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Metagenomic pan-genome profiling

This script will map metagenomic reads to bacterial pangenomes and quantify these genes in your data. You can either target one or more specific species (--species_id), or provide this script a species abundance file.

The pipeline can be broken down into the following steps:

  • build a database of pangenomes for abundance bacterial species
  • map high-quality metagenomic reads to the database
  • use mapped reads to quantify pangenome genes

Usage

Usage: run_midas.py genes <outdir> [options]

positional arguments:
  outdir                Path to directory to store results.
                        Directory name should correspond to sample identifier

optional arguments:
  -h, --help            show this help message and exit
  --remove_temp         Remove intermediate files generated by MIDAS (False).
                        Useful to reduce disk space of MIDAS output

Pipeline options (choose one or more; default=all):
  --build_db            Build bowtie2 database of pangenomes
  --align               Align reads to pangenome database
  --call_genes          Compute coverage of genes in pangenome database

Database options (if using --build_db):
  -d DB                 Path to reference database
                        By default, the MIDAS_DB environmental variable is used
  --species_cov FLOAT   Include species with >X coverage (3.0)
  --species_topn INT    Include top N most abundant species
  --species_id CHAR     Include specified species. Separate ids with a comma

Read alignment options (if using --align):
  -1 M1                 FASTA/FASTQ file containing 1st mate if using paired-end reads.
                        Otherwise FASTA/FASTQ containing unpaired reads.
                        Can be gzip'ed (extension: .gz) or bzip2'ed (extension: .bz2)
  -2 M2                 FASTA/FASTQ file containing 2nd mate if using paired-end reads.
                        Can be gzip'ed (extension: .gz) or bzip2'ed (extension: .bz2)
  --interleaved         FASTA/FASTQ file in -1 are paired and contain forward AND reverse reads
  -s {very-fast,fast,sensitive,very-sensitive}
                        Alignment speed/sensitivity (very-sensitive)
  -m {local,global}     Global/local read alignment (local)
  -n MAX_READS          # reads to use from input file(s) (use all)
  -t THREADS            Number of threads to use (1)

Quantify genes options (if using --call_genes):
  --readq INT           Discard reads with mean quality < READQ (20)
  --mapid FLOAT         Discard reads with alignment identity < MAPID (94.0)
  --aln_cov FLOAT       Discard reads with alignment coverage < ALN_COV (0.75)
  --trim INT            Trim N base-pairs from 3'/right end of read (0)

Examples

  1. run entire pipeline using defaults:
    run_midas.py genes /path/to/outdir -1 /path/to/reads_1.fq.gz -2 /path/to/reads_2.fq.gz

  2. run entire pipeline for a specific species:
    run_midas.py genes /path/to/outdir --species_id Bacteroides_vulgatus_57955 -1 /path/to/reads_1.fq.gz -2 /path/to/reads_2.fq.gz

  3. just align reads, use faster alignment, only use the first 10M reads, use 4 CPUs:
    run_midas.py genes /path/to/outdir --align -1 /path/to/reads_1.fq.gz -2 /path/to/reads_2.fq.gz -s very-fast -n 10000000 -t 4

  4. just quantify genes, keep reads with >=95% alignment identity and reads with an average quality-score >=30:
    run_midas.py genes /path/to/outdir --call_genes --mapid 95 --readq 20

Output files

output: directory of per-species output files; files are tab-delimited, gzip-compressed, with header.
species.txt: list of species_ids included in local database
summary.txt: tab-delimited with header; summarizes alignment results per-species
log.txt: log file containing parameters used
temp: directory of intermediate files; run with --remove_temp to remove these files

Output formats

output/<species_id>.genes.gz

  • gene_id: id of non-redundant gene used for read mapping; 'peg' and 'rna' indicate coding & RNA genes respectively
  • count_reads: number of aligned reads to gene_id after quality filtering
  • coverage: average read-depth of gene_id based on aligned reads (# aligned bp / gene length in bp)
  • copy_number: estimated copy-number of gene_id based on aligned reads (coverage of gene_id / median coverage of 15 universal single copy genes)

summary.txt

  • species_id: species id
  • pangenome_size: number of non-redundant genes in reference pan-genome
  • covered_genes: number of genes with at least 1 mapped read
  • fraction_covered: proportion of genes with at least 1 mapped read
  • mean_coverage: average read-depth across genes with at least 1 mapped read
  • marker_coverage: median read-depth across 15 universal single copy genes
  • aligned_reads: number of aligned reads BEFORE quality filtering
  • mapped_reads: number of aligned reads AFTER quality filtering

Memory usage

  • Memory usage will depend on the number of species you search and the number of reference genomes sequenced per species.
  • In practice, peak memory usage will not exceed 1 Gb for most samples

Speed

  • Speed will depend on the number of species you search and the number of reference genomes sequenced per species.
  • For a single species with 1 reference genome, expect ~16,000 reads/second
  • Use -n and -t to increase throughput

Next step

Merge results across samples