Linked Life Data

18155790

Source:http://linkedlifedata.com/resource/pubmed/id/18155790

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2008-1-18
pubmed:databankReference
pubmed:abstractText
Sequences in public databases may contain a number of sequencing errors. A double binomial model describing the distribution of indel-excluded similarity coefficients (S) among repeatedly sequenced 16S rRNA was previously developed and it produced a confidence interval of S useful for testing sequence identity among sequences of 400-bp length. We characterized patterns in sequencing errors found in nearly complete 16S rRNA sequences of Vibrionaceae as highly variable in reported sequence length and containing a small number of indels. To accommodate these characteristics, a simple binomial model for distribution of the similarity coefficient (H) that included indels was derived from the double binomial model for S. The model showed good fit to empirical data. By using either a pre-determined or bootstrapping estimated standard probability of base matching, we were able to use the exact binomial test to determine the relative level of sequencing error for a given pair of duplicated sequences. A limitation of the method is the requirement that duplicated sequences for the same template sequence be paired, but this can be overcome by using only conserved regions of 16S rRNA sequences and pairing a given sequence with its highest scoring BLAST search hit from the nr database of GenBank.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0167-7012
pubmed:author
pubmed:issnType
Print
pubmed:volume
72
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
166-79
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
A simple binomial test for estimating sequencing errors in public repository 16S rRNA sequences.
pubmed:affiliation
Center of Marine Biotechnology, University of Maryland Biotechnology Institute, 701 E. Pratt Street, Baltimore, MD 21202, USA,
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Evaluation Studies, Research Support, N.I.H., Extramural