collects more than 70 distinct signatures, relating to multiple tumors and multiple cancer processes.",1,no,no | |
| bioconductor-singlecellexperiment,single_cell,"S4 Classes for Single Cell Data; Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.",1,no,no | |
| bioconductor-singlecellmultimodal,single_cell,MCP wrapper for Bioconductor singlecellmultimodal.,1,no,no | |
| bioconductor-smoothclust,single_cell,"smoothclust; Method for identification of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses.",1,no,no | |
| bioconductor-smoppix,single_cell,"Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index; Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.",4,no,no | |
| bioconductor-sosta,single_cell,A package for the analysis of anatomical tissue structures in spatial omics data; sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs anatomically relevant structures based on molecular features or cell types. It further calculates a range of metrics at the structure level to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial omics data.,4,no,no | |
| bioconductor-spacetrooper,single_cell,"SpaceTrooper performs Quality Control analysis of Image-Based spatial; SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package.",5,no,no | |
| bioconductor-spaniel,single_cell,MCP wrapper for Bioconductor spaniel.,2,no,no | |
| bioconductor-spari,single_cell,"Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering; The R package used in the manuscript ""Spatially Aware Adjusted Rand Index for Evaluating Spatial Transcritpomics Clustering"".",3,no,no | |
| bioconductor-sparsematrixstats,transcriptomics,"Summary Statistics for Rows and Columns of Sparse Matrices; High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson.",1,no,no | |
| bioconductor-spatialcpie,single_cell,MCP wrapper for Bioconductor spatialcpie.,1,no,no | |
| bioconductor-spatialdatasets,single_cell,"Collection of spatial omics datasets; This is a collection of publically available spatial omics datasets. Where possible we have curated these datasets as either SpatialExperiments, MoleculeExperiments or CytoImageLists and included annotations of the sample characteristics.",3,no,no | |
| bioconductor-spatialdecon,single_cell,MCP wrapper for Bioconductor spatialdecon.,1,no,no | |
| bioconductor-spatialdmelxsim,single_cell,"Spatial allelic expression counts for fly cross embryo; Spatial allelic expression counts from Combs & Fraser (2018), compiled into a SummarizedExperiment object. This package contains data of allelic expression counts of spatial slices of a fly embryo, a Drosophila melanogaster x Drosophila simulans cross. See the CITATION file for the data source, and the associated script for how the object was constructed from publicly available data.",4,no,no | |
| bioconductor-spatialexperiment,single_cell,MCP wrapper for Bioconductor spatialexperiment.,2,no,no | |
| bioconductor-spatialexperimentio,transcriptomics,"Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object; Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.",5,no,no | |
| bioconductor-spatialfda,single_cell,"A Tool for Spatial Multi-sample Comparisons; spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.",5,no,no | |
| bioconductor-spatialfeatureexperiment,single_cell,"Integrating SpatialExperiment with Simple Features in sf; A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.",2,no,no | |
| bioconductor-spatialheatmap,single_cell,MCP wrapper for Bioconductor spatialheatmap.,5,no,no | |
| bioconductor-spatialomicsoverlay,single_cell,Spatial Overlay for Omic Data from Nanostring GeoMx Data; Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.,1,no,no | |
| bioconductor-speckle,single_cell,"Statistical methods for analysing single cell RNA-seq data; The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.",6,no,no | |
| bioconductor-spoon,single_cell,"Address the Mean-variance Relationship in Spatial Transcriptomics Data; This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools.",1,no,no | |
| bioconductor-spotclean,single_cell,"SpotClean adjusts for spot swapping in spatial transcriptomics data; SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.",2,no,no | |
| bioconductor-spotsweeper,single_cell,"Spatially-aware quality control for spatial transcriptomics; Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts.",4,no,no | |
| bioconductor-standr,single_cell,"Spatial transcriptome analyses of Nanostring's DSP data in R; standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.",1,no,no | |
| bioconductor-statial,single_cell,A package to identify changes in cell state relative to spatial associations; Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.,5,no,no | |
| bioconductor-stjoincount,single_cell,"stJoincount - Join count statistic for quantifying spatial correlation between clusters; stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate ""joins"" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.",4,no,no | |
| bioconductor-summarizedexperiment,single_cell,"A container (S4 class) for matrix-like assays; The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples.",1,no,no | |
| bioconductor-svp,transcriptomics,"Predicting cell states and their variability in single-cell or spatial omics data; SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.",2,no,no | |
| bioconductor-tenxvisiumdata,single_cell,"Visium spatial gene expression data by 10X Genomics; Collection of Visium spatial gene expression datasets by 10X Genomics, formatted into objects of class SpatialExperiment. Data cover various organisms and tissues, and include: single- and multi-section experiments, as well as single sections subjected to both whole transcriptome and targeted panel analysis. Datasets may be used for testing of and as examples in packages, for tutorials and workflow demonstrations, or similar purposes.",1,no,no | |
| bioconductor-tenxxeniumdata,single_cell,"Collection of Xenium spatial data by 10X genomics; Collection of Xenium spatial transcriptomics datasets provided by 10x Genomics, formatted into the Bioconductor classes, the SpatialExperiment or SpatialFeatureExperiment (SFE), to facilitate seamless integration into various applications, including examples, demonstrations, and tutorials. The constructed data objects include gene expression profiles, per-transcript location data, centroid, segmentation boundaries (e.g., cell or nucleus boundaries), and image.",1,no,no | |
| bioconductor-tidyomics,single_cell,"Easily install and load the tidyomics ecosystem; The tidyomics ecosystem is a set of packages for ’omic data analysis that work together in harmony; they share common data representations and API design, consistent with the tidyverse ecosystem. The tidyomics package is designed to make it easy to install and load core packages from the tidyomics ecosystem with a single command.",3,no,no | |
| bioconductor-tidyspatialexperiment,single_cell,"SpatialExperiment with tidy principles; tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object.",2,no,no | |
| bioconductor-treesummarizedexperiment,single_cell,TreeSummarizedExperiment: a S4 Class for Data with Tree Structures; TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.,1,no,no | |
| bioconductor-tximport,transcriptomics,MCP wrapper for Bioconductor tximport.,3,no,yes | |
| bioconductor-variantannotation,single_cell,MCP wrapper for Bioconductor variantannotation.,3,yes,no | |
| bioconductor-vectrapolarisdata,single_cell,Vectra Polaris and Vectra 3 multiplex single-cell imaging data; Provides two multiplex imaging datasets collected on Vectra instruments at the University of Colorado Anschutz Medical Campus. Data are provided as a Spatial Experiment objects. Data is provided in tabular form and has been segmented and phenotyped using Inform software. Raw .tiff files are not included.,4,no,no | |
| bioconductor-visiumio,single_cell,"Import Visium data from the 10X Space Ranger pipeline; The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects.",2,no,no | |
| bioconductor-weberdivechalcdata,single_cell,Spatially-resolved transcriptomics and single-nucleus RNA-sequencing data from the locus coeruleus (LC) in postmortem human brain samples; Spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from the locus coeruleus (LC) in postmortem human brain samples. Data were generated with the 10x Genomics Visium SRT and 10x Genomics Chromium snRNA-seq platforms. Datasets are stored in SpatialExperiment and SingleCellExperiment formats.,2,no,no | |
| bioconductor-xcms,single_cell,"LC-MS and GC-MS Data Analysis; Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling.",1,no,no | |
| bioconductor-xenlite,single_cell,"Simple classes and methods for managing Xenium datasets; Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage.",5,no,no | |
| bioconductor-xvector,single_cell,MCP wrapper for Bioconductor xvector.,8,no,no | |
| bioconductor-zlibbioc,single_cell,MCP wrapper for Bioconductor zlibbioc.,1,no,no | |
| biopython,single_cell,Freely available tools for computational molecular biology.,1,no,no | |
| blast,transcriptomics,MCP wrapper for blast.,5,yes,yes | |
| blast-legacy,single_cell,The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences.,2,yes,no | |
| bowtie,transcriptomics,MCP wrapper for bowtie.,2,no,no | |
| bowtie2,transcriptomics,MCP wrapper for bowtie2.,1,no,yes | |
| bpipe,single_cell,MCP wrapper for bpipe.,10,no,no | |
| brooklyn_plot,genomics,MCP wrapper for brooklyn plot.,1,no,no | |
