Giter Club home page Giter Club logo

treangenlab / komb Goto Github PK

View Code? Open in Web Editor NEW
5.0 2.0 0.0 247.43 MB

KOMB is a tool for fast identification of unitigs of interest in metagenomes. KOMB introduces the concept of a Hybrid Unitig Graph (an extension to compacted de Bruijn graphs) and relies on k-core and K-truss decomposition algorithms.

License: GNU General Public License v2.0

Makefile 0.45% Shell 0.04% M4 0.14% C++ 79.86% Python 19.51%
anomaly-detection k-core-decomposition metagenomic-analysis metagenomic-pipeline metagenomics short-read-sequencing

komb's Introduction

KOMB Logo

KOMB

Welcome to the KOMB repo! Get ready to KOMB through your (large) metagenomes to find regions of biological (functional or taxonomic) interest!

KOMB version: 2.0.0

KOMB installation

Current version of KOMB has to be installed from source. It has only been tested on Linux systems.

In order to install KOMB you will need to install several dependencies first. We recommend using conda (Miniconda download) for managing KOMB dependencies. Below is an example of installing required tools with conda.

  1. Create a new conda environment and make sure that conda-forge and bioconda channels are enabled.
conda create --name komb-env python=3.9
conda config --add channels conda-forge
conda config --add channels bioconda
  1. Install dependencies available through conda
conda install bwa-mem2
conda install seqkit
conda install igraph>=0.10.0

Note: we have upgraded KOMB to be compatible with newer version of the igraph library which means that the versions 0.8.3 and older no longer will work. We hope that igraph API will stay consitent going forward, but we have no way to ensure that. 3. Install rustup and nightly toolchain and build ggcat from source

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup toolchain install nightly
git clone https://github.com/algbio/ggcat --recursive
cd ggcat/
cargo install --path crates/cmdline/ --locked
  1. Clone the repo and build the source code
git clone https://gitlab.com/treangenlab/komb.git.
cd komb 
./autogen.sh
./configure
make; make install
  1. Now you will have a komb2 executable in the komb/bin directory and you should be able to run KOMB.py in order to run the complete KOMB pipeline.

Quickstart example

You can test KOMB by running the following command:
KOMB.py -i example_data/reads1.fastq -j example_data/reads2.fastq -k 51

KOMB usage

Full set of parameters available in the KOMB pipeline is shown below and can be accessed by running python KOMB.py --help.

usage: KOMB.py [-h] -i INPUT_READS1 -j INPUT_READS2 [-o OUTPUT_DIR] [--keep-alignments] [-e LOG_FILE] [--overwrite] -k KMER_SIZE [-t NUM_THREADS] [-l MIN_UNITIG_LENGTH] [-v VERBOSITY] [-c MIN_COUNT]
               [-m GGCAT_MEMORY] [--eulertigs | --greedy-matchtigs | --pathtigs] [--min-seed-length MIN_SEED_LENGTH]

KOMB Analysis Pipeline example: python KOMB.py -i <read1.fq> -j <read2.fq> -k <k-mer size> -t <threads>

optional arguments:
  -h, --help            show this help message and exit
  --eulertigs           Generate Eulertigs instead of unitigs
  --greedy-matchtigs    Generate greedy matchtigs instead of unitigs
  --pathtigs            Generate pathtigs instead of unitigs

Input/Output:
  -i INPUT_READS1, --input-reads1 INPUT_READS1
                        Path to the first sequencing reads file for paired-end data in FASTQ format
  -j INPUT_READS2, --input-reads2 INPUT_READS2
                        Path to the second sequencing reads file for paired-end data in FASTQ format
  -o OUTPUT_DIR, --output-dir OUTPUT_DIR
                        Path to the first sequencing reads file for paired-end data in FASTQ format
  --keep-alignments     Keep SAM files after the graph has been constructed (might require a lot of disk space)
  -e LOG_FILE, --log-file LOG_FILE
                        File for logging [default: stdout]
  --overwrite           Delete <output-dir> and create a new one in case it exists

Common arguments:
  -k KMER_SIZE, --kmer-size KMER_SIZE
                        k-mer size used for the *tig construction and subsequent analyses
                        use -1 to let KOMB automatically pick a value [default: -1]
  -t NUM_THREADS, --num-threads NUM_THREADS
                        Maximum number of threads you want programs to use, note that some might use less than the amount specified
  -l MIN_UNITIG_LENGTH, --min-unitig-length MIN_UNITIG_LENGTH
                        Minimum length of a unitig to be kept for the analysis.
                        Value -1 indicates setting this to the read length [default]
                        Value 0 would result in keeping all unitigs, and values > 0 will apply the filter
  -v VERBOSITY, --verbosity VERBOSITY
                        Logging level: 0 (DEBUG), 1 (INFO), 2 (ERROR)

GGCAT *tig construction:
  -c MIN_COUNT, --min-count MIN_COUNT
                        Minimum count required to keep a kmer [default: 2]
  -m GGCAT_MEMORY, --ggcat-memory GGCAT_MEMORY
                        Maximum memory usage for GGCAT (GB) [default: 8]

BWA MEM parameters:
  --min-seed-length MIN_SEED_LENGTH
                        Minimum seed length. Matches shorter than the value will be missed.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.