iqKM (Identification and Quantification of KEGG Modules)

iqKM is an easy to use pipeline to assign and/or quantify KEGG Orthology (KO) and KEGG modules (KMs) in metagenome/genome.

iqKM -i genome.fna -o out_dir --help_dir help_dir
iqKM -i metagenome.fna -o out_dir --help_dir help_dir --fq raw_reads.fastq(.gz) --meta --quantify
iqKM -h

Detailed pipeline walkthrough

Installation

iqKM is a command line tool developed for Linux and macOS and is available to install from github, bioconda or pypi.

Install via conda (recommended)

Installing iqKM via conda will automatically install all dependencies.

• Step 1: Create the iqKM environment

conda create -n iqKM -c bioconda iqkm

• Step 2: Download Kofam HMM db and help files

conda activate iqKM

# download help_dir, which contains Kofam HMM db and other help_files
# go to our ftp site and download help_dir.zip
wget https://drive.google.com/u/0/uc?export=download&confirm=H3_U&id=1_Kxhox_hqrs7c_fVD8LC8mbwf4vp0ehX
unzip help_dir && cd help_dir
pwd
# /path/to/help_dir
# now you can use above path as --help_dir /path/to/help_dir when running iqkm

Install via pip

• Step 1: Install third-party dependencies

Before installing iqKM using pip, make sure the following softwares are on the system path, they are all easy-to-install tools.

Software	Version
HMMER	>=3.1
Prodigal	>=2.6.3
bwa	>= 0.7.17
samtools	>= 1.3.1

• Step 2: Install iqKM

pip install iqkm

• Step 3: Download Kofam HMM db and help files

# download help_dir, which contains Kofam HMM db and other help_files
# go to our ftp site and download help_dir.zip
wget https://drive.google.com/u/0/uc?export=download&confirm=H3_U&id=1_Kxhox_hqrs7c_fVD8LC8mbwf4vp0ehX
unzip help_dir && cd help_dir
pwd
# /path/to/help_dir
# now you can use above path as --help_dir /path/to/help_dir when running iqkm

Install from github

• Step 1: Install third-party dependencies

Before installing iqKM, make sure the following softwares are on the system path, they are all easy-to-install tools.

Software	Version
HMMER	>=3.1
Prodigal	>=2.6.3
bwa	>= 0.7.17
samtools	>= 1.3.1

• Step 2: Clone the repo and install

git clone https://github.com/lijingdi/iqKM.git
cd /path/to/iqKM
python3 setup.py install

• Step 3: Download Kofam HMM db and help files

# go to our ftp site https://drive.google.com/u/0/uc?export=download&confirm=H3_U&id=1_Kxhox_hqrs7c_fVD8LC8mbwf4vp0ehX and download help_dir.zip
unzip help_dir && cd help_dir
pwd
# /path/to/help_dir
# now you can use above path as --help_dir /path/to/help_dir when running iqkm

Usage

Basic usage

• KMs assignment for individual genomes

iqKM -i genome.fna -o out_dir --help_dir help_dir

• KMs assignment and quantification for individual genomes

iqKM -i genome.fna -o out_dir --help_dir help_dir --fq raw_reads_1.fastq(.gz) --rq raw_reads_2.fastq(.gz) --quantify

• KMs assignment for metagenomes

iqKM -i metagenome.fna -o out_dir --help_dir help_dir --meta

• KMs assignment and quantification for metagenomes

iqKM -i metagenome.fna -o out_dir --help_dir help_dir --fq raw_reads_1.fastq(.gz) --rq raw_reads_2.fastq(.gz) --meta --quantify

Arguments

iqKM -h

iqkm -i input_genome -o out_dir [--fq fastq_1.gz] [--rq fastq_2.gz] [--prefix PREFIX] [--db HMMdb] [--com float] [--skip] [--quantify] [--meta] [-w] [-n int] [-f] [-d] [-g file]

Required arguments
-i, --input	input genome/metagenome
-o, --out_dir	output folder
--help_dir	Folder containing Kofam HMM database and essential help files, refer to install to download

Optional arguments
--fq	input first/single read file, fastq(.gz), only required when '--quantify' is specified
--rq	input reverse read file, fastq(.gz), only required when '--quantify' is specified
--prefix	prefix of output files, default: input genome filename without postfix
--db	Your customised Kofam HMM database, default=None
--com	KM completeness threshold (%) on contig basis, default=66.67
--skip	Force skipping steps if output files exist, default=False
-q, --quantify	Run both KM assignment and quantification, default=False
-m, --meta	Run in metagenome mode, default=False
-w,--include_weights	Enable normalizing KM abundance using KO weights, default=True
-n, --threads	Number of threads used for computation, default=1
-f, --force	Force rerunning the whole pipeline, don't resume previous run, default=False
-d, --dist	Apply KM minimum distance threshold, default=True
-g,--genome_equivalent	Genome equivalent output generated from microbe-census, can be used for library-size normalization, optional

Files output

• output
  • prodigal(intermediate output files)
    • [prefix].cds
    • [prefix].pep
    • [prefix].gff
    • [prefix].cds.bwa_index (only when '--quantify' is specified)
  • hmmsearch (intermediate output files)
    • [prefix]_hmmsearch.log
    • [prefix]_hmmsearch.tbl
  • KO_parsing (intermediate output files)
    • [prefix].ko
  • KM_assignment_unfiltered (intermediate output files)
    • [prefix].summary.kegg_contigs.tsv
    • [prefix].summary.kegg_pathways.tsv
  • KM_assignment_filtered (KM assignment output)
    • [prefix]_km_on_contig.tsv
    • [prefix]_km_sample_count.tsv
  • out_remap (intermediate output files, only when '--quantify' is specified)
    • [prefix]_remapping.log
    • [prefix]_unique.tab
  • out_abundance (KM abundance output, only when '--quantify' is specified)
    • km_abd_contig
      • [prefix]_km_contig_abd.tsv
    • km_abd_sample
      • [prefix]_km_sample_abd.tsv
    • ko_abd
      • [prefix]_ko_abd.tsv

Acknowledgements

Author of pipeline: Jingdi Li

Principal Investigators: Rob Finn

If you find any errors or bugs, please do not hesitate to contact lijingdioo@outlook.com or open a new Issue thread on this github page, we will get back to you as soon as possible.