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license: mit |
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# Datasets for MicrobeRT |
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<!-- This sheet is to show the collection information and stats for all of the datasets used to train the MicrobeRT Models --> |
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<!-- It includes info for 1. Taxonomy, 2. AMR, and 3. Pathogenicity --> |
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## Taxonomy |
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Data collection was pulled using the [ncbi-genome-download](https://github.com/kblin/ncbi-genome-download) package on both [Genbank](https://www.ncbi.nlm.nih.gov/genbank/) and [RefSeq](https://www.ncbi.nlm.nih.gov/refseq/) assemblies for fungal, viral, bacterial, archaeal genomes. |
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For fungi, complete, chromosome, and scaffold assemblies were downloaded from GenBank. For comparison, a single representative assembly was retrieved for potential host organisms, including human, cow, dog, domestic cat, pig, wheat, and corn. |
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For bacterial, archaea, and viral genomes we included both complete & all representative assemblies. |
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| Superkingdom / Kingdom | Representation (n) | |
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|-----------------------|--------------------| |
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| Archaea | 1,252,736 | |
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| Bacteria | 145,122,383 | |
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| Eukaryota | 8,633,180 | |
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| Viruses | 717,342 | |
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| **Total** | **155,725,641** | |
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To minimize redundancy, a cluster-based down-selection was applied. Full-length sequences were first randomly fragmented into subsequences of 750-3500 base pairs (bp). These subsequences were clustered using [MMSeqs2](https://github.com/soedinglab/MMseqs2) at 90% identity within each taxonomic family, and one representative sequence was retained per cluster. The resulting representatives were then clustered again at 70% identity, with a single sequence selected from each cluster to form the final dataset. In addition, derivative datasets of defined sequence lengths were generated to benchmark trained genomic tokenizers. A hold-out set of ~1% of the total dataset, comprising 1,572,987 sequences, was reserved for testing. |
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Two datasets were created to reflect similar sequence sizes to standard sequencing platform output lengths. That is, each model trained would perform more effectively the closer to an observed sequence was to the original trained ones. We created 2 separate datasets for Long reads at a max of 1750 bp and Short reads at a max of 300 bp. Each of these files were evenly fragmented into x number of subsequences depending on the original MMseqs2 clustered sequence length. That is, the maximum subsequence was targeted based on the training type (Long or Short). |
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| Superkingdom / Kingdom | Long Read (n) | Short Read (n) | |
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| ---------------------- | --------------- | --------------- | |
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| Archaea | 1,290,396 | 5,169,673 | |
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| Bacteria | 148,337,911 | 569,859,777 | |
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| Fungal | 8,990,001 | 36,070,662 | |
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| Viruses | 917,475 | 3,239,241 | |
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| **Total** | **159,535,783** | **614,339,353** | |
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## Anti-Microbial Resistance (AMR) |
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Data was pulled from the [Resistance Gene Identifier (RGI)](https://card.mcmaster.ca/analyze/rgi) which predicts antibiotic resistomes from protein or nucleotide data based on homology and single nucleotide polymorphism (SNP) models using the [Comprehensive Antibiotic Resistance Database (CARD)](https://card.mcmaster.ca/). Both the Perfect and Strict algorithms were used to pull the sequences. |
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A total of 672,392 AMR-positive sequences and 3,598,448,873 AMR-negative sequences were pulled from the database. However, we "chunked" each into fragments of a varying range between 140 & 475 bp. MMseqs2 was applied to reduce redundancy at 100% and 70% for positive and negative sequences, respectively. The final count is as shown below: |
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| AMR Status | Representation (n) | |
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|------------|--------------------| |
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| Negative | 86,866,041 | |
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| Positive | 3,000,091 | |
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| **Total** | **89,866,132** | |
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Performance was assessed under different sequence lengths, curated into 5 datasets in total. The first contained full-length AMR sequences from the CARD and [NCBI AMR](https://www.ncbi.nlm.nih.gov/pathogens/antimicrobial-resistance/) Reference databases. The second included partial AMR sequences randomly truncated to 140–450 bp. The remaining three consisted of full AMR sequences flanked on both ends by 50 bp, 100 bp, or randomly distributed lengths of 50–2500 bp. Table 3 lists the number of sequences in each set. |
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| Test Set | Representation (n) | |
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|---------------------------------------|--------------------| |
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| Whole AMR | 14,069 | |
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| Partial AMR | 20,000 | |
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| 50 bp flank | 12,093 | |
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| 100 bp flank | 12,093 | |
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| Random flanks between 50 and 2500 bp | 12,093 | |
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In addition to a binary classification of sequences resistance, information about the specific antibiotic to which each sequence shows resistance was extracted from the RGI output to assess the models’ ability to perform multi-class classification. To generate negative controls, positional information was used to extract sequence fragments that did not map to antimicrobial resistance genes (ARGs). |
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| Antibiotic Drug Class | Training (n) | Whole AMR (n) | Partial AMR (n) | 50 bp flank (n) | 100 bp flank (n) | Random flanks between 50 and 2500 bp (n) | |
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|----------------------|--------------|---------------|-----------------|------------------|-------------------|-----------------------------------------| |
