---
license: mit
---

# Extended-BioAgentBench

> **A growing benchmark for evaluating LLM agents on complex bioinformatics workflows.**

[![Dataset on HF](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench)
[![Tasks](https://img.shields.io/badge/Tasks-71-blue)]()
[![Based on](https://img.shields.io/badge/Based%20on-BioAgentBench-green)](https://github.com/bioagent-bench/bioagent-bench)

Building on [BioAgentBench](https://arxiv.org/abs/2601.21800) (10 tasks), this benchmark adds **71 new tasks** that test LLM agents on increasingly complex, multi-tool bioinformatics pipelines. Tasks span 6 domains and range from simple linear workflows to depth-8 diamond DAGs with 16+ CLI tools.

**This benchmark is actively growing** — new tasks are added continuously to cover more domains, increase complexity, and push the boundaries of what AI agents can do in bioinformatics.

## What makes this benchmark different?

- **Diamond DAG complexity**: Tasks require running multiple independent tool branches that converge — not just linear pipelines
- **No pipeline leakage**: Task prompts describe *what* to produce, never *which tools* to use or *how* to structure the pipeline
- **Domain-specific traps**: Tasks include steps where default parameters silently produce wrong results (e.g., Tn5 shift correction in ATAC-seq, Medaka model selection for Nanopore)
- **Real public data**: Every task uses published datasets with ground truth generated by validated reference pipelines

## Tasks (71 total)

| # | Task ID | Name |
|---|---------|------|
| 11 | `chipseq-peak-calling` | ChIP-seq Peak Calling: TAL1 Binding Site Comparison |
| 12 | `bacterial-assembly` | Bacterial Genome Assembly and Annotation: MRSA Characte |
| 13 | `mobile-elements` | Bacterial Mobile Genetic Element Characterization: MRSA |
| 14 | `outbreak-investigation` | Foodborne Pathogen Outbreak Investigation via WGS Phylo |
| 15 | `atacseq-accessibility` | ATAC-seq Chromatin Accessibility Profiling |
| 16 | `longread-assembly` | Nanopore Long-read Bacterial Genome Assembly |
| 17 | `hybrid-assembly` | Hybrid Genome Assembly from Illumina and Nanopore Data |
| 18 | `sv-detection` | Bacterial Structural Variant and SNP Detection |
| 19 | `pangenome-evolution` | E. coli Pan-genome and Core Phylogeny |
| 20 | `metagenomic-profiling` | Metagenomic Assembly and Functional Profiling |
| 21 | `phage-characterization` | Bacteriophage Genome Assembly and Functional Characteri |
| 22 | `genome-comparison` | Pairwise Bacterial Genome Comparison |
| 23 | `mapping-qc` | Genome Mapping and Coverage Quality Assessment |
| 24 | `multisample-variants` | Multi-sample Variant Calling and Comparison |
| 25 | `consensus-genome` | Bacterial Consensus Genome Generation |
| 26 | `gene-prediction` | Gene Prediction Method Comparison |
| 27 | `downsampling-analysis` | Read Downsampling and Assembly Quality Titration |
| 28 | `plasmid-typing` | Plasmid Detection and Replicon Typing |
| 29 | `genome-completeness` | Genome Completeness and Quality Assessment |
| 30 | `species-identification` | Multi-reference Bacterial Species Identification |
| 31 | `viral-amplicon` | Viral Amplicon Surveillance Analysis |
| 32 | `bisulfite-methylation` | Bisulfite Sequencing DNA Methylation Analysis |
| 33 | `rnaseq-isoform` | RNA-seq Isoform Assembly and Quantification |
| 34 | `ancient-dna` | Ancient DNA Authentication and Damage Assessment |
| 35 | `mirna-seq` | Small RNA-seq miRNA Discovery and Quantification |
| 36 | `gcms-metabolomics` | GC-MS Metabolomics Profiling: Brown Algae Salinity Adap |
| 37 | `cutandrun` | CUT&RUN Epigenomic Profiling |
| 38 | `scrna-full-pipeline` | Single-Cell RNA-seq Full Pipeline: Multi-Quantifier Ana |
| 39 | `crispr-screen` | CRISPR Screen Analysis: Drug Sensitivity Gene Discovery |
| 40 | `amplicon-microbiome` | 16S Amplicon Microbiome: Community Profiling and Functi |
| 41 | `rna-fusion` | RNA Fusion Detection from RNA-seq |
| 42 | `spatial-transcriptomics` | Spatial Transcriptomics: Visium FFPE Brain Cancer Analy |
| 43 | `taxonomic-profiling` | Multi-classifier Taxonomic Profiling of Metagenomic Rea |
| 44 | `lcms-metabolomics` | LC-MS Untargeted Metabolomics: Urine Feature Discovery |
| 45 | `somatic-variant-calling` | Somatic Variant Calling: Tumor-Normal Paired Analysis |
| 46 | `amr-bgc-screening` | Antimicrobial Resistance and Biosynthetic Gene Cluster  |
| 47 | `variant-trio` | Variant Annotation Trio: Clinical Interpretation of Ash |
| 48 | `clinical-metaproteomics` | Clinical Metaproteomics: Multi-Engine Marine Microbiome |
| 49 | `mhc-immunopeptidomics` | MHC Immunopeptidomics: Peptide Identification and Quant |
| 50 | `riboseq` | Ribosome Profiling Translation Analysis |
| 51 | `neoantigen-prediction` | Neoantigen Prediction: Tumor-Normal Somatic Analysis |
| 52 | `somatic-germline-dual` | Somatic+Germline Dual Analysis: Hereditary Cancer Varia |
| 53 | `hicar-chromatin` | HiCAR Chromatin Interaction: Proximity Ligation and Acc |
| 54 | `radseq-popgen` | RADseq Population Genetics: Stickleback Freshwater-Mari |
| 55 | `mag-recovery` | MAG Recovery: Metagenome-Assembled Genomes from Environ |
| 56 | `viral-phylodynamics` | Viral Phylodynamics (Molecular Clock Analysis) |
| 57 | `edna-metabarcoding` | eDNA Aquatic Metabarcoding Biodiversity Assessment |
| 58 | `metatranscriptomics` | Metatranscriptomics: Active Microbial Community Profili |
| 59 | `nascent-transcription` | Nascent Transcription: GRO-seq Polymerase Activity Prof |
| 60 | `circrna-discovery` | Circular RNA Discovery: C. elegans Wild-type vs fust-1  |
| 61 | `hic-3d-conformation` | Hi-C 3D Genome Conformation Analysis |
| 62 | `genome-scaffolding` | Long-Read Genome Scaffolding of Fragmented Assembly |
| 63 | `longread-rna-isoform` | Long-Read RNA Isoform Discovery from Direct RNA Sequenc |
| 64 | `circrna-detection` | Circular RNA Detection and Quantification |
| 65 | `pharmacogenomics` | CYP2D6 Pharmacogenomic Star Allele Calling |
| 66 | `rna-editing-detection` | RNA Editing Detection: A-to-I Editing from Matched RNA/ |
| 67 | `cnv-detection-wes` | CNV Detection from Whole-Exome Sequencing |
| 68 | `haplotype-phasing` | Haplotype Phasing and Genotype Refinement |
| 69 | `dda-proteomics-simple` | DDA Proteomics: Single-Engine BSA Identification |
| 70 | `immune-repertoire` | Immune Repertoire Analysis (BCR-seq) |
| 71 | `germline-wes-gatk` | Germline WES Variant Calling: Clinical Exome Analysis |
| 72 | `gwas-association` | GWAS Population Association Testing |
| 73 | `dia-proteomics` | Label-Free Proteomics: BSA Standard Identification |
| 74 | `structural-variant-multi` | Structural Variant Detection: Multi-caller Human SV Ana |
| 75 | `dda-lfq-proteomics` | DDA Label-Free Quantitative Proteomics |
| 76 | `clinical-wgs-interpretation` | Clinical WGS Interpretation: Full Clinical Genome Analy |
| 77 | `repeat-element-annotation` | Repeat Element Annotation (Transposable Element Analysi |
| 78 | `msi-detection` | Microsatellite Instability Detection: Multi-Caller Cons |
| 79 | `scatac-seq` | Single-Cell ATAC-seq Chromatin Accessibility Analysis |
| 80 | `multiomics-rna-atac` | Multi-omics Integration (RNA-seq + ATAC-seq) |
| 81 | `methylation-array-epic` | Methylation Array Analysis (Illumina EPIC) |

