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Read e-book online Biological Sequence Analysis: Probabilistic Models of PDF

By Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison

ISBN-10: 0521629713

ISBN-13: 9780521629713

Probablistic types have gotten more and more very important in studying the massive volume of information being produced through large-scale DNA-sequencing efforts comparable to the Human Genome venture. for instance, hidden Markov types are used for examining organic sequences, linguistic-grammar-based probabilistic types for choosing RNA secondary constitution, and probabilistic evolutionary versions for inferring phylogenies of sequences from diverse organisms. This e-book offers a unified, up to date and self-contained account, with a Bayesian slant, of such equipment, and extra as a rule to probabilistic tools of series research. Written through an interdisciplinary staff of authors, it truly is available to molecular biologists, computing device scientists, and mathematicians with out formal wisdom of the opposite fields, and whilst offers the state-of-the-art during this new and demanding box.

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Additional resources for Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

Example text

There is another widely used approach for finding multiple matches due to Waterman & Eggert [1987], which will be described in Chapter 4. Let us assume that we are only interested in matches scoring higher than some threshold T . This will be true in general, because there are always short local alignments with small positive scores even between entirely unrelated sequences. Let y be the sequence containing the domain or motif, and x be the sequence in which we are looking for multiple matches.

There are two difficulties with this simple approach. The first is that of obtaining a good random sample of confirmed alignments. Alignments tend not to be independent from each other because protein sequences come in families. The second is more subtle. In truth, different pairs of sequences have diverged by different amounts. When two sequences have diverged from a common ancestor very recently, we expect many of their residues to be identical. The probability pab for a = b should be small, and hence s(a, b) should be strongly negative unless a = b.

These can be very fast, but they make additional assumptions and will miss the best match for some sequence pairs. We will briefly discuss a few approaches to heuristic searching later in the chapter. Because we introduced the scoring scheme as a log-odds ratio, better alignments will have higher scores, and so we want to maximise the score to find the optimal alignment. Sometimes scores are assigned by other means and interpreted as costs or edit distances, in which case we would seek to minimise the cost of an alignment.

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Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison

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