Journal article
Robust computational analysis of rRNA hypervariable tag datasets
PloS one, Vol.5(12), pp.e15220-e15220
12/31/2010
Handle:
https://hdl.handle.net/2376/103229
PMCID: PMC3013109
PMID: 21217830
Abstract
Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.
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Details
- Title
- Robust computational analysis of rRNA hypervariable tag datasets
- Creators
- Maksim Sipos - Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of AmericaPatricio JeraldoNicholas ChiaAni QuA Singh DhillonMichael E KonkelKaren E NelsonBryan A WhiteNigel Goldenfeld
- Publication Details
- PloS one, Vol.5(12), pp.e15220-e15220
- Academic Unit
- Molecular Biosciences, School of; Washington Animal Disease Diagnostic Laboratory
- Publisher
- United States
- Identifiers
- 99900546535801842
- Language
- English
- Resource Type
- Journal article