| busco,single_cell,"Assessment of assembly completeness using Universal Single Copy Orthologs; BUSCO provides measures for quantitative assessment of genome assembly, gene set, and transcriptome completeness based on evolutionarily informed expectations of gene content from near-universal single-copy orthologs selected from OrthoDB.",4,no,yes | |
| bwa,genomics,MCP wrapper for bwa.,11,no,yes | |
| bx-python,single_cell,"Tools for manipulating biological data, particularly multiple sequence alignments.",5,no,no | |
| c-ares,single_cell,c-ares is a C library for asynchronous DNS requests (including name resolves).,2,no,no | |
| cascade-reg,single_cell,Causal discovery of gene regulatory programs from single-cell genomics; CASCADE stands for Causality-Aware Single-Cell Adaptive.,7,no,no | |
| cd-hit,single_cell,MCP wrapper for cd hit.,1,no,no | |
| cdbtools,single_cell,CDB (Constant DataBase) indexing and retrieval tools for FASTA files.,3,no,no | |
| cell2cell,pathway_enrichment,Inferring cell-cell interactions from transcriptomes with cell2cell.,2,no,no | |
| cellitac,single_cell,Cell type identification using Transcription factor Analysis and Chromatin accessibility.,1,no,no | |
| cellqc,single_cell,Cellqc standardizes the qualiy control of single-cell RNA-Seq (scRNA) data to render clean feature count matrices.,1,no,no | |
| cellrank,general,MCP wrapper for cellrank.,1,no,no | |
| cellsnake,single_cell,"cellsnake, a user-friendly tool for single cell RNA sequencing analysis.",5,no,no | |
| celltypist,single_cell,MCP wrapper for celltypist.,3,no,no | |
| celltypist-so,single_cell,Fork of CellTypist without leidenalg in the package requirements.,2,no,no | |
| checkatlas,single_cell,One liner tool to check the quality of your single-cell atlases.,2,no,no | |
| checkm-genome,genomics,"Assess the quality of microbial genomes recovered from isolates, single cells, and metagenomes.",16,no,no | |
| circexplorer2,genomics,Circular RNA analysis toolkits.,4,no,no | |
| circos,transcriptomics,MCP wrapper for circos.,6,no,no | |
| clustalo,single_cell,MCP wrapper for clustalo.,1,no,no | |
| clustalw,single_cell,MCP wrapper for clustalw.,2,no,no | |
| cmappy,single_cell,"Assorted tools for interacting with .gct, .gctx, .grp, and .gmt files as well as other Connectivity Map (Broad Institute) data/tools.",5,no,no | |
| cnmf,single_cell,MCP wrapper for cnmf.,5,no,no | |
| cnvkit,genomics,Copy number variant detection from high-throughput sequencing.,24,no,no | |
| comebin,genomics,COMEBin allows effective binning of metagenomic contigs using COntrastive Multi-viEw representation learning.,4,no,no | |
| comet-ms,single_cell,Comet is an open source tandem mass spectrometry (MS/MS) sequence database search tool.,1,no,no | |
| constellations,single_cell,MCP wrapper for constellations.,1,no,no | |
| cooler,genomics,Sparse binary format for genomic interaction matrices.,15,no,no | |
| cooltools,genomics,Analysis tools for genomic interaction data stored in .cool format.,9,no,no | |
| coreutils,single_cell,"The GNU Core Utilities are the basic file, shell and text manipulation utilities of the GNU operating system. These are the core utilities which are expected to exist on every operating system.",12,no,no | |
| cosg,single_cell,Accurate and fast cell marker gene identification with COSG.,1,no,no | |
| cospar,pathway_enrichment,A toolkit for dynamic inference of cell fate by integrating state and lineage information.,6,no,no | |
| crispresso2,genomics,A software pipeline designed to enable rapid and intuitive interpretation of genome editing experiments.,6,no,no | |
| crispritz,genomics,"CRISPRitz, tool package for CRISPR experiments assessment and analysis.",4,no,no | |
| crisprme,single_cell,"CRISPRme, tool package for CRISPR experiments assessment and analysis.",1,no,no | |
| cromwell,transcriptomics,MCP wrapper for cromwell.,4,no,no | |
| csvtk,utility,MCP wrapper for csvtk.,40,no,yes | |
| csvtk copy,utility,Auto-indexed MCP server for csvtk copy.,1,no,no | |
| cutadapt,single_cell,Trim adapters from high-throughput sequencing reads.,3,no,no | |
| cwltool,single_cell,Common Workflow Language reference implementation.,6,no,no | |
| cytoscape,single_cell,MCP wrapper for cytoscape.,1,no,no | |
| cytotrace2-python,single_cell,MCP wrapper for cytotrace2 python.,1,no,no | |
| cyvcf2,single_cell,A cython wrapper around htslib built for fast parsing of Variant Call Format (VCF) files.,1,no,no | |
| dca,single_cell,Count autoencoder for scRNA-seq denoising.,1,no,no | |
| ddocent,genomics,"dDocent is an interactive bash wrapper to QC, assemble, map, and call SNPs from all types of RAD data.",1,no,no | |
| deblur,single_cell,Deblur is a greedy deconvolution algorithm based on known read error profiles.,2,no,no | |
| decoupler,pathway_enrichment,MCP wrapper for decoupler.,7,no,no | |
| deeptools,single_cell,A set of user-friendly tools for normalization and visualzation of deep-sequencing data.,1,no,no | |
| deeptoolsintervals,single_cell,A python module creating/accessing GTF-based interval trees with associated meta-data.,1,no,no | |
| delly,single_cell,MCP wrapper for delly.,3,no,no | |
| dendropy,single_cell,MCP wrapper for dendropy.,5,no,no | |
| diamond,single_cell,MCP wrapper for diamond.,17,no,yes | |
| diamond copy,general,Auto-indexed MCP server for diamond copy.,1,no,no | |
| disease-gene-qa,single_cell,MCP wrapper for disease gene qa.,3,yes,no | |
| dnaio,single_cell,Read and write FASTA and FASTQ files efficiently.,5,no,no | |
| doubletdetection,single_cell,Method to detect and enable removal of doublets from single-cell RNA-sequencing.,3,no,no | |
| dropkick,single_cell,Automated scRNA-seq filtering.,2,no,no | |
| dsh-bio,single_cell,MCP wrapper for dsh bio.,2,no,no | |
| dxpy,utility,"DNAnexus Platform API bindings for Python."".",12,no,no | |
| easy_vitessce,single_cell,A package to easily use Vitessce to create interactive plots for single-cell data.,4,no,no | |
| echidna,single_cell,Mapping genotype to phenotype through joint probabilistic modeling of single-cell gene expression and chromosomal copy number variation.,2,no,no | |
| emboss,single_cell,The European Molecular Biology Open Software Suite.,13,no,no | |
| ena-webin-cli,single_cell,MCP wrapper for ena webin cli.,2,no,no | |
| ensembl-vep,utility,"Ensembl Variant Effect Predictor; The VEP determines the effect of your variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions.",3,yes,no | |
| entrez-direct,single_cell,"Entrez Direct (EDirect) - Access to NCBI's Entrez databases; Entrez Direct (EDirect) provides access to Entrez, the NCBI's suite of interconnected databases (publication, sequence, structure, gene, variation, expression, etc.) from a Unix terminal window. Search terms are entered as command-line arguments. Individual operations are connected with Unix pipes to construct multi-step queries. Selected records can then be retrieved in a variety of formats.",10,no,no | |
| epic,single_cell,Chip-Seq broad peak/domain finder.,3,no,no | |
| episcanpy,single_cell,Epigenomics Single-Cell Analysis in Python.,1,no,no | |
| eva-sub-cli,single_cell,EVA Submission Command Line Interface.,6,no,no | |
| f5c,single_cell,MCP wrapper for f5c.,6,no,no | |
| famsa,single_cell,MCP wrapper for famsa.,1,no,no | |
| fast5,single_cell,A C++ header-only library for reading Oxford Nanopore Fast5 files.,2,no,no | |
| fastani,single_cell,FastANI is developed for fast alignment-free computation of whole-genome Average Nucleotide Identity (ANI).,1,no,no | |
| fastdtw,single_cell,MCP wrapper for fastdtw.,2,no,no | |
| fastp,single_cell,MCP wrapper for fastp.,1,no,no | |
| fastqc,transcriptomics,Auto-indexed MCP server for fastqc.,1,no,yes | |
| fasttree,single_cell,MCP wrapper for fasttree.,1,no,no | |
| fermi2,transcriptomics,MCP wrapper for fermi2.,6,no,no | |
| fgbio,single_cell,MCP wrapper for fgbio.,11,no,no | |
| filechunkio,single_cell,FileChunkIO represents a chunk of an OS-level file containing bytes data.,1,no,no | |
| flagx,single_cell,FLAG-X: FLow cytometry Automated Gating toolboX.,2,no,no | |
| flye,single_cell,MCP wrapper for flye.,1,no,no | |
| foldseek,single_cell,Auto-indexed MCP server for foldseek.,1,no,no | |
| freebayes,genomics,MCP wrapper for freebayes.,1,no,yes | |
| ftputil,single_cell,High-level FTP client library (virtual file system and more).,7,no,no | |
| fwdpy11,single_cell,MCP wrapper for fwdpy11.,3,no,no | |
| galaxy-lib,single_cell,Subset of Galaxy (http: core code base designed to be used a library.,5,no,no | |
| gatk,genomics,MCP wrapper for gatk.,15,no,yes | |
| gatk4,genomics,MCP wrapper for gatk4.,10,no,yes | |
| gatk4-spark,single_cell,MCP wrapper for gatk4 spark.,5,no,no | |
| gdk-pixbuf,single_cell,MCP wrapper for gdk pixbuf.,4,no,no | |
| gecode,single_cell,Generic constraint development environment.,2,no,no | |
| gemini,general,a lightweight db framework for disease and population genetics.,1,yes,no | |
| gene-trajectory-python,single_cell,Compute gene trajectories; Gene Trajectory is a Python package that computes and analyzes gene trajectories in single-cell data.,1,no,no | |
| genecircuitry,pathway_enrichment,"GeneCircuitry: TRN analysis from single-cell data (Scanpy, CellOracle, Hotspot); A modular, checkpoint-enabled pipeline for TRN analysis from.",2,no,no | |
| geneimpacts,genomics,"prioritize effects of variant annotations from VEP, SnpEff, et al.",2,yes,no | |
| genenotebook,general,Auto-indexed MCP server for genenotebook.,1,no,no | |
