Compareads: comparing huge metagenomic experiments.

ABSTRACT:

Background

Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.

Methods

This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.

R

SUBMITTER: Maillet N 

PROVIDER: S-EPMC3526429 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature