UCHIME improves sensitivity and speed of chimera detection

Edgar, Robert C., Haas, Brian J., Clemente, Jose C., Quince, Christopher and Knight, Rob (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27 (16). pp. 2194-2200. ISSN 1367-4803

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Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer.

Item Type: Article
Additional Information: Funding Information: Funding: National Institutes of Health (grant U54-HG004969 to B.J.H.); Engineering and Physical Sciences Research Council Career Acceleration Fellowship (EP/H003851/1 to C.Q.); National Institutes of Health (grant HG004872 and HHMI to R.K., in part).
Uncontrolled Keywords: statistics and probability,biochemistry,molecular biology,computer science applications,computational theory and mathematics,computational mathematics ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School: Faculty of Science > School of Biological Sciences
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Depositing User: LivePure Connector
Date Deposited: 12 Sep 2022 10:32
Last Modified: 20 Oct 2022 19:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/88117
DOI: 10.1093/bioinformatics/btr381

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