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 |
Appears in Collections: | Elements Staff Publications |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
10_1186_s12859-016-1378-x.pdf | 771 kB | Adobe PDF | OPEN | None | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.