| genoboo,genomics,A collaborative notebook for comparative genomics (active fork of GeneNoteBook).,3,no,no | |
| genomad,general,Identification of mobile genetic elements.,1,no,no | |
| genomepy,single_cell,Install and use genomes & gene annotations the easy way!,11,no,no | |
| genometools-genometools,genomics,GenomeTools genome analysis system.,9,no,no | |
| gffread,transcriptomics,MCP wrapper for gffread.,1,no,no | |
| gffutils,genomics,Work with GFF and GTF files in a flexible database framework.,11,yes,yes | |
| ghostscript,single_cell,An interpreter for the PostScript language and for PDF.,7,no,no | |
| gimmemotifs,genomics,Motif prediction pipeline and various motif-related tools.,11,yes,no | |
| glimmerhmm,single_cell,MCP wrapper for glimmerhmm.,1,no,no | |
| gmap,genomics,MCP wrapper for gmap.,7,no,no | |
| gneiss,single_cell,Compositional data analysis tools and visualizations.,6,no,no | |
| gnuplot,single_cell,MCP wrapper for gnuplot.,1,no,no | |
| gofasta,genomics,MCP wrapper for gofasta.,8,yes,no | |
| goleft,single_cell,MCP wrapper for goleft.,6,no,no | |
| gridss,single_cell,Auto-indexed MCP server for gridss.,1,no,no | |
| gromacs,single_cell,GROMACS is a versatile package to perform molecular dynamics.,9,no,no | |
| gseapy,pathway_enrichment,Gene Set Enrichment Analysis in Python.,5,yes,no | |
| gsmap,single_cell,MCP wrapper for gsmap.,9,no,no | |
| gtdbtk,single_cell,MCP wrapper for gtdbtk.,8,no,no | |
| harpy,genomics,"Process raw haplotagging data, from raw sequences to phased haplotypes; Harpy is a command-line tool to easily process platform-agnostic linked-read or WGS data. It uses.",17,no,no | |
| hhsuite,transcriptomics,MCP wrapper for hhsuite.,2,no,no | |
| hicexplorer,single_cell,"Set of programs to process, analyze and visualize Hi-C and capture Hi-C data.",9,no,no | |
| hifiasm,single_cell,MCP wrapper for hifiasm.,1,no,no | |
| hisat2,transcriptomics,MCP wrapper for hisat2.,7,no,yes | |
| hmmer,single_cell,Biosequence analysis using profile hidden Markov models.,11,no,no | |
| htseq,transcriptomics,HTSeq is a Python library to facilitate processing and analysis of data from high-throughput sequencing (HTS) experiments.,1,no,yes | |
| htslib,genomics,C library for high-throughput sequencing data formats.,4,no,no | |
| humann,genomics,"HUMAnN: The HMP Unified Metabolic Analysis Network, version 3.",1,no,no | |
| humann2,genomics,HUMAnN2: The HMP Unified Metabolic Analysis Network 2.,10,no,no | |
| hyphy,single_cell,Auto-indexed MCP server for hyphy.,1,no,no | |
| igv,single_cell,MCP wrapper for igv.,5,no,no | |
| igv-reports,genomics,Creates self-contained html pages for visual variant review with IGV (igv.js).,4,no,no | |
| illumina-interop,single_cell,"The Illumina InterOp libraries are a set of common routines used for reading and writing InterOp metric files. These metric files are binary files produced during a run providing detailed statistics about a run. In a few cases, the metric files are produced after a run during secondary analysis (index metrics) or for faster display of a subset of the original data (collapsed quality scores).",7,no,no | |
| infernal,single_cell,"Infernal is for searching DNA sequence databases for RNA structure and sequence similarities; Infernal (""INFERence of RNA ALignment"") is for searching DNA sequence databases for RNA structure and sequence similarities.",11,no,no | |
| insilicoseq,genomics,A sequencing simulator.,2,no,no | |
| intarna,single_cell,Efficient RNA-RNA interaction prediction incorporating seeding and accessibility of interacting sites.,1,no,no | |
| involucro,single_cell,MCP wrapper for involucro.,3,no,no | |
| iow,single_cell,Implementation of Balanced Parentheses; An implementation of the balanced parentheses tree structure as described by.,2,no,no | |
| ipyrad,genomics,Interactive assembly and analysis of RAD-seq data sets.,3,no,no | |
| ipython-cluster-helper,transcriptomics,Tool to easily start up an IPython cluster on different schedulers.,7,no,no | |
| iqtree,transcriptomics,MCP wrapper for iqtree.,1,no,no | |
| itsxpress,single_cell,ITSxpress: Software to rapidly trim the Internally Transcribed Spacer (ITS) region from FASTQ files.,1,no,no | |
| ivar,single_cell,MCP wrapper for ivar.,7,no,no | |
| jalview,single_cell,MCP wrapper for jalview.,5,no,no | |
| java-jdk,single_cell,MCP wrapper for java jdk.,3,no,no | |
| jbrowse2,genomics,The JBrowse 2 Genome Browser.,12,no,no | |
| jcvi,genomics,"Python utility libraries on genome assembly, annotation, and comparative genomics; JCVI utility libraries.",30,no,no | |
| jellyfish,single_cell,MCP wrapper for jellyfish.,11,no,no | |
| jq,utility,MCP wrapper for jq.,1,no,no | |
| k8,single_cell,MCP wrapper for k8.,3,no,no | |
| kaiju,transcriptomics,MCP wrapper for kaiju.,11,no,no | |
| kalign2,single_cell,Kalign is a fast and accurate multiple sequence alignment algorithm designed to align large numbers of protein sequences.,1,no,no | |
| kallisto,transcriptomics,MCP wrapper for kallisto.,10,no,yes | |
| kb-python,transcriptomics,A wrapper for the kallisto; bustools workflow for single-cell RNA-seq pre-processing.,6,no,no | |
| khipu-metabolomics,single_cell,"Python library for generalized, low-level annotation of MS metabolomics.",1,no,no | |
| kma,single_cell,Auto-indexed MCP server for kma.,1,no,no | |
| kmc,single_cell,MCP wrapper for kmc.,2,no,no | |
| kmer-jellyfish,single_cell,"Jellyfish is a tool for fast, memory-efficient counting of k-mers in DNA. A k-mer is a substring of length k, and counting the occurrences of all such substrings is a central step in many analyses of DNA sequence.",8,no,no | |
| kraken2,metagenomics,MCP wrapper for kraken2.,3,no,yes | |
| krona,single_cell,Krona Tools is a set of scripts to create Krona charts from several Bioinformatics tools as well as from text and XML files.,6,no,no | |
| last,single_cell,MCP wrapper for last.,1,no,no | |
| lastz,transcriptomics,MCP wrapper for lastz.,2,no,no | |
| liana,transcriptomics,LIANA+: a one-stop-shop framework for cell-cell communication.,1,no,no | |
| libcifpp,single_cell,"Library containing code to manipulate mmCIF and PDB files; This library, libcifpp, is a generic CIF library with some specific additions to work with mmCIF files.",5,no,no | |
| libdb,general,The Berkeley DB embedded database system.,1,no,no | |
| libdeflate,single_cell,"libdeflate is a library for fast, whole-buffer DEFLATE-based compression and decompression.",4,no,no | |
| libsequence,single_cell,A C++ class library for evolutionary genetics.,1,yes,no | |
| local-bio-cache,genomics,"Offline local bio cache MCP server for registering FASTA collections, optionally building local BLAST databases, running local BLAST-like sequence search with Python fallback, running pairwise sequence alignment, and querying cached TF binding tables from GTRD, ENCODE, or ChIP-Atlas; Use this server when remote BLAST, UniProt sequence search, or TF binding APIs are unstable. It provides local sequence grounding, local UniProt-like search over predownloaded FASTA files, and offline TF binding lookup over curated flat files.",11,no,no | |
| locarna,single_cell,MCP wrapper for locarna.,6,no,no | |
| macs2,single_cell,Model Based Analysis for ChIP-Seq data.,12,no,no | |
| mafft,single_cell,MCP wrapper for mafft.,3,no,yes | |
| mageck,pathway_enrichment,MCP wrapper for mageck.,6,no,no | |
| mameshiba,single_cell,mameshiba installs only the dependencies needed to run MameShiba; mameshiba is a minimal conda meta-package that installs all dependencies required.,3,no,no | |
| mappy,single_cell,MCP wrapper for mappy.,2,no,no | |
| markerrepo,single_cell,A tool for marker list management and annotation in the single cell context.,4,no,no | |
| mash,single_cell,MCP wrapper for mash.,8,no,no | |
| maxquant,single_cell,MCP wrapper for maxquant.,3,no,no | |
| mcl,single_cell,Auto-indexed MCP server for mcl.,1,no,no | |
| medaka,genomics,A tool to create consensus sequences and variant calls from nanopore sequencing data using neural networks.,7,no,no | |
| megahit,single_cell,MCP wrapper for megahit.,2,no,no | |
| meme,single_cell,Motif-based sequence analysis tools.,2,no,no | |
| mentalist,single_cell,The MLST pipeline developed by the PathOGiST research group.,4,no,no | |
| metagenome-atlas,transcriptomics,ATLAS - Three commands to start analysing your metagenome data; Atlas is a easy to use metagenomic pipeline.,2,no,no | |
| metaphlan,metagenomics,Metagenomic Phylogenetic Analysis; MetaPhlAn is a computational tool for profiling the composition of microbial.,2,no,yes | |
| metaphlan2,metagenomics,Metagenomic Phylogenetic Analysis; MetaPhlAn is a computational tool for profiling the composition of microbial.,4,no,yes | |
| mikado,genomics,A Python3 annotation program to select the best gene model in each locus.,8,no,no | |
| minced,single_cell,MCP wrapper for minced.,1,no,no | |
| miniasm,single_cell,MCP wrapper for miniasm.,2,no,no | |
| minimap2,single_cell,MCP wrapper for minimap2.,3,no,yes | |
| minvar,genomics,A tool to detect minority variants in HIV-1 and HCV populations.,1,no,no | |
| mitos,single_cell,MITOS is a tool for the annotation of metazoan mitochondrial genomes.,4,no,no | |
| mlst,transcriptomics,Auto-indexed MCP server for mlst.,1,no,no | |
| mmtf-python,single_cell,A decoding libary for the PDB mmtf format.,2,no,no | |
| mobivision-m,transcriptomics,MobiVision-M is a linux based software design specifically for single-microbe RNA sequencing analysis.,3,no,no | |
| moments,single_cell,Evolutionary inference using SFS and LD statistics.,6,no,no | |
