https://github.com/mbhall88/classification_benchmark
Benchmarking different ways of doing read (taxonomic) classification, with a focus on removal of contamination and MTB classification
https://github.com/mbhall88/classification_benchmark
bioinformatics contamination sequence-analysis taxonomic-classification tuberculosis
Last synced: 2 months ago
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Benchmarking different ways of doing read (taxonomic) classification, with a focus on removal of contamination and MTB classification
- Host: GitHub
- URL: https://github.com/mbhall88/classification_benchmark
- Owner: mbhall88
- License: mit
- Created: 2023-05-22T05:25:56.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-08T00:42:39.000Z (about 1 year ago)
- Last Synced: 2025-04-06T15:53:18.137Z (2 months ago)
- Topics: bioinformatics, contamination, sequence-analysis, taxonomic-classification, tuberculosis
- Language: Python
- Homepage: https://doi.org/10.1093/gigascience/giae010
- Size: 1.15 MB
- Stars: 12
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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.
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][doi], along with accompanying configuration
files.
Custom databases have all been uploaded to Zenodo, along with the simulated reads:
- Nanopore simulated metagenomic reads -
- Illumina simulated metagenomic reads -
- Nanopore and Illumina artificial real reads -
- Kraken2 database built from the Human Pangenome Reference Consortium
genomes -
- Kraken2 database built from the kraken2 Human
library -
- Kraken2 database built from a *Mycobacterium* representative set of
genomes -
- A (fasta) database of representative genomes from the *Mycobacterium*
genus -
- A (fasta) database of *M. tuberculosis* genomes from a variety of
lineages -
- The fasta file built from the [Clockwork](https://github.com/iqbal-lab-org/clockwork)
decontamination pipeline -
## 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`](https://github.com/mbhall88/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](https://github.com/DerrickWood/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](https://github.com/shenwei356/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 $2}' classifications.tsv > ids.txt
seqkit grep --id-regexp '^(\S+)/[12]' -f ids.txt -o reads_1.depleted.fq reads_1.fq
seqkit grep --id-regexp '^(\S+)/[12]' -f ids.txt -o reads_2.depleted.fq reads_2.fq
```
### *M. tuberculosis* classification/enrichment
For this step we recommend either [minimap2](https://github.com/lh3/minimap2) or kraken2
with a *Mycobacterium* genus database. We leave it to the user to decide which approach
they prefer based on the results in our manuscript.
#### Download databases
```
mkdir Mycobacterium_db
cd Mycobacterium_db
# download database for use with minimap2
URL="https://zenodo.org/record/8339941/files/Mycobacterium.rep.fna.gz"
wget "$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](https://github.com/jenniferlu717/KrakenTools#extract_kraken_readspy)
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
```
[doi]: https://doi.org/10.1093/gigascience/giae010