Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data

Hall, Michael B., and Lachlan J. M. Coin. “Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data” GigaScience, April 4, 2024. https://doi.org/10.1093/gigascience/giae010

Benchmarking different ways of doing read (taxonomic) classification, with a focus on removal of contamination and classification of M. tuberculosis reads.

This repository contains the code and snakemake pipeline to build/download the databases, obtain all results from the paper, along with accompanying configuration files.

Custom databases have all been uploaded to Zenodo, along with the simulated reads:

• Nanopore simulated metagenomic reads - https://doi.org/10.5281/zenodo.8339788
• Illumina simulated metagenomic reads - https://doi.org/10.5281/zenodo.8339790
• Nanopore and Illumina artificial real reads - https://doi.org/10.5281/zenodo.10472796
• Kraken2 database built from the Human Pangenome Reference Consortium genomes - https://doi.org/10.5281/zenodo.8339731
• Kraken2 database built from the kraken2 Human library - https://doi.org/10.5281/zenodo.8339699
• Kraken2 database built from a Mycobacterium representative set of genomes - https://doi.org/10.5281/zenodo.8339821
• A (fasta) database of representative genomes from the Mycobacterium genus - https://doi.org/10.5281/zenodo.8339940
• A (fasta) database of M. tuberculosis genomes from a variety of lineages - https://doi.org/10.5281/zenodo.8339947
• The fasta file built from the Clockwork decontamination pipeline - https://doi.org/10.5281/zenodo.8339802

Example usage

We provide some usage examples showing how to download the databases and then use them on your reads.

Human read removal

The method we found to give the best balance of runtime, memory usage, and precision and recall was kraken2 with a database built from the Human Pangenome Reference Consortium genomes.

This example has been wrapped into a standalone tool called nohuman which takes a fastq as input and returns a fastq with human reads removed.

Download human database

mkdir HPRC_db/
cd HPRC_db
URL="https://zenodo.org/record/8339732/files/k2_HPRC_20230810.tar.gz"
wget "$URL"
tar -xzf k2_HPRC_20230810.tar.gz
rm k2_HPRC_20230810.tar.gz

Run kraken2 with HPRC database

You'll need kraken2 installed for this step.

kraken2 --threads 4 --db HPRC_db/ --output classifications.tsv reads.fq

If you are using Illumina reads, a slight adjustment is needed

kraken2 --paired --threads 4 --db HPRC_db/ --output classifications.tsv reads_1.fq reads_2.fq

Extract non-human reads

You'll need seqkit installed for this step

For Nanopore data

awk -F'\t' '$1=="U" {print $2}' classifications.tsv | \
  seqkit grep -f - -o reads.depleted.fq reads.fq

For Illumina data

awk -F'\t' '$1=="U" {print $2}' classifications.tsv > ids.txt
seqkit grep --id-regexp '^(\S+)/[12]' -f ids.txt -o reads_1.depleted.fq reads_1.fq
seqkit grep --id-regexp '^(\S+)/[12]' -f ids.txt -o reads_2.depleted.fq reads_2.fq

M. tuberculosis classification/enrichment

For this step we recommend either minimap2 or kraken2 with a Mycobacterium genus database. We leave it to the user to decide which approach they prefer based on the results in our manuscript.

Download databases

mkdir Mycobacterium_db
cd Mycobacterium_db
# download database for use with minimap2
URL="https://zenodo.org/record/8339941/files/Mycobacterium.rep.fna.gz"
wget "$URL"
IDS_URL="https://zenodo.org/record/8343322/files/mtb.ids"
wget "$IDS_URL"
# download kraken database
URL="https://zenodo.org/record/8339822/files/k2_Mycobacterium_20230817.tar.gz"
wget "$URL"
tar -xzf k2_Mycobacterium_20230817.tar.gz
rm k2_Mycobacterium_20230817.tar.gz

Classify reads

minimap2

# nanopore
minimap2 --secondary=no -c -t 4 -x map-ont -o reads.aln.paf Mycobacterium_db/Mycobacterium.rep.fna.gz reads.depleted.fq
# illumina
minimap2 --secondary=no -c -t 4 -x sr -o reads.aln.paf Mycobacterium_db/Mycobacterium.rep.fna.gz reads_1.depleted.fq reads_2.depleted.fq

kraken2

# nanopore
kraken2 --db Mycobacterium_db --threads 4 --report myco.kreport --output classifications.myco.tsv reads.depleted.fq
# illumina
kraken2 --db Mycobacterium_db --paired --threads 4 --report myco.kreport --output classifications.myco.tsv reads_1.depleted.fq reads_2.depleted.fq

Extract M. tuberculosis reads

minimap2

# nanopore
grep -Ff Mycobacterium_db/mtb.ids reads.aln.paf | cut -f1 | \
  seqkit grep -f - -o reads.enriched.fq reads.depleted.fq
# illumina
grep -Ff Mycobacterium_db/mtb.ids reads.aln.paf | cut -f1 > keep.ids
seqkit grep -f keep.ids -o reads_1.enriched.fq reads_1.depleted.fq
seqkit grep -f keep.ids -o reads_2.enriched.fq reads_2.depleted.fq

kraken2

We'll use the extract_kraken_reads.py script for this

# nanopore
python extract_kraken_reads.py -k classifications.myco.tsv -1 reads.depleted.fq -o reads.enriched.fq -t 1773 -r myco.kreport --include-children
# illumina
python extract_kraken_reads.py -k classifications.myco.tsv -1 reads_1.depleted.fq -2 reads_2.depleted.fq -o reads_1.enriched.fq -o2 reads_2.enriched.fq -t 1773 -r myco.kreport --include-children