| mosdepth,single_cell,MCP wrapper for mosdepth.,1,no,no | |
| mothur,single_cell,MCP wrapper for mothur.,5,no,no | |
| msisensor-pro,genomics,MCP wrapper for msisensor pro.,2,no,no | |
| msproteomicstools,single_cell,msproteomicstools is a Python library that can be used in LC-MS/MS based proteomics. It features a core library called.,1,no,no | |
| msstitch,single_cell,MS proteomics post processing utilities.,22,no,no | |
| multiqc,transcriptomics,Aggregate results from bioinformatics analyses across many samples into a single report.,9,no,yes | |
| multiqc-bcbio,single_cell,MultiQC plugin for bcbio report visualization.,4,no,no | |
| multiqc-xenium-extra,single_cell,MultiQC plugin for extra Xenium spatial transcriptomics analysis.,1,no,no | |
| multivelo,utility,MCP wrapper for multivelo.,1,no,no | |
| mummer,single_cell,MCP wrapper for mummer.,1,no,no | |
| munkres,single_cell,MCP wrapper for munkres.,1,no,no | |
| muscle,single_cell,MCP wrapper for muscle.,2,no,no | |
| mysql-connector-c,single_cell,"MySQL Connector/C, the C interface for communicating with MySQL servers.",1,no,no | |
| mztosqlite,single_cell,MCP wrapper for mztosqlite.,1,no,no | |
| nanocomp,single_cell,Comparing runs of Oxford Nanopore sequencing data and alignments.,2,no,no | |
| nanoget,single_cell,Functions to extract information from Oxford Nanopore sequencing data and alignments.,4,no,no | |
| nanomath,single_cell,A few simple math function for other Oxford Nanopore processing scripts.,3,no,no | |
| nanoplot,single_cell,Plotting suite for long read sequencing data and alignments.,1,no,no | |
| nanopolish,genomics,Signal-level algorithms for MinION data.,6,no,no | |
| ncbi-amrfinderplus,general,AMRFinderPlus finds antimicrobial resistance and other genes in protein or nucleotide sequences; This software and the accompanying database are designed to.,1,yes,no | |
| ncbi-datasets-pylib,genomics,Easily gather data from across NCBI databases.,12,yes,no | |
| ncbi-genome-download,single_cell,Download genome files from the NCBI FTP server.,2,no,no | |
| ncbi-ngs-sdk,single_cell,"NGS is a new, domain-specific API for accessing reads, alignments and pileups produced from Next Generation Sequencing.",1,no,no | |
| ncbi-vdb,single_cell,"SRA tools database engine; ""VDB is the database engine that all SRA tools use. It is a columnar database.",1,no,no | |
| ncls,single_cell,"A fast interval tree-like implementation in C, wrapped for the Python ecosystem. Basically a static interval-tree that is silly fast for both construction and lookups.",4,no,no | |
| nextalign,single_cell,MCP wrapper for nextalign.,3,no,no | |
| nextclade,single_cell,MCP wrapper for nextclade.,7,no,no | |
| nextflow,single_cell,MCP wrapper for nextflow.,9,no,no | |
| nf-core,single_cell,Python package with helper tools for the nf-core community.,15,no,no | |
| nglview,single_cell,An IPython widget to interactively view molecular structures and trajectories. Utilizes the embeddable NGL Viewer for rendering.,6,no,no | |
| ngmlr,genomics,MCP wrapper for ngmlr.,1,no,no | |
| ngs-smap,genomics,SMAP is a software package that analyzes next-generation DNA sequencing read mapping distributions and performs haplotype calling to create multi-allelic molecular markers.,1,no,no | |
| ngs-tools,single_cell,Reusable tools for working with next-generation sequencing (NGS) data.,1,no,no | |
| novae,single_cell,Graph-based foundation model for spatial transcriptomics data.,4,no,no | |
| novoalign,single_cell,MCP wrapper for novoalign.,2,no,no | |
| ont-fast5-api,single_cell,Oxford Nanopore Technologies fast5 API software.,5,no,no | |
| openms-thirdparty,single_cell,A helper package to install OpenMS TOPP tools with all their compatible and conda-available versions of adapted thirdparty tools.,1,no,no | |
| opticlust,single_cell,Single cell clustering and recommendations at a glance.,5,no,no | |
| orthofinder,single_cell,MCP wrapper for orthofinder.,1,no,yes | |
| ourotools,single_cell,"A comprehensive toolkit for quality control and analysis of single-cell long-read RNA-seq data; Ouro-Tools is a novel, comprehensive computational pipeline for long-read scRNA-seq with the following key features. Ouro-Tools (1) normalizes mRNA size distributions and (2) detects mRNA 7-methylguanosine caps to integrate multiple single-cell long-read RNA-sequencing experiments across modalities and characterize full-length transcripts, respectively.",1,no,no | |
| pairix,transcriptomics,MCP wrapper for pairix.,5,no,no | |
| palantir,single_cell,Palantir for modeling continuous cell state and cell fate choices in single cell data.,11,no,no | |
| paml,single_cell,A package of programs for phylogenetic analyses of DNA or protein sequences using maximum likelihood.,5,no,no | |
| pandaseq,general,Auto-indexed MCP server for pandaseq.,1,no,no | |
| pango-designation,single_cell,MCP wrapper for pango designation.,1,no,no | |
| pangolearn,single_cell,Store of the trained model for pangolin to access.,2,no,no | |
| pangolin,single_cell,Phylogenetic Assignment of Named Global Outbreak LINeages.,1,no,no | |
| parasail-python,single_cell,Python bindings for the parasail C library containing implementations of pairwise sequence alignment algorithms.,1,no,no | |
| pasta,single_cell,MCP wrapper for pasta.,1,no,no | |
| paste-bio,single_cell,A computational method to align and integrate spatial transcriptomics experiments.,2,no,no | |
| pcdl,single_cell,"physicell data loader (pcdl) provides a platform independent, python3 based, pip installable interface to transform output, generated with the PhysiCell agent based modeling framework, into standard formats.",18,no,no | |
| peakqc,single_cell,Quality control of single cell ATAC-seq data based on fragment length distributions.,1,no,no | |
| peptide-shaker,general,MCP wrapper for peptide shaker.,4,no,no | |
| perl-aceperl,transcriptomics,MCP wrapper for perl aceperl.,1,no,no | |
| perl-algorithm-diff,transcriptomics,MCP wrapper for perl algorithm diff.,4,no,no | |
| perl-algorithm-munkres,transcriptomics,Auto-indexed MCP server for perl-algorithm-munkres.,1,no,no | |
| perl-alien-build,general,Build external dependencies for use in CPAN.,1,no,no | |
| perl-alien-libxml2,single_cell,Installs the C libxml2 library on your system.,1,no,no | |
| perl-app-cpanminus,transcriptomics,MCP wrapper for perl app cpanminus.,1,no,no | |
| perl-appconfig,transcriptomics,MCP wrapper for perl appconfig.,1,no,no | |
| perl-archive-tar,transcriptomics,MCP wrapper for perl archive tar.,3,no,no | |
| perl-autoloader,transcriptomics,MCP wrapper for perl autoloader.,2,no,no | |
| perl-base,transcriptomics,MCP wrapper for perl base.,1,no,no | |
| perl-bio-asn1-entrezgene,transcriptomics,MCP wrapper for perl bio asn1 entrezgene.,1,no,no | |
| perl-bio-coordinate,single_cell,Methods for dealing with genomic coordinates.,3,no,no | |
| perl-bio-featureio,transcriptomics,MCP wrapper for perl bio featureio.,1,no,no | |
| perl-bio-phylo,transcriptomics,Auto-indexed MCP server for perl-bio-phylo.,1,no,no | |
| perl-bio-samtools,transcriptomics,MCP wrapper for perl bio samtools.,1,no,no | |
| perl-bio-searchio-hmmer,single_cell,"A parser for HMMER2 and HMMER3 output (hmmscan, hmmsearch, hmmpfam).",2,no,no | |
| perl-bio-tools-phylo-paml,single_cell,"Parses output from the PAML programs codeml, baseml, basemlg, codemlsites and yn00.",5,no,no | |
| perl-bio-tools-run-alignment-clustalw,single_cell,Object for the calculation of a multiple sequence alignment from a set of unaligned sequences or alignments using the Clustalw program.,1,no,no | |
| perl-bioperl,transcriptomics,Auto-indexed MCP server for perl-bioperl.,1,no,no | |
| perl-bioperl-core,transcriptomics,MCP wrapper for perl bioperl core.,3,no,no | |
| perl-bioperl-run,transcriptomics,Auto-indexed MCP server for perl-bioperl-run.,1,no,no | |
| perl-business-isbn,transcriptomics,MCP wrapper for perl business isbn.,6,no,no | |
| perl-business-isbn-data,single_cell,data pack for Business::ISBN.,7,no,no | |
| perl-capture-tiny,transcriptomics,MCP wrapper for perl capture tiny.,1,no,no | |
| perl-carp,transcriptomics,Auto-indexed MCP server for perl-carp.,1,no,no | |
| perl-cgi,transcriptomics,MCP wrapper for perl cgi.,1,no,no | |
| perl-class-inspector,transcriptomics,MCP wrapper for perl class inspector.,1,no,no | |
| perl-class-load,transcriptomics,Auto-indexed MCP server for perl-class-load.,1,no,no | |
| perl-class-load-xs,transcriptomics,Auto-indexed MCP server for perl-class-load-xs.,1,no,no | |
| perl-common-sense,transcriptomics,MCP wrapper for perl common sense.,1,no,no | |
| perl-compress-raw-zlib,transcriptomics,MCP wrapper for perl compress raw zlib.,4,no,no | |
| perl-config-general,transcriptomics,MCP wrapper for perl config general.,2,no,no | |
| perl-constant,transcriptomics,MCP wrapper for perl constant.,1,no,no | |
| perl-convert-binary-c,transcriptomics,MCP wrapper for perl convert binary c.,1,no,no | |
| perl-convert-binhex,transcriptomics,MCP wrapper for perl convert binhex.,1,no,no | |
| perl-cpan-meta,transcriptomics,Auto-indexed MCP server for perl-cpan-meta.,1,no,no | |
| perl-cpan-meta-requirements,single_cell,A set of version requirements for a CPAN dist.,4,no,no | |
| perl-crypt-rc4,transcriptomics,MCP wrapper for perl crypt rc4.,4,no,no | |
| perl-data-dumper,transcriptomics,MCP wrapper for perl data dumper.,1,no,no | |
| perl-data-optlist,transcriptomics,MCP wrapper for perl data optlist.,1,no,no | |
