Linked Life Data

10380193

Source:http://linkedlifedata.com/resource/pubmed/id/10380193

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
1999-8-10
pubmed:abstractText
We consider the problem of obtaining the maximum a posteriori probability (MAP) estimate of a consensus ancestral sequence for a set of DNA sequences. Our maximization method, called ASA (dnA Sequence Alignment), can be applied to the refinement of noisy regions of a DNA assembly, to the alignment of genomic functional sites, or to the alignment of any set of DNA sequences related by a star-like phylogeny. Along with the optimal consensus, ASA finds suboptimal solutions together with their relative probabilities. The probabilistic approach makes it possible to establish the limits to which an ancestor can in principle be recovered from diverged sequences. In simulations on rather short synthetic sequences (of length up to 80) with different coverage and error rates ranging from 5% to 30%, ASA restored the consensus from noisy observations essentially as best as is theoretically possible for the given error rates. We also illustrate the performance of ASA on the alignment of E.Coli promoters and the Alu-Sb subfamily of human repeat sequences. Since our model is a special case of a profile HMM, we give a comparison between these two approaches, as well as with other DNA alignment methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1793-5091
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
150-61
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
A probabilistic approach to consensus multiple alignment.
pubmed:affiliation
Department of Computer Science, University of California at Santa Cruz 95064, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't