Format

Send to

Choose Destination
See comment in PubMed Commons below
Sci Rep. 2015 May 19;5:9743. doi: 10.1038/srep09743.

MICCA: a complete and accurate software for taxonomic profiling of metagenomic data.

Author information

1
Fondazione Edmund Mach, Research and Innovation Centre, Computational Biology Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.
2
Fondazione Edmund Mach, Research and Innovation Centre, Food Quality Nutrition &Health Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.

Abstract

The introduction of high throughput sequencing technologies has triggered an increase of the number of studies in which the microbiota of environmental and human samples is characterized through the sequencing of selected marker genes. While experimental protocols have undergone a process of standardization that makes them accessible to a large community of scientist, standard and robust data analysis pipelines are still lacking. Here we introduce MICCA, a software pipeline for the processing of amplicon metagenomic datasets that efficiently combines quality filtering, clustering of Operational Taxonomic Units (OTUs), taxonomy assignment and phylogenetic tree inference. MICCA provides accurate results reaching a good compromise among modularity and usability. Moreover, we introduce a de-novo clustering algorithm specifically designed for the inference of Operational Taxonomic Units (OTUs). Tests on real and synthetic datasets shows that thanks to the optimized reads filtering process and to the new clustering algorithm, MICCA provides estimates of the number of OTUs and of other common ecological indices that are more accurate and robust than currently available pipelines. Analysis of public metagenomic datasets shows that the higher consistency of results improves our understanding of the structure of environmental and human associated microbial communities. MICCA is an open source project.

PMID:
25988396
PMCID:
PMC4649890
DOI:
10.1038/srep09743
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Nature Publishing Group Icon for PubMed Central
    Loading ...
    Support Center