Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-016-1378-x
Title: RiboTagger: Fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys
Authors: Xie C. 
Goi C.L.W. 
Huson D.H. 
Little P.F.R. 
Williams R.B.H. 
Keywords: Ecology
Microorganisms
Nucleic acids
RNA
Surveys
High sensitivity
High throughput
Microbial communities
Microbial ecology
Ribosomal RNA
Sequence analysis
Small subunits
Variable regions
Nucleic acid sequences
ribosome DNA
RNA 16S
RNA 18S
transcriptome
bacterium
biology
DNA sequence
genetics
high throughput sequencing
metagenome
metagenomics
procedures
software
Bacteria
Computational Biology
DNA, Ribosomal
High-Throughput Nucleotide Sequencing
Metagenome
Metagenomics
RNA, Ribosomal, 16S
RNA, Ribosomal, 18S
Sequence Analysis, DNA
Software
Transcriptome
Issue Date: 2016
Publisher: BioMed Central Ltd.
Citation: Xie C., Goi C.L.W., Huson D.H., Little P.F.R., Williams R.B.H. (2016). RiboTagger: Fast and unbiased 16S/18S profiling using whole community shotgun metagenomic or metatranscriptome surveys. BMC Bioinformatics 17 : 508. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-016-1378-x
Abstract: Background: Taxonomic profiling of microbial communities is often performed using small subunit ribosomal RNA (SSU) amplicon sequencing (16S or 18S), while environmental shotgun sequencing is often focused on functional analysis. Large shotgun datasets contain a significant number of SSU sequences and these can be exploited to perform an unbiased SSU--based taxonomic analysis. Results: Here we present a new program called RiboTagger that identifies and extracts taxonomically informative ribotags located in a specified variable region of the SSU gene in a high-throughput fashion. Conclusions: RiboTagger permits fast recovery of SSU-RNA sequences from shotgun nucleic acid surveys of complex microbial communities. The program targets all three domains of life, exhibits high sensitivity and specificity and is substantially faster than comparable programs. © 2016 The Author(s).
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/174241
ISSN: 14712105
DOI: 10.1186/s12859-016-1378-x
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