| perl-date-format,transcriptomics,MCP wrapper for perl date format.,1,no,no | |
| perl-dbi,transcriptomics,MCP wrapper for perl dbi.,3,no,no | |
| perl-devel-globaldestruction,transcriptomics,Auto-indexed MCP server for perl-devel-globaldestruction.,1,no,no | |
| perl-devel-overloadinfo,single_cell,introspect overloaded operators.,1,no,no | |
| perl-devel-stacktrace,transcriptomics,MCP wrapper for perl devel stacktrace.,4,no,no | |
| perl-digest-md5,transcriptomics,MCP wrapper for perl digest md5.,1,no,no | |
| perl-digest-perl-md5,transcriptomics,MCP wrapper for perl digest perl md5.,2,no,no | |
| perl-digest-sha1,single_cell,Perl interface to the SHA-1 algorithm.,3,no,no | |
| perl-dist-checkconflicts,transcriptomics,Auto-indexed MCP server for perl-dist-checkconflicts.,1,no,no | |
| perl-dynaloader,transcriptomics,Auto-indexed MCP server for perl-dynaloader.,1,no,no | |
| perl-encode,transcriptomics,MCP wrapper for perl encode.,1,no,no | |
| perl-encode-locale,transcriptomics,MCP wrapper for perl encode locale.,1,no,no | |
| perl-error,transcriptomics,MCP wrapper for perl error.,2,no,no | |
| perl-eval-closure,transcriptomics,Auto-indexed MCP server for perl-eval-closure.,1,no,no | |
| perl-exception-class,transcriptomics,MCP wrapper for perl exception class.,1,no,no | |
| perl-exporter,transcriptomics,MCP wrapper for perl exporter.,4,no,no | |
| perl-extutils-cbuilder,transcriptomics,MCP wrapper for perl extutils cbuilder.,5,no,no | |
| perl-extutils-makemaker,transcriptomics,MCP wrapper for perl extutils makemaker.,4,no,no | |
| perl-ffi-checklib,single_cell,Check that a library is available for FFI.,10,no,no | |
| perl-file-sort,transcriptomics,MCP wrapper for perl file sort.,2,no,no | |
| perl-file-spec,transcriptomics,MCP wrapper for perl file spec.,2,no,no | |
| perl-file-which,transcriptomics,MCP wrapper for perl file which.,2,no,no | |
| perl-font-afm,transcriptomics,MCP wrapper for perl font afm.,1,no,no | |
| perl-getopt-long,transcriptomics,MCP wrapper for perl getopt long.,6,no,no | |
| perl-graph,transcriptomics,MCP wrapper for perl graph.,2,no,no | |
| perl-graphviz,transcriptomics,MCP wrapper for perl graphviz.,1,no,no | |
| perl-html-element-extended,transcriptomics,MCP wrapper for perl html element extended.,1,no,no | |
| perl-html-formatter,transcriptomics,MCP wrapper for perl html formatter.,1,no,no | |
| perl-html-parser,transcriptomics,MCP wrapper for perl html parser.,2,no,no | |
| perl-html-tableextract,transcriptomics,MCP wrapper for perl html tableextract.,3,no,no | |
| perl-html-tagset,transcriptomics,MCP wrapper for perl html tagset.,2,no,no | |
| perl-html-tidy,transcriptomics,MCP wrapper for perl html tidy.,1,no,no | |
| perl-html-tree,transcriptomics,MCP wrapper for perl html tree.,1,no,no | |
| perl-html-treebuilder-xpath,transcriptomics,MCP wrapper for perl html treebuilder xpath.,1,no,no | |
| perl-http-cookies,transcriptomics,MCP wrapper for perl http cookies.,9,no,no | |
| perl-http-daemon,transcriptomics,MCP wrapper for perl http daemon.,1,no,no | |
| perl-http-date,transcriptomics,MCP wrapper for perl http date.,5,no,no | |
| perl-http-negotiate,transcriptomics,MCP wrapper for perl http negotiate.,2,no,no | |
| perl-image-info,transcriptomics,MCP wrapper for perl image info.,2,no,no | |
| perl-image-size,transcriptomics,MCP wrapper for perl image size.,1,no,no | |
| perl-importer,transcriptomics,MCP wrapper for perl importer.,5,no,no | |
| perl-io-compress,transcriptomics,MCP wrapper for perl io compress.,2,no,no | |
| perl-io-html,transcriptomics,MCP wrapper for perl io html.,4,no,no | |
| perl-io-sessiondata,transcriptomics,MCP wrapper for perl io sessiondata.,4,no,no | |
| perl-io-string,transcriptomics,MCP wrapper for perl io string.,4,no,no | |
| perl-io-stringy,transcriptomics,MCP wrapper for perl io stringy.,4,no,no | |
| perl-io-tty,transcriptomics,MCP wrapper for perl io tty.,1,no,no | |
| perl-io-zlib,transcriptomics,MCP wrapper for perl io zlib.,2,no,no | |
| perl-ipc-cmd,transcriptomics,MCP wrapper for perl ipc cmd.,1,no,no | |
| perl-ipc-run,transcriptomics,Auto-indexed MCP server for perl-ipc-run.,1,no,no | |
| perl-ipc-sharelite,single_cell,Lightweight interface to shared memory.,1,no,no | |
| perl-jcode,transcriptomics,MCP wrapper for perl jcode.,1,no,no | |
| perl-json,transcriptomics,MCP wrapper for perl json.,5,no,no | |
| perl-json-pp,single_cell,JSON::XS compatible pure-Perl module.,2,no,no | |
| perl-json-xs,transcriptomics,MCP wrapper for perl json xs.,4,no,no | |
| perl-lib,genomics,MCP wrapper for perl lib.,8,no,no | |
| perl-libwww-perl,transcriptomics,MCP wrapper for perl libwww perl.,9,no,no | |
| perl-libxml-perl,transcriptomics,MCP wrapper for perl libxml perl.,1,no,no | |
| perl-list-moreutils,transcriptomics,MCP wrapper for perl list moreutils.,10,no,no | |
| perl-list-moreutils-xs,transcriptomics,MCP wrapper for perl list moreutils xs.,1,no,no | |
| perl-locale-maketext-simple,transcriptomics,MCP wrapper for perl locale maketext simple.,1,no,no | |
| perl-lwp-mediatypes,transcriptomics,MCP wrapper for perl lwp mediatypes.,5,no,no | |
| perl-lwp-simple,transcriptomics,Auto-indexed MCP server for perl-lwp-simple.,1,no,no | |
| perl-mailtools,transcriptomics,MCP wrapper for perl mailtools.,1,no,no | |
| perl-math-bezier,transcriptomics,MCP wrapper for perl math bezier.,1,no,no | |
| perl-math-derivative,transcriptomics,MCP wrapper for perl math derivative.,3,no,no | |
| perl-math-random,transcriptomics,MCP wrapper for perl math random.,6,no,no | |
| perl-math-round,transcriptomics,MCP wrapper for perl math round.,1,no,no | |
| perl-math-spline,transcriptomics,MCP wrapper for perl math spline.,1,no,no | |
| perl-metabolomics-fragment-annotation,transcriptomics,MCP wrapper for perl metabolomics fragment annotation.,1,no,no | |
| perl-mime-base64,transcriptomics,Auto-indexed MCP server for perl-mime-base64.,1,no,no | |
| perl-mime-lite,transcriptomics,MCP wrapper for perl mime lite.,5,no,no | |
| perl-mime-types,transcriptomics,MCP wrapper for perl mime types.,1,no,no | |
| perl-module-build,transcriptomics,MCP wrapper for perl module build.,4,no,no | |
| perl-module-corelist,transcriptomics,MCP wrapper for perl module corelist.,8,no,no | |
| perl-module-load-conditional,transcriptomics,MCP wrapper for perl module load conditional.,3,no,no | |
| perl-module-metadata,transcriptomics,MCP wrapper for perl module metadata.,11,no,no | |
| perl-module-runtime,transcriptomics,MCP wrapper for perl module runtime.,1,no,no | |
| perl-module-runtime-conflicts,single_cell,Provide information on conflicts for Module::Runtime.,1,no,no | |
| perl-moo,transcriptomics,MCP wrapper for perl moo.,1,no,no | |
| perl-moose,transcriptomics,MCP wrapper for perl moose.,1,no,no | |
| perl-mozilla-ca,transcriptomics,Auto-indexed MCP server for perl-mozilla-ca.,1,no,no | |
| perl-net-http,transcriptomics,MCP wrapper for perl net http.,5,no,no | |
| perl-net-ssleay,transcriptomics,MCP wrapper for perl net ssleay.,2,no,no | |
| perl-number-format,transcriptomics,MCP wrapper for perl number format.,7,no,no | |
| perl-ole-storage_lite,transcriptomics,MCP wrapper for perl ole storage lite.,1,no,no | |
| perl-package-deprecationmanager,single_cell,Manage deprecation warnings for your distribution.,1,no,no | |
| perl-package-stash,transcriptomics,MCP wrapper for perl package stash.,3,no,no | |
| perl-params-check,transcriptomics,MCP wrapper for perl params check.,1,no,no | |
| perl-params-util,transcriptomics,MCP wrapper for perl params util.,1,no,no | |
| perl-params-validate,transcriptomics,MCP wrapper for perl params validate.,1,no,no | |
| perl-parent,transcriptomics,MCP wrapper for perl parent.,7,no,no | |
| perl-parse-recdescent,transcriptomics,MCP wrapper for perl parse recdescent.,4,no,no | |
| perl-pod-escapes,transcriptomics,MCP wrapper for perl pod escapes.,1,no,no | |
| perl-pod-usage,transcriptomics,MCP wrapper for perl pod usage.,2,no,no | |
| perl-postscript,transcriptomics,MCP wrapper for perl postscript.,4,no,no | |
| perl-regexp-common,transcriptomics,MCP wrapper for perl regexp common.,5,no,no | |
| perl-role-tiny,transcriptomics,MCP wrapper for perl role tiny.,4,no,no | |
| perl-sereal,transcriptomics,MCP wrapper for perl sereal.,1,no,no | |
| perl-sereal-decoder,transcriptomics,MCP wrapper for perl sereal decoder.,3,no,no | |
| perl-set-intervaltree,transcriptomics,MCP wrapper for perl set intervaltree.,8,no,no | |
| perl-set-intspan,transcriptomics,Auto-indexed MCP server for perl-set-intspan.,1,no,no | |
| perl-set-scalar,transcriptomics,Auto-indexed MCP server for perl-set-scalar.,1,no,no | |
| perl-soap-lite,transcriptomics,MCP wrapper for perl soap lite.,4,no,no | |
| perl-sort-naturally,transcriptomics,MCP wrapper for perl sort naturally.,2,no,no | |
| perl-spreadsheet-parseexcel,utility,MCP wrapper for perl spreadsheet parseexcel.,4,no,no | |
| perl-spreadsheet-writeexcel,transcriptomics,MCP wrapper for perl spreadsheet writeexcel.,2,no,no | |
| perl-sub-exporter,transcriptomics,MCP wrapper for perl sub exporter.,5,no,no | |
| perl-sub-exporter-progressive,transcriptomics,MCP wrapper for perl sub exporter progressive.,5,no,no | |
| perl-sub-identify,transcriptomics,MCP wrapper for perl sub identify.,1,no,no | |
| perl-sub-name,single_cell,MCP wrapper for perl sub name.,1,no,no | |
| perl-sub-quote,single_cell,Efficient generation of subroutines via string eval.,8,no,no | |
| perl-sub-uplevel,transcriptomics,MCP wrapper for perl sub uplevel.,2,no,no | |
| perl-svg,transcriptomics,Auto-indexed MCP server for perl-svg.,1,no,no | |
