Giter Site home page Giter Site logo

fischuu / concoct Goto Github PK

View Code? Open in Web Editor NEW

This project forked from binpro/concoct

0.0 0.0 0.0 21.22 MB

Clustering cONtigs with COverage and ComposiTion

License: Other

Dockerfile 0.44% Makefile 0.58% Python 62.27% C 19.44% R 2.50% Perl 9.19% Shell 5.57%

concoct's Introduction

CONCOCT 1.0.0 Build Status

A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.

Please Cite

If you use CONCOCT in your publication, please cite:

Johannes Alneberg, Brynjar Smári Bjarnason, Ino de Bruijn, Melanie Schirmer, Joshua Quick, Umer Z Ijaz, Leo Lahti, Nicholas J Loman, Anders F Andersson & Christopher Quince. 2014. Binning metagenomic contigs by coverage and composition. Nature Methods, doi: 10.1038/nmeth.3103

Documentation

A comprehensive documentation for concoct is hosted on readthedocs.

Basic Usage

Cut contigs into smaller parts

cut_up_fasta.py original_contigs.fa -c 10000 -o 0 --merge_last -b contigs_10K.bed > contigs_10K.fa

Generate table with coverage depth information per sample and subcontig. This step assumes the directory 'mapping' contains sorted and indexed bam files where each sample has been mapped against the original contigs.

concoct_coverage_table.py contigs_10K.bed mapping/Sample*.sorted.bam > coverage_table.tsv

Run concoct

concoct --composition_file contigs_10K.fa --coverage_file coverage_table.tsv -b concoct_output/

Merge subcontig clustering into original contig clustering

merge_cutup_clustering.py concoct_output/clustering_gt1000.csv > concoct_output/clustering_merged.csv

Extract bins as individual FASTA

mkdir concoct_output/fasta_bins
extract_fasta_bins.py original_contigs.fa concoct_output/clustering_merged.csv --output_path concoct_output/fasta_bins

Support

Gitter If you are having trouble running CONCOCT or interpretting any results, please don't hesitate to write a question in our gitter channel.

Contribute

Recommend Projects

  • React photo React

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

  • Vue.js photo Vue.js

    🖖 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. 📊📈🎉

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 ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.