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Quantitative Biology > Genomics

arXiv:q-bio/0512019 (q-bio)
[Submitted on 8 Dec 2005]

Title:Parametric inference of recombination in HIV genomes

Authors:Niko Beerenwinkel, Colin N. Dewey, Kevin M. Woods
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Abstract: Recombination is an important event in the evolution of HIV. It affects the global spread of the pandemic as well as evolutionary escape from host immune response and from drug therapy within single patients. Comprehensive computational methods are needed for detecting recombinant sequences in large databases, and for inferring the parental sequences.
We present a hidden Markov model to annotate a query sequence as a recombinant of a given set of aligned sequences. Parametric inference is used to determine all optimal annotations for all parameters of the model. We show that the inferred annotations recover most features of established hand-curated annotations. Thus, parametric analysis of the hidden Markov model is feasible for HIV full-length genomes, and it improves the detection and annotation of recombinant forms.
All computational results, reference alignments, and C++ source code are available at this http URL.
Comments: 20 pages, 5 figures
Subjects: Genomics (q-bio.GN); Quantitative Methods (q-bio.QM)
Cite as: arXiv:q-bio/0512019 [q-bio.GN]
  (or arXiv:q-bio/0512019v1 [q-bio.GN] for this version)
  https://doi.org/10.48550/arXiv.q-bio/0512019

Submission history

From: Kevin Woods [view email]
[v1] Thu, 8 Dec 2005 22:54:27 UTC (98 KB)
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