| perl-task-weaken,transcriptomics,MCP wrapper for perl task weaken.,6,no,no | |
| perl-template-toolkit,transcriptomics,MCP wrapper for perl template toolkit.,3,no,no | |
| perl-test,transcriptomics,MCP wrapper for perl test.,2,no,no | |
| perl-test-deep,transcriptomics,MCP wrapper for perl test deep.,3,no,no | |
| perl-test-differences,transcriptomics,MCP wrapper for perl test differences.,4,no,no | |
| perl-test-exception,transcriptomics,MCP wrapper for perl test exception.,4,no,no | |
| perl-test-fatal,transcriptomics,MCP wrapper for perl test fatal.,1,no,no | |
| perl-test-harness,transcriptomics,MCP wrapper for perl test harness.,1,no,no | |
| perl-test-most,transcriptomics,Auto-indexed MCP server for perl-test-most.,1,no,no | |
| perl-test-warn,transcriptomics,MCP wrapper for perl test warn.,3,no,no | |
| perl-text-diff,transcriptomics,Auto-indexed MCP server for perl-text-diff.,1,no,no | |
| perl-text-parsewords,transcriptomics,Auto-indexed MCP server for perl-text-parsewords.,1,no,no | |
| perl-tie-ixhash,transcriptomics,MCP wrapper for perl tie ixhash.,5,no,no | |
| perl-time-hires,transcriptomics,MCP wrapper for perl time hires.,15,no,no | |
| perl-timedate,transcriptomics,Auto-indexed MCP server for perl-timedate.,1,no,no | |
| perl-tree-dag_node,transcriptomics,MCP wrapper for perl tree dag node.,6,no,no | |
| perl-try-tiny,transcriptomics,MCP wrapper for perl try tiny.,6,no,no | |
| perl-types-serialiser,transcriptomics,Auto-indexed MCP server for perl-types-serialiser.,1,no,no | |
| perl-unicode-map,transcriptomics,MCP wrapper for perl unicode map.,1,no,no | |
| perl-uri,transcriptomics,MCP wrapper for perl uri.,9,no,no | |
| perl-url-encode,single_cell,Encoding and decoding of application/x-www-form-urlencoded encoding.,6,no,no | |
| perl-version,single_cell,Structured version objects.,7,no,no | |
| perl-www-robotrules,transcriptomics,MCP wrapper for perl www robotrules.,4,no,no | |
| perl-xml-dom-xpath,transcriptomics,Auto-indexed MCP server for perl-xml-dom-xpath.,1,no,no | |
| perl-xml-filter-buffertext,transcriptomics,MCP wrapper for perl xml filter buffertext.,1,no,no | |
| perl-xml-libxml,transcriptomics,MCP wrapper for perl xml libxml.,5,no,no | |
| perl-xml-libxslt,transcriptomics,MCP wrapper for perl xml libxslt.,1,no,no | |
| perl-xml-namespacesupport,single_cell,MCP wrapper for perl xml namespacesupport.,1,no,no | |
| perl-xml-parser,transcriptomics,MCP wrapper for perl xml parser.,1,no,no | |
| perl-xml-regexp,transcriptomics,MCP wrapper for perl xml regexp.,4,no,no | |
| perl-xml-sax,transcriptomics,MCP wrapper for perl xml sax.,6,no,no | |
| perl-xml-sax-base,transcriptomics,MCP wrapper for perl xml sax base.,3,no,no | |
| perl-xml-sax-expat,transcriptomics,MCP wrapper for perl xml sax expat.,1,no,no | |
| perl-xml-twig,transcriptomics,MCP wrapper for perl xml twig.,5,no,no | |
| perl-xml-xpath,transcriptomics,MCP wrapper for perl xml xpath.,5,no,no | |
| perl-xml-xpathengine,transcriptomics,MCP wrapper for perl xml xpathengine.,1,no,no | |
| perl-xsloader,transcriptomics,MCP wrapper for perl xsloader.,4,no,no | |
| perl-yaml,transcriptomics,MCP wrapper for perl yaml.,9,no,no | |
| phyml,single_cell,MCP wrapper for phyml.,1,no,no | |
| piaso,single_cell,PIASO: Precise Integrative Analysis of Single-cell Omics; PIASO is a Python toolkit for precise integrative analysis of single-cell omics data.,2,no,no | |
| picard,single_cell,MCP wrapper for picard.,10,no,no | |
| picard copy,general,Auto-indexed MCP server for picard copy.,1,no,no | |
| picard-slim,general,Auto-indexed MCP server for picard-slim.,1,no,no | |
| pilon,general,Auto-indexed MCP server for pilon.,1,no,no | |
| pixelator,single_cell,A command-line tool and library to process and analyze sequencing data from Molecular Pixelation (MPX) assays.,10,no,no | |
| planemo,general,Command-line utilities to assist in building tools for the Galaxy project (https://galaxyproject.org).,1,no,no | |
| plink,single_cell,Auto-indexed MCP server for plink.,1,no,no | |
| poa,general,Auto-indexed MCP server for poa.,1,no,no | |
| portcullis,single_cell,Splice junction analysis and filtering from BAM files.,9,no,no | |
| pplacer,single_cell,Auto-indexed MCP server for pplacer.,1,no,no | |
| prank,single_cell,MCP wrapper for prank.,4,no,no | |
| primer3,single_cell,"Design PCR primers from DNA sequence. From mispriming libraries to sequence quality data to the generation of internal oligos, primer3 does it.",5,no,no | |
| primer3-py,general,Python bindings for Primer3.,1,yes,no | |
| prodigal,transcriptomics,MCP wrapper for prodigal.,2,no,no | |
| prokka,transcriptomics,MCP wrapper for prokka.,7,no,yes | |
| prophyle,single_cell,"ProPhyle is an accurate, resource-frugal and deterministic phylogeny-based metagenomic classifier.",8,no,no | |
| proteinortho,transcriptomics,MCP wrapper for proteinortho.,5,no,no | |
| proteomiqon-peptidedb,single_cell,MCP wrapper for proteomiqon peptidedb.,1,no,no | |
| proteomiqon-peptidespectrummatching,single_cell,MCP wrapper for proteomiqon peptidespectrummatching.,1,no,no | |
| pubchempy,single_cell,MCP wrapper for pubchempy.,6,no,no | |
| py2bit,single_cell,MCP wrapper for py2bit.,5,no,no | |
| pybedtools,genomics,Wraps BEDTools for use in Python and adds many additional features.,20,yes,yes | |
| pybigwig,single_cell,A python extension written in C for quick access to bigWig files.,7,no,no | |
| pybiolib,general,BioLib Python Client.,1,no,no | |
| pycistopic,single_cell,MCP wrapper for pycistopic.,6,no,no | |
| pyfaidx,general,pyfaidx: efficient pythonic random access to fasta subsequences.,1,yes,no | |
| pyfastx,single_cell,MCP wrapper for pyfastx.,7,no,no | |
| pyhmmer,single_cell,Cython bindings and Python interface to HMMER3.,6,no,no | |
| pyranges,transcriptomics,Performant Pythonic GenomicRanges.,7,no,no | |
| pyrle,general,Genomic Rle-objects for Python.,1,no,no | |
| pyrodigal,transcriptomics,MCP wrapper for pyrodigal.,1,no,no | |
| pyroe,general,MCP wrapper for pyroe.,5,no,no | |
| pyrovelocity,single_cell,Probabilistic RNA velocity for cell fate uncertainty estimation.,1,no,no | |
| pysam,genomics,"Pysam is a Python module for reading and manipulating SAM files. It's a lightweight wrapper of the htslib C-API, the same one that powers samtools, bcftools, and tabix.",11,no,no | |
| pysftp,single_cell,A friendly face on SFTP.,6,no,no | |
| pyspoa,single_cell,Python binding to spoa library.,1,no,no | |
| pytabix,single_cell,Fast random access to sorted files compressed with bgzip and indexed by tabix.,3,no,no | |
| pyteomics,single_cell,A framework for proteomics data analysis.,8,no,no | |
| python-edlib,single_cell,"Lightweight, super fast C/C++ (& Python) library for sequence alignment using edit (Levenshtein) distance.",1,no,no | |
| pyvcf,genomics,A Variant Call Format reader for Python.,1,no,no | |
| pyvcf3,genomics,A Variant Call Format reader for Python.,4,yes,no | |
| qcatch,single_cell,QCatch: Quality Control downstream of alevin-fry / simpleaf.,2,no,no | |
| qiime,general,Quantitative Insights Into Microbial Ecology.,1,no,no | |
| qualimap,transcriptomics,MCP wrapper for qualimap.,6,no,no | |
| quast,genomics,Quality Assessment Tool for Genome Assemblies.,1,no,no | |
| r-abdiv,single_cell,Alpha and Beta Diversity Measures; 'A collection of measures for measuring ecological diversity.,10,no,no | |
| r-acidbase,single_cell,Low-level base functions imported by Acid Genomics packages.,11,no,no | |
| r-archr,transcriptomics,This package is designed to streamline scATAC analyses in R.,8,no,no | |
| r-azimuth,single_cell,Azimuth is a Shiny app demonstrating a query-reference mapping algorithm for single-cell data.,1,no,no | |
| r-basejump,single_cell,Base functions for bioinformatics and R package development.,1,no,no | |
| r-beyondcell,single_cell,"Tool for the Analysis of tumour therapeutic heterogeneity in single-cell RNA-seq; Beyondcell is a methodology for the identification of drug vulnerabilities in single-cell RNA-seq (scRNA-seq) data. To this end, Beyondcell focuses on the analysis of drug-related commonalities between cells by classifying them into distinct Therapeutic Clusters (TCs).",7,no,no | |
| r-cdseq,single_cell,Estimate cell-type-specific gene expression profiles and sample-specific cell-type proportions simultaneously using bulk sequencing data. Kang et al. (2019) <doi:10.1371/journal.pcbi.1007510>.,3,no,no | |
| r-dwls,single_cell,Deconvolution of bulk mRNA data using single-cell RNAseq to provide cell type specific signatures.,6,no,no | |
| r-epitrace,single_cell,Inference of cell age and phylogeny from single cell ATAC data.,6,no,no | |
| r-flanders,transcriptomics,Fast colocalization using AnnData objects in R; flanders is an R package designed to seamlessly convert finemapping output files from the nf-flanders pipeline.,3,no,no | |
| r-goalie,single_cell,Assertive check functions for defensive R programming.,11,no,no | |
| r-mams,single_cell,R package for Matrix and Analysis Metadata Standards.,1,no,no | |
| r-redeemr,single_cell,"R package for Regulatory multi-omics with Deep Mitochondrial mutation profiling; Introduce a new approach for single-cell Regulatory multi-omics (transcriptomics and chromatin accessibility) with Deep Mitochondrial mutation profiling (~10-fold increase in detection rate), or ReDeeM. redeemR is the R package that facilitates mutation refining, lineage tracing, as well multiomics integration analysis.",1,no,no | |
| r-restfulr,single_cell,Models a RESTful service as if it were a nested R list.,5,no,no | |
