Link to Pubmed [PMID] – 29040406
Bioinformatics 2018 02;34(4):585-591
Motivation: Advances in the sequencing of uncultured environmental samples, dubbed metagenomics, raise a growing need for accurate taxonomic assignment. Accurate identification of organisms present within a community is essential to understanding even the most elementary ecosystems. However, current high-throughput sequencing technologies generate short reads which partially cover full-length marker genes and this poses difficult bioinformatic challenges for taxonomy identification at high resolution.
Results: We designed MATAM, a software dedicated to the fast and accurate targeted assembly of short reads sequenced from a genomic marker of interest. The method implements a stepwise process based on construction and analysis of a read overlap graph. It is applied to the assembly of 16S rRNA markers and is validated on simulated, synthetic and genuine metagenomes. We show that MATAM outperforms other available methods in terms of low error rates and recovered fractions and is suitable to provide improved assemblies for precise taxonomic assignments.
Availability and implementation: https://github.com/bonsai-team/matam.
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Supplementary information: Supplementary data are available at Bioinformatics online.https://www.ncbi.nlm.nih.gov/pubmed/29040406