MOCAT: a metagenomics assembly and gene prediction toolkit

Research output: Contribution to journalJournal articleResearchpeer-review

  • Jens Roat Kultima
  • Shinichi Sunagawa
  • Junhua Li
  • Weineng Chen
  • Hua Chen
  • Daniel R. Mende
  • Arumugam, Mani
  • Qi Pan
  • Binghang Liu
  • Junjie Qin
  • Jun Wang
  • Peer Bork
MOCAT is a highly configurable, modular pipeline for fast, standardized processing of single or paired-end sequencing data generated by the Illumina platform. The pipeline uses state-of-the-art programs to quality control, map, and assemble reads from metagenomic samples sequenced at a depth of several billion base pairs, and predict protein-coding genes on assembled metagenomes. Mapping against reference databases allows for read extraction or removal, as well as abundance calculations. Relevant statistics for each processing step can be summarized into multi-sheet Excel documents and queryable SQL databases. MOCAT runs on UNIX machines and integrates seamlessly with the SGE and PBS queuing systems, commonly used to process large datasets. The open source code and modular architecture allow users to modify or exchange the programs that are utilized in the various processing steps. Individual processing steps and parameters were benchmarked and tested on artificial, real, and simulated metagenomes resulting in an improvement of selected quality metrics. MOCAT can be freely downloaded at http://www.bork.embl.de/mocat/.
Original languageEnglish
JournalPLOS ONE
Volume7
Issue number10
Pages (from-to)e47656
ISSN1932-6203
DOIs
Publication statusPublished - 2012
Externally publishedYes

    Research areas

  • Computational Biology, Computer Simulation, Databases, Genetic, Gastrointestinal Tract, Genes, Humans, Metagenome, Metagenomics, Reference Standards, Sequence Analysis, DNA, Software, Statistics as Topic

ID: 101041674