Tentacle: distributed quantification of genes in metagenomes
Journal article, 2015

Background In metagenomics, microbial communities are sequenced at increasingly high resolution, generating datasets with billions of DNA fragments. Novel methods that can efficiently process the growing volumes of sequence data are necessary for the accurate analysis and interpretation of existing and upcoming metagenomes. Findings Here we present Tentacle, which is a novel framework that uses distributed computational resources for gene quantification in metagenomes. Tentacle is implemented using a dynamic master-worker approach in which DNA fragments are streamed via a network and processed in parallel on worker nodes. Tentacle is modular, extensible, and comes with support for six commonly used sequence aligners. It is easy to adapt Tentacle to different applications in metagenomics and easy to integrate into existing workflows. Conclusions Evaluations show that Tentacle scales very well with increasing computing resources. We illustrate the versatility of Tentacle on three different use cases. Tentacle is written for Linux in Python 2.7 and is published as open source under the GNU General Public License (v3). Documentation, tutorials, installation instructions, and the source code are freely available online at: http://bioinformatics.math.chalmers.se/tentacle.

DNA sequence analysis

Master-worker

Gene quantification

Distributed computing

Read mapping

Metagenomics

Next-generation sequencing

DNA sequencing

Author

Fredrik Boulund

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Anders Sjögren

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Erik Kristiansson

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

GigaScience

2047-217X (eISSN)

Vol. 4 1 artikel nr 40- 40

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Bioinformatics (Computational Biology)

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1186/s13742-015-0078-1

PubMed

26351566

More information

Created

10/7/2017