MetaGPA

MetaGPA (Metagenomics Genome-Phenome Association) pipeline is used to link genetic information (e.g. genes encoding for enzymes) in metagenome with a dedicated functional phenotype (e.g. DNA cytosine modification). The main MetaGPA pipeline identifies protein family domains (Pfam, from Pfam-A database, http://pfam.xfam.org/) which are significantly associated with the studied phenotype. We also provide additional scripts for further analyses of any chosen interested Pfam identified from the main pipeline. For more details, please refer to our paper: A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA.

Usage

Clone the package:

git clone https://github.com/linyc74/MetaGPA.git

Run with default settings:

python MetaGPA \
-1 <control.1.fq> \
-2 <control.2.fq> \
-3 <case.1.fq> \
-4 <case.2.fq> \
-p <Pfam-A.hmm.gz>

Other options:

-l <minimum contig length> [default 1000]
-c <enrichment score cutoff> [default 3.0]

For more details, use help message:

python MetaGPA -h

Requirements

Executables in the environment variable PATH:

• SPAdes (3.15.3)
• cd-hit (4.8.1)
• hmmer (3.3.2)
• bowtie2 (2.4.1)
• samtools (1.11)
• bedtools (2.30.0)

Python 3 libraries:

• numpy (1.19.2)
• pandas (1.2.4)
• matplotlib (3.3.2)
• seaborn (0.11.0)
• scipy (1.5.2)
• statsmodels (0.12.0)
• argparse (1.1)

Versions shown in parentheses have been tested, but not required.

Resource Files

Pfam-A.hmm.gz can be downloaded from Release 33.0

Input Files

• Can be compressed .gz
• Fastq files need to be trimmed, e.g. by Cutadapt

Output Files

1. all_contigs.nd.fa all contigs assembled from case and control samples, redundant contigs removed
2. all_contigs_pfam_a.gtf hmm annotation file, containing 9 tab-delimited fields:

1   seqname     <contig_id>
2   source      .
3   feature     CDS
4   start       <start_bp>
5   end         <end_bp>
6   score       .
7   strand      +/-
8   frame       0
9   attribute   name "<query_name>";accession "<query_accession>";description "<query_description>";E_value "<E_value>"

3. contig_enrichment.csv table for contig enrichment scores, containing 8 comma-delimited fields:

1   contig_id      <contig_id>
2   start          <start_bp>
3   end            <end_bp>
4   control_counts <mapped reads number in control sample>
5   case_counts    <mapped reads number in case sample>
6   RPKM(Ctrl)     <Reads Per Kb per Million reads in control>
7   RPKM(Case)     <Reads Per Kb per Million reads in case>
8   enrichment     <RPKM(Case)/RPKM(Control)>

4. contig_enrichment.png stripplot of contig enrichment scores, group by control or case contigs
5. modified_contigs.fa modified contigs, i.e. enrichment score >= cutoff
6. unmodified_contigs.fa unmodified contigs, i.e. enrichment score < cutoff
7. modified_contigs_pfam_a.gtf annotation file of modified contigs
8. unmodified_contigs_pfam_a.gtf annotation file of unmodified contigs
9. pfam_a_statistics.csv statistics of pfam a domains, containing 6 comma-delimited fields:

1   pfam                      <pfam name>
2   counts_modified_contigs   <counts on modified contigs>
3   counts_unmodified_contigs <counts on unmodified contigs>
4   p_value                   <two sided fisher exact test p value>
5   Enriched/depleted         <enriched/depleted in modified contigs>
6   corrected_p_value         <bonferroni correction for multitest>

Docker

Pull Docker image

docker pull linyc74/metagpa:latest

Run container

docker run \
--volume "/PATH/TO/OUTDIR":"/PATH/TO/OUTDIR" \
--volume "/PATH/TO/INDIR":"/PATH/TO/INDIR" \
linyc74/metagpa:latest \
--control-fq-1 /PATH/TO/INDIR/control.1.fq.gz \
--control-fq-2 /PATH/TO/INDIR/control.2.fq.gz \
--case-fq-1 /PATH/TO/INDIR/case.1.fq.gz \
--case-fq-2 /PATH/TO/INDIR/case.2.fq.gz \
--pfam-hmm /PATH/TO/INDIR/Pfam-A.hmm.gz \
--min-contig-length 1000 \
--enrichment-cutoff 3.0 \
--threads 32 \
--memory 128 \
--outdir /PATH/TO/OUTDIR

Test

To test the MetaGPA main pipeline, a small dataset is provided in the test folder, including the following files:

• test_control.1.fq.gz
• test_control.2.fq.gz
• test_case.1.fq.gz
• test_case.2.fq.gz
• Pfam-A-sampled.hmm.gz

Publication

• Yang W, Lin Y-C, Johnson W, Dai N, Vaisvila R, Weigele P, Lee Y-J, Corrêa IR Jr, Schildkraut I & Ettwiller L (2021/11). A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA. eLife, 10:e70021.