## Quick start

```bash
# Clone and install
git clone https://github.com/lingzhi227/Extended-BioAgentBench.git
cd Extended-BioAgentBench
pip install click requests

# List tasks
python src/dataset.py list-tasks

# Download a specific task (data + reference + ground truth)
python src/dataset.py download --task outbreak-investigation --reference --results

# Download everything
python src/dataset.py download --all --reference --results
```

## Task format

Each task follows the BioAgentBench format:

```
tasks/<task_id>/
  Dockerfile          # Reproduce ground truth with this
  environment.yml     # Conda environment specification
  run_script.sh       # Reference pipeline (ground truth generator)
```

Data, reference, and results are downloaded via `dataset.py` from [HuggingFace](https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench):

```
tasks/<task_id>/
  data/               # Input data (FASTQ, FASTA, etc.)
  reference/          # Reference genomes (if needed)
  results/            # Ground truth output
```

## Evaluation

Each task prompt provides the expected output format (CSV columns + example values). Evaluation uses **GPT-5.1 as LLM-as-Judge**, scoring:

- `steps_completed` / `steps_to_completion` — how many pipeline stages the agent executed
- `completion_rate` — fraction of the pipeline completed
- `results_match` — full_match / partial_match / no_match against ground truth

## Contributing new tasks

This benchmark is designed to grow. To add a new task:

1. Pick a bioinformatics domain not yet covered
2. Find small public data (< 1 GB, runtime < 4h on 8 CPUs)
3. Write `run_script.sh` to generate ground truth
4. Write a task prompt that says *what* to produce, not *how*
5. Verify the prompt doesn't leak tool names or pipeline structure
6. Submit a PR

## Citation

Based on BioAgentBench:
```bibtex
@article{patino2025bioagentbench,
  title={BioAgentBench: A Benchmark for Evaluating LLM Agents in Bioinformatics},
  author={Patino, Luis and others},
  journal={arXiv preprint arXiv:2601.21800},
  year={2025}
}
```