Title |
A Platform-Independent Method for Detecting Errors in Metagenomic Sequencing Data: DRISEE
|
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Published in |
PLoS Computational Biology, June 2012
|
DOI | 10.1371/journal.pcbi.1002541 |
Pubmed ID | |
Authors |
Kevin P. Keegan, William L. Trimble, Jared Wilkening, Andreas Wilke, Travis Harrison, Mark D'Souza, Folker Meyer |
Abstract |
We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 20% |
Germany | 1 | 10% |
Belgium | 1 | 10% |
Cameroon | 1 | 10% |
United Kingdom | 1 | 10% |
Unknown | 4 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 60% |
Members of the public | 3 | 30% |
Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 15 | 8% |
Brazil | 3 | 2% |
United Kingdom | 2 | 1% |
Canada | 2 | 1% |
Australia | 1 | <1% |
Israel | 1 | <1% |
Sweden | 1 | <1% |
Germany | 1 | <1% |
Egypt | 1 | <1% |
Other | 7 | 4% |
Unknown | 148 | 81% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 59 | 32% |
Student > Ph. D. Student | 45 | 25% |
Student > Master | 15 | 8% |
Student > Bachelor | 12 | 7% |
Professor | 12 | 7% |
Other | 27 | 15% |
Unknown | 12 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 102 | 56% |
Biochemistry, Genetics and Molecular Biology | 21 | 12% |
Computer Science | 12 | 7% |
Environmental Science | 8 | 4% |
Engineering | 7 | 4% |
Other | 11 | 6% |
Unknown | 21 | 12% |