| r-saige,single_cell,"SAIGE is an R package with Scalable and Accurate Implementation of Generalized mixed model (Chen, H. et al. 2016); SAIGE is an R package with Scalable and Accurate Implementation of Generalized.",1,no,no | |
| r-sceasy,single_cell,A package providing functions to convert between different single-cell data formats.,1,no,no | |
| r-scopfunctions,single_cell,An R package of functions for single cell -omics analysis.,8,no,no | |
| r-scpred,single_cell,MCP wrapper for r scpred.,3,no,no | |
| r-seurat,single_cell,MCP wrapper for r seurat.,9,no,no | |
| r-seurat-data,single_cell,"Single cell RNA sequencing datasets can be large, consisting of matrices that contain expression data for several thousand features across several thousand cells. This package is designed to easily install, manage, and learn about various single-cell datasets, provided Seurat objects and distributed as independent packages.",5,no,no | |
| r-seurat-disk,single_cell,"The h5Seurat file format is specifically designed for the storage and analysis of multi-modal single-cell and spatially-resolved expression experiments, for example, from CITE-seq or 10X Visium technologies. It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Scanpy.",4,no,no | |
| r-seurat-scripts,single_cell,MCP wrapper for r seurat scripts.,1,no,no | |
| r-signac,single_cell,MCP wrapper for r signac.,3,no,no | |
| racon,genomics,Auto-indexed MCP server for racon.,1,no,no | |
| raxml,single_cell,Phylogenetics - Randomized Axelerated Maximum Likelihood.,5,no,no | |
| recognizer,general,"A tool for domain based annotation with the COG database; reCOGnizer performs domain based annotation with RPS-BLAST, using.",1,no,no | |
| repeatmasker,single_cell,RepeatMasker is a program that screens DNA sequences for interspersed repeats and low complexity DNA sequences.,2,no,no | |
| rgi,genomics,"This tool provides a preliminary annotation of your DNA sequence(s) based upon the data available in The Comprehensive Antibiotic Resistance Database (CARD). Hits to genes tagged with Antibiotic Resistance ontology terms will be highlighted. As CARD expands to include more pathogens, genomes, plasmids, and ontology terms this tool will grow increasingly powerful in providing first-pass detection of antibiotic resistance associated genes. See license at CARD website.",9,no,no | |
| rnastructure,single_cell,"RNAstructure is a complete package for RNA and DNA secondary structure prediction and analysis. It includes algorithms for secondary structure prediction, including facility to predict base pairing probabilities. It also can be used to predict bimolecular structures and can predict the equilibrium binding affinity of an oligonucleotide to a structured RNA target. This is useful for siRNA design. It can also predict secondary structures common to two, unaligned sequences, which is much more accurate than single sequence secondary structure prediction. Finally, RNAstructure can take a number of different types of experiment mapping data to constrain or restrain structure prediction. These include chemical mapping, enzymatic mapping, NMR, and SHAPE data.",10,no,no | |
| rnftools,genomics,"RNF framework for NGS: simulation of reads, evaluation of mappers, conversion of RNF-compliant data.",1,no,no | |
| rpsbproc,single_cell,Auto-indexed MCP server for rpsbproc.,1,no,no | |
| rsa,single_cell,Pure-Python RSA implementation.,6,no,no | |
| rscape,transcriptomics,MCP wrapper for rscape.,1,no,no | |
| rsem,transcriptomics,MCP wrapper for rsem.,3,no,no | |
| rseqc,single_cell,QC package for RNA-seq data.,18,no,no | |
| rtg-tools,general,Auto-indexed MCP server for rtg-tools.,1,no,no | |
| rust-bio-tools,transcriptomics,A growing collection of fast and secure command line utilities for dealing with NGS data.,10,no,no | |
| sage-proteomics,proteomics,Proteomics searching so fast it feels like magic.,1,no,no | |
| salmon,transcriptomics,Auto-indexed MCP server for salmon.,1,no,yes | |
| samap,single_cell,The SAMap algorithm.,3,no,no | |
| sambamba,single_cell,MCP wrapper for sambamba.,11,no,no | |
| samblaster,genomics,MCP wrapper for samblaster.,1,no,no | |
| samsift,single_cell,Advanced filtering and tagging of SAM/BAM alignments using Python expressions.,1,no,no | |
| samtools,single_cell,MCP wrapper for samtools.,29,yes,yes | |
| sc-musketeers,single_cell,A tri-partite modular autoencoder for addressing imbalanced cell type annotation and batch effect reduction.,1,no,no | |
| scaden,single_cell,Cell type deconvolution using single cell data.,6,no,no | |
| scanpy,single_cell,MCP wrapper for scanpy.,1,no,no | |
| scanpy-cli,single_cell,CLI for Scanpy.,13,no,no | |
| scanpy-scripts,single_cell,Scripts for using scanpy from the command line.,16,no,no | |
| scar,single_cell,MCP wrapper for scar.,1,no,no | |
| scarches,single_cell,MCP wrapper for scarches.,6,no,no | |
| sccaf,single_cell,MCP wrapper for sccaf.,1,no,no | |
| sccellfie,pathway_enrichment,A tool for inferring metabolic activities from single-cell and spatial transcriptomics.,4,no,no | |
| scelvis,transcriptomics,MCP wrapper for scelvis.,9,no,no | |
| scepia,single_cell,Single Cell Epigenome-based Inference of Activity.,2,no,no | |
| scglue,single_cell,Graph-linked unified embedding for unpaired single-cell multi-omics data integration; GLUE is a flexible framework that utilizes prior knowledge about feature.,11,no,no | |
| scib,single_cell,Evaluating single-cell data integration methods.,1,no,no | |
| scirpy,single_cell,A Scanpy extension for analyzing single-cell T-cell and B-cell receptor (TCR/BCR) sequencing data.,5,no,no | |
| scmeta,single_cell,MCP wrapper for scmeta.,8,no,no | |
| scmidas,single_cell,A torch-based integration method for single-cell multi-omic data.,3,no,no | |
| scpred-cli,single_cell,MCP wrapper for scpred cli.,2,no,no | |
| scrnasim-toolz,single_cell,Tools used by scRNAsim workflow.,7,no,no | |
| scspectra,single_cell,Supervised discovery of interpretable gene programs from single-cell data.,6,no,no | |
| scstem,single_cell,A method for mapping single-cell and spatial transcriptomics data with transfer learning.,1,no,no | |
| sctriangulate,pathway_enrichment,"A Python package to mix-and-match conflicting clustering results in single cell analysis, and generate reconciled clustering solutions.",1,no,no | |
| scvelo,single_cell,MCP wrapper for scvelo.,1,no,no | |
| scvi,single_cell,Single-cell Variational Inference.,7,no,no | |
| scvi-tools,single_cell,Deep probabilistic analysis of single-cell omics data.,1,no,no | |
| scvis,single_cell,"scvis is a python package for dimension reduction of high-dimensional biological data, especially single-cell RNA-sequencing (scRNA-seq) data.",2,no,no | |
| scvis_galaxy,single_cell,"scvis is a python package for dimension reduction of high-dimensional biological data, especially single-cell RNA-sequencing (scRNA-seq) data.",3,no,no | |
| scxmatch,single_cell,"Python implementation for single-cell cross match test, an efficient implementation of Rosenbaum's test.",1,no,no | |
| sdeper,single_cell,"Spatial Deconvolution method with Platform Effect Removal; SDePER (Spatial Deconvolution method with Platform Effect Removal) is a hybrid machine learning and regression method to deconvolve Spatial barcoding-based transcriptomic data using reference single-cell RNA sequencing data, considering platform effects removal, sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. SDePER is also able to impute cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution.",2,no,no | |
| seacells,single_cell,SEACells algorithm for Inference of transcriptional and epigenomic cellular states from single-cell genomics data.,5,no,no | |
| searchgui,single_cell,MCP wrapper for searchgui.,4,no,no | |
| segemehl,single_cell,Short read mapping with gaps.,2,no,no | |
| sentieon,single_cell,Accelerated performance bioinformatics tools for mapping and variant calling.,1,no,no | |
| sepp,single_cell,SATe-enabled phylogenetic placement.,3,no,no | |
| seq2science,genomics,Automated preprocessing of Next-Generation-Sequencing data.,3,no,no | |
| seqcluster,single_cell,small RNA analysis from NGS data.,1,no,no | |
| seqfu,transcriptomics,MCP wrapper for seqfu.,19,no,no | |
| seqkit,single_cell,MCP wrapper for seqkit.,27,no,no | |
| seqtk,single_cell,MCP wrapper for seqtk.,15,no,no | |
| sequence-operations,single_cell,MCP wrapper for sequence operations.,7,no,no | |
| seurat-scripts,single_cell,MCP wrapper for seurat scripts.,11,no,no | |
| sevenbridges-python,single_cell,SBG API python client bindings; sevenbridges-python is a Python library that provides an interface for the Seven Bridges Platform the Cancer Genomics Cloud and Cavatica public APIs. It works with Python versions 2.6+ and supports Python 3.,4,no,no | |
| shiba,transcriptomics,A versatile method for systematic identification of differential RNA splicing across platforms; A versatile computational method for systematic identification of differential RNA splicing.,2,no,no | |
| simo-omics,single_cell,Spatial integration of multi-omics single-cell datasets through probabilistic alignment.,1,no,no | |
| simplejson,single_cell,"Simple, fast, extensible JSON encoder/decoder for Python.",2,no,no | |
| sina,single_cell,MCP wrapper for sina.,3,no,no | |
| sincei,single_cell,"A user-friendly toolkit for QC, counting, clustering and plotting of single-cell (epi)genomics data.",6,no,no | |
| slow5tools,single_cell,MCP wrapper for slow5tools.,11,no,no | |