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| acriflavine | 580 | | | | | | |
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| aminocoumarin | 16,217 | 297 | 1 | 1 | 1 | 1 | |
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| aminoglycoside | 13,062 | | 383 | 182 | 182 | 182 | |
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| bacitracin | 1,128 | | | | | | |
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| beta_lactam | 10,299 | 5809 | 2850 | 4906 | 490 | 4906 |6 |
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| bleomycin | 6 | | | | | | | |
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| fosfomycin | 3,658 | 11 | 1229 | 37 | 37 | 37 | |
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| fusidic_acid | 51 | 7 | 10 | 9 | 9 | 9 | |
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| glycopeptide | 65,297 | | | | | | |
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| kasugamycin | 18 | | | | | | |
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| linezolid | 11 | | | | | | |
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| macrolide-lincosamide-streptogramin | 4,994 | 164 | 422 | 131 | 131 | 131 | |
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| multidrug | 9,359 | 4 | 10 | 7 | | 7 | |
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| mupirocin | 82 | | 8 | 3 | 3 | 3 | |
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| peptide | 686 | 109 | 730 | 210 | 210 | 210 | |
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| phenicol | 2,466 | 84 | 1443 | 59 | 59 | 59 | |
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| pleuromutilin | 1,271 | 12 | 1271 | 28 | 28 | 28 | |
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| polymyxin | 13,479 | | | | | | |
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| puromycin | 3,619 | 19 | 32 | 23 | 23 | 23 | |
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| quinolone | 22,297 | 183 | 66 | 162 | 162 | 162 | |
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| rifampin | 8,648 | 29 | 41 | 43 | 43 | 43 | |
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| roxithromycin | 508 | | | | | | |
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| streptothricin | 72 | | | | | | |
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| sulfonamide | 191 | 7 | 750 | 6 | 6 | 6 | |
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| tetracycline | 100,124 | 81 | 396 | 109 | 109 | 109 | |
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| triclosan | 13,045 | | | | | | |
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| trimethoprim | 8,801 | 120 | 111 | 102 | 102 | 102 | |
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| tunicamycin | 110 | | | | | | |
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| viomycin | 12 | | | | | | |
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| **Total** | **300091** | **6936** | **9753** | **6018** | **6018*** | **6018** | |
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## Data and Metadata Paths |
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### Taxonomy - Load Read |
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- **train:** `nucl_gb_train.csv` |
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- **test:** `nucl_gb_test.csv` |
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- **data_processor:** `data_processor.pkl` |
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- **metadata.json:** `metadata.json` |
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### AMR - Binary |
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- **train:** `train_process.csv` |
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- **test:** `test_process.csv` |
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- **data_processor:** `data_processor_amr.pkl` |
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- **metadata.json:** `metadata_amr.json` |
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### AMR - Multiclass |
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- **train:** `train_process_multiclass.csv` |
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- **test:** `test_process_multiclass.csv` |
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- **data_processor:** `data_processor_amr_multiclass.pkl` |
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- **metadata.json:** `metadata_amr_multiclass.json` |
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## Loading in train/val/test sets |
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All datasets presented here are already fitted with a label encoder and split into train/val/test sets. |
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To inverse transform the dataset labels to their original label values, use the corresponding data_processor.pkl and metadata.json files. |
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Here is a sample code snippet using code using helper functions in the code repository [github.com/jhuapl-bio/microbert](https://github.com/jhuapl-bio/microbert). Use this to reverse transform the dataset labels for ease of use. |
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``` |
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import json |
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import pandas as pd |
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from analysis.experiment.utils.data_processor import DataProcessor |
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metadata_path = 'taxonomy/long_read/data_processor/metadata.json' |
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data_processor_dir = 'taxonomy/long_read/data_processor' |
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dataset_path = 'taxonomy/long_read/nucl_gb_test.csv' |
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with open(metadata_path, "r") as f: |
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metadata = json.load(f) |
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sequence_column = metadata["sequence_column"] |
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labels = metadata["labels"] |
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data_processor_filename = "data_processor.pkl" |
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data_processor = DataProcessor( |
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sequence_column=sequence_column, |
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labels=labels, |
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save_file=data_processor_filename, |
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) |
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data_processor.load_processor(data_processor_dir) |
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df = pd.read_csv(dataset_path) |
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for label in labels: |
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label_col = f"label_level_{label}" |
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encoded_values = df[label_col] |
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df[label] = data_processor.encoders[label].inverse_transform(encoded_values) |
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``` |
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