genome-catalogue-pipeline

Introduction

genome-catalogue-pipelines is an automated data analysis workflow designed for large-scale metagenomic species and functional analysis on high-performance computing clusters.

Pipeline summary

This pipeline is implemented in Nextflow. The core functions and steps are as follows:

• Imports genome data from public databases (SPIRE, Progenomes3, Mgnify, etc.) or assembled MAGs and isolate genomes.
• Performs quality control using CheckM2, filtering out genomes with high or medium quality.
• Removes redundancy by applying the dRep with MASH algorithm at 99.9% ANI threshold, constructing the genome catalogue.
• Cluter to species clusters (SGBs) using dRep's fastANI algorithm at 95% ANI threshold and selects representative genomes (SRs) based on genome completeness, contamination, N50, and genome type.
• Builds a protein catalogue by annotating genomes using Prokka and clustering protein sequences using MMseqs2 at 50%, 90%, and 100% amino acid identity. Protein domains are annotated with InterProScan and gene functions with eggNOG-mapper.
• Performs phylogenetic analysis using GTDB-Tk for taxonomy assignment and IQ-TREE2 for phylogenetic tree construction of representative genomes (SRs).

Note

When performing quality control on the obtained genomes, make sure to retain high-quality or medium-quality genomes by specifying the --quality_filter before running the workflow on actual data. High-quality :completeness > 90%, contamination < 5%;medium-quality:completeness > 50%, contamination < 5%, and QS > 50. (QS = % completeness - 5 * % contamination).

Note

If you only want to get species clusters (SGBs) ,representative genomes,add --skip_annotation parameter,default run annotation

Data preparation

Input genomes specifications

The input data can be passed to this pipeline using the --input_genomes parameter ,and you need to prepare all your genomes into a directory and make sure all extension with ".fna.gz" .

Samplesheet input file

You need to provide a TSV samplesheet input file using the --input_genomes_metadata parameter and TSV with atleast four columns:genome,type(MAG or isolate), completeness ,contamination . However, if the quality statistics for the genomes is unavailable, you can add --run_checkm2 to run CheckM2 and generate the related information columns.

Additionally, you will need the following information to run the pipeline:

• prefix genome name (for example, BIFIDO)
• catalogue version (for example, 1.0)
• min and max accession number to be assigned to the genomes . Max - Min = #total number of genomes
• save genomes quality level (medium or high quality)
• drep cluster method (for example, fastANI)

Note

There are several dRep supported clustering algorithms. Please refer to Overview of genome comparison algorithms on clustering algorithms. Make sure to choose cluster method with --drepcluster_method before running the workflow on actual data.

Usage

The pipeline is built in Nextflow, and utilized docker container to run the software. The typical command for running the pipeline is as follows:

nextflow run genome-catalogue-pipeline/main.nf -profile docker -c custom.config  \
--input_genomes input_genomes \
--input_genomes_metadata metadata.tsv \
--outdir /mnt/chenwen/02.program/check_genome_catalog_pipeline/01.results \
--start_number 1 \
--end_number 10000 \
--version 1.0 \
--genome_prefix BIFIDO \
--drepcluster_method fastANI \
--quality_filter medium \

Note

If you need to perform re-clustering on the resulting species clusters, add --run_recluster parameter . This is because dRep may assign some genome pairs with an ANI greater than the threshold end up in different clusters. The re-clustering process will allow you to recluster and choose new representative genomes.

Warning

For the subsequent update-catalogue pipeline, you can choose to skip phylogenetic analysis by adding --skip_gtdb and --skip_tree parameters, as not every update results in new species or replaces representative genomes.

Tools used in the pipeline

Tool/Database	Version	Purpose
CheckM2	1.0.1	Determining genome quality
dRep	3.2.2	Genome clustering
Mash	2.3	Sketch for the catalogue; placement of genomes into clusters (update only); strain tree
GTDB-Tk	2.4.0	Assigning taxonomy; generating alignments
GTDB	r220	Database for GTDB-Tk
Prokka	1.14.6	Protein annotation
IQ-TREE 2	2.2.0.3	Generating a phylogenetic tree
MMseqs2	13.45111	Generating a protein catalogue
eggNOG-mapper	2.1.11	Protein annotation (eggNOG, KEGG, COG, CAZy)
eggNOG DB	5.0.2	Database for eggNOG-mapper
Diamond	2.0.11	Protein annotation (eggNOG)
InterProScan	5.62-94.0	Protein annotation (InterPro, Pfam)
run_dbCAN	4.1.2	Polysaccharide utilization loci prediction
dbCAN DB	V12	Database for run_dbCAN