| snakemake,single_cell,A popular workflow management system aiming at full in-silico reproducibility; Snakemake is a workflow management system that aims to reduce the complexity of creating.,6,no,no | |
| snakemake-interface-common,general,Common functions and classes for Snakemake and its plugins.,1,no,no | |
| snakemake-interface-executor-plugins,single_cell,This package provides a stable interface for interactions between Snakemake and its executor plugins.,1,no,no | |
| snakemake-interface-logger-plugins,general,This package provides a stable interface for interactions between Snakemake and its logger plugins.,1,no,no | |
| snakemake-interface-report-plugins,single_cell,This package provides a stable interface for interactions between Snakemake and its report plugins.,5,no,no | |
| snakemake-interface-storage-plugins,single_cell,This package provides a stable interface for interactions between Snakemake and its storage plugins.,5,no,no | |
| snakemake-minimal,single_cell,A popular workflow management system aiming at full in-silico reproducibility; Snakemake is a workflow management system that aims to reduce the complexity.,8,no,no | |
| snakemake-wrapper-utils,genomics,A collection of utility functions and classes for Snakemake wrappers.,5,no,no | |
| snakesv,genomics,snakeSV: Flexible framework for large-scale SV discovery.,4,no,no | |
| snap-aligner,genomics,Auto-indexed MCP server for snap-aligner.,1,no,no | |
| snippy,genomics,MCP wrapper for snippy.,5,no,no | |
| snp2cell,single_cell,MCP wrapper for snp2cell.,8,no,no | |
| snpeff,genomics,MCP wrapper for snpeff.,6,yes,no | |
| snpsift,transcriptomics,MCP wrapper for snpsift.,1,no,no | |
| sopa,single_cell,Spatial-omics pipeline and analysis.,8,no,no | |
| sorted_nearest,general,Find nearest interval.,1,no,no | |
| sortmerna,transcriptomics,MCP wrapper for sortmerna.,3,no,no | |
| sourmash,single_cell,"Quickly search, compare, and analyze genomic and metagenomic data sets.",8,no,no | |
| spades,single_cell,SPAdes (St. Petersburg genome assembler) is intended for both standard isolates and single-cell MDA bacteria assemblies; SPAdes (St. Petersburg genome assembler) is a genome assembly algorithm which was designed for.,10,no,no | |
| spagrn,single_cell,"A comprehensive tool to infer TF-centered, spatial gene regulatory networks for the spatially resolved transcriptomics (SRT) data.",1,no,no | |
| spapros,single_cell,Probe set selection for targeted spatial transcriptomics.,5,no,no | |
| spatialleiden,single_cell,Implementation of multiplex Leiden for analysis of spatial omics data.,1,no,no | |
| sra-tools,transcriptomics,MCP wrapper for sra tools.,6,no,no | |
| stacks,single_cell,MCP wrapper for stacks.,15,no,no | |
| star,transcriptomics,An RNA-seq read aligner.,4,no,yes | |
| star-fusion,transcriptomics,MCP wrapper for star fusion.,4,no,no | |
| stream,single_cell,"STREAM-Single-cell Trajectories Reconstruction, Exploration And Mapping.",8,no,no | |
| stringtie,genomics,MCP wrapper for stringtie.,2,no,no | |
| subread,transcriptomics,"High-performance read alignment, quantification, and mutation discovery.",8,no,yes | |
| switchtfi,single_cell,Implementation of the SwitchTFI method as presented in: https:.,1,no,no | |
| t-coffee,transcriptomics,MCP wrapper for t coffee.,5,no,no | |
| t_coffee,single_cell,"A collection of tools for Computing, Evaluating and Manipulating Multiple Alignments of DNA, RNA, Protein Sequences and Structures.",5,no,no | |
| tabix,genomics,MCP wrapper for tabix.,1,yes,no | |
| tabixpp,genomics,"A C++ wrapper around the tabix project, a generic indexer for TAB-delimited genome position files.",1,yes,no | |
| talon,genomics,TALON is a Python package for identifying and quantifying known and novel.,8,no,no | |
| taxonkit,single_cell,MCP wrapper for taxonkit.,12,no,no | |
| tb-profiler,genomics,Profiling tool for Mycobacterium tuberculosis to detect drug resistance and lineage from sequencing data.,5,no,no | |
| thapbi-pict,general,THAPBI Phytophthora ITS1 Classifier Tool (PICT); THAPBI Phytophthora ITS1 Classifier Tool (PICT) an ITS1-based.,1,no,no | |
| tidyp,single_cell,MCP wrapper for tidyp.,2,no,no | |
| tirank,pathway_enrichment,A comprehensive analysis tool for transferring phenotype of bulk transcriptomic data to single-cell or spatial transcriptomic data; TiRank integrates deep learning and statistical analysis to infer phenotype.,2,no,no | |
| tmalign,single_cell,TM-align sequence-order independent protein structure alignment.,5,no,no | |
| tobias,single_cell,Transcription factor Occupancy prediction By Investigation of ATAC-seq Signal; TOBIAS (Transcription factor Occupancy prediction By Investigation of ATAC-seq Signal) is a collection.,1,no,no | |
| toil,single_cell,"A scalable, efficient, cross-platform and easy-to-use workflow engine in pure Python.",1,no,no | |
| transdecoder,single_cell,Auto-indexed MCP server for transdecoder.,1,no,no | |
| treetime,single_cell,Maximum-Likelihood dating and ancestral inference for phylogenetic trees.,5,no,no | |
| trf,single_cell,Auto-indexed MCP server for trf.,1,no,no | |
| trim-galore,single_cell,Trim Galore! is a wrapper script to automate quality and adapter trimming as well as quality control.,3,no,no | |
| trimadap,single_cell,Fast but inaccurate adapter trimmer for Illumina reads.,1,no,no | |
| trimal,general,Auto-indexed MCP server for trimal.,1,no,no | |
| trimmomatic,single_cell,MCP wrapper for trimmomatic.,2,no,no | |
| trinity,transcriptomics,MCP wrapper for trinity.,4,no,no | |
| trnascan-se,single_cell,tRNA detection in large-scale genomic sequences.,4,no,no | |
| ucsc-bedgraphtobigwig,single_cell,Convert a bedGraph file to bigWig format.,1,no,no | |
| ucsc-bedtobigbed,single_cell,Convert bed file to bigBed. (BigBed version: 4).,1,no,no | |
| ucsc-cell-browser,single_cell,"A browser for single-cell data, main site at http://cells.ucsc.edu. UCSC Cellbrowser, an interactive browser for single cell data. Includes importers and basic pipelines for text files, Seurat, Scanpy and Cellranger. All Javascript - does not require a server backend.",10,no,no | |
| ucsc-fatotwobit,single_cell,Convert DNA from fasta to 2bit format.,1,yes,no | |
| ucsc-gtftogenepred,single_cell,Convert a GTF file to a genePred.,1,no,no | |
| ucsc-liftover,single_cell,Move annotations from one assembly to another.,2,yes,no | |
| ucsc-nibfrag,single_cell,Extract part of a nib file as .fa (all bases/gaps lower case by default).,1,no,no | |
| ucsc-twobitinfo,single_cell,Get information about sequences in a .2bit file.,1,no,no | |
| ucsc-twobittofa,single_cell,Convert all or part of .2bit file to fasta.,1,yes,no | |
| ucsc-wigtobigwig,single_cell,"Convert ascii format wig file (in fixedStep, variableStep).",1,no,no | |
| umi_tools,single_cell,Tools for dealing with Unique Molecular Identifiers (UMIs) / Random Molecular Tags (RMTs).,1,no,no | |
| umis,transcriptomics,Tools for processing UMI RNA-tag data.,5,no,no | |
| unicycler,single_cell,MCP wrapper for unicycler.,3,no,no | |
| unifrac,single_cell,Fast phylogenetic diversity calculations; UniFrac is a commonly phylogenetic diversity distance metric used in.,2,no,no | |
| unifrac-binaries,single_cell,Fast phylogenetic diversity calculations; UniFrac is a commonly phylogenetic diversity distance metric used in.,1,no,no | |
| upimapi,general,UniProt Id Mapping through API; UPIMAPI takes as input either a list of UniProt IDs or a blast file from.,1,no,no | |
| urllib3,single_cell,"HTTP library with thread-safe connection pooling, file post, and more.",4,no,no | |
| usher,single_cell,Ultrafast Sample Placement on Existing Trees (UShER).,4,no,no | |
| vardict,transcriptomics,Auto-indexed MCP server for vardict.,1,no,no | |
| vardict-java,genomics,MCP wrapper for vardict java.,5,no,no | |
| varlociraptor,single_cell,Auto-indexed MCP server for varlociraptor.,1,no,no | |
| varscan,single_cell,Auto-indexed MCP server for varscan.,1,no,no | |
| vcflib,transcriptomics,Auto-indexed MCP server for vcflib.,1,no,no | |
| vcfpy,single_cell,Python 3 VCF library with good support for both reading and writing.,6,no,no | |
| vcftools,genomics,MCP wrapper for vcftools.,6,yes,no | |
| vcontact2,single_cell,Viral Contig Automatic Clustering and Taxonomy.,4,no,no | |
| viennarna,single_cell,ViennaRNA package -- RNA secondary structure prediction and comparison.,23,no,no | |
| vitessce-python,single_cell,Jupyter widget facilitating interactive visualization of spatial single-cell data with Vitessce.,1,no,no | |
| vpt,single_cell,Command line tool for highly parallelized processing of Vizgen data.,11,no,no | |
| vsearch,general,Auto-indexed MCP server for vsearch.,1,no,no | |
| vt,single_cell,A tool set for short variant discovery in genetic sequence data.,11,no,no | |
| wasp2,genomics,Allele-specific analysis of next-generation sequencing data with Rust acceleration; WASP2 is a high-performance tool for allele-specific analysis of NGS data.,6,no,no | |
| whatshap,genomics,Phase genomic variants using DNA sequencing reads (haplotype assembly).,8,no,no | |
| womtool,single_cell,MCP wrapper for womtool.,6,no,no | |
| xclone,single_cell,Inference of clonal Copy Number Alterations in single cells.,5,no,no | |
| xopen,general,Open compressed files transparently in Python.,1,no,no | |
| yacrd,single_cell,MCP wrapper for yacrd.,5,no,no | |
| zdb,transcriptomics,MCP wrapper for zdb.,6,no,no | |
| zol,genomics,zol (& fai): large-scale targeted detection and evolutionary investigation of gene clusters.,11,no,no | |