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idseq-bench

Benchmark generator for the IDSeq Portal.

So far just a thin wrapper around InSilicoSeq.

setup

pip3 install InSilicoSeq
pip3 install ncbi-acc-download

running

python3 main.py

This produces zipped fastq files that you can upload to the IDSeq Portal via IDSEQ-CLI.

selecting organisms and chromosomes

Add/modify entries like this in params.py

    Genome("fungi", "aspergillus_fumigatus",
           [("subspecies", 330879), ("species", 746128), ("genus", 5052), ("family", 1131492)],
           ["NC_007194.1", "NC_007195.1", "NC_007196.1", "NC_007197.1", "NC_007198.1", "NC_007199.1",
            "NC_007200.1", "NC_007201.1"],
           "https://www.ncbi.nlm.nih.gov/genome/18?genome_assembly_id=22576"),

tweaking InSilicoSeq options

Edit params.py or main.py as desired, e.g., to select a different set of error models.

If the output data changes, we expect the output file name to change as well. It's always a good idea to increment LOGICAL_VERSION (whole numbers only) after making changes to the code.

interpreting the output

Each output file name reflects the params of its generation, like so:

norg_6__nacc_27__uniform_weight_per_organism__hiseq_reads__v4__[R1, R2].fastq.gz
  -- number of organisms: 6
  -- number of accessions: 27
  -- distribution: uniform per organism
  -- error model: hiseq
  -- logical version: 4

TODO: Random generator seed control.

We generate a summary file for each pair of fastqs, indicating read counts per organism, and the average coverage of the organism's genome. Each pair counts as 2 reads / 300 bases, matching InSilicoSeq and IDSeq conventions.

READS  COVERAGE    LINEAGE                                          GENOME
----------------------------------------------------------------------------------------------------------------------
16656    215.3x    benchmark_lineage_0_37124_11019_11018            viruses__chikungunya__37124
16594      0.1x    benchmark_lineage_330879_746128_5052_1131492     fungi__aspergillus_fumigatus__330879
16564    352.1x    benchmark_lineage_0_463676_12059_12058           viruses__rhinovirus_c__463676
16074      0.5x    benchmark_lineage_1125630_573_570_543            bacteria__klebsiella_pneumoniae_HS11286__1125630
15078      0.8x    benchmark_lineage_93061_1280_1279_90964          bacteria__staphylococcus_aureus__93061
14894      0.1x    benchmark_lineage_36329_5833_5820_1639119        protista__plasmodium_falciuparum__36329

We alter the read id's generated by ISS to satisfy the input requirements of tools like STAR. This requires stripping those _1 and _2 pair indicators from all read id's, so that both reads in a pair have the exact same read ID. Then, each read ID gets a serial number, and a tag identifying the taxonomic lineage of the organism that read was sourced from, like so:

@NC_016845.1_503__benchmark_lineage_1125630_573_570_543__s0000001169

This is helpful in tracking reads through complex bioinformatic pipelines and scoring results. We assume the pipelines would not cheat by inspecting those tags.