Book Chapter10.1016/S0076-6879(96)66029-7
Local alignment statistics.
Stephen F. Altschul,Warren Gish +1 more
934
TL;DR: In this paper, the authors discuss the study of local alignment statistics, the distribution of optimal gapped subalignment scores, and the evidence that two parameters are sufficient to describe both the form of this distribution and its dependence on sequence length.
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Abstract: Publisher Summary This chapter discusses the study of local alignment statistics, the distribution of optimal gapped subalignment scores, and the evidence that two parameters are sufficient to describe both the form of this distribution and its dependence on sequence length. Using a random protein model, the relevant statistical parameters are calculated for a variety of substitution matrices and gap costs. An analysis of these parameters elucidates the relative effectiveness of affine as opposed to length-proportional gap costs. Thus, sum statistics provide a method for evaluating sequence similarity that treats short and long gaps differently. By example, the chapter shows how this method has the potential to increase search sensitivity. The statistics described can be applied to the results of fast alignment (FASTA) searches or to those from a variation of the basic local alignment search tool (BLAST) programs.
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References
Basic Local Alignment Search Tool
TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
98.8K
A general method applicable to the search for similarities in the amino acid sequence of two proteins
TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
13.2K
Identification of common molecular subsequences.
TL;DR: This letter extends the heuristic homology algorithm of Needleman & Wunsch (1970) to find a pair of segments, one from each of two long sequences, such that there is no other Pair of segments with greater similarity (homology).
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•Book
Atlas of protein sequence and structure
M. A. Chang,M. O. Dayhoff,R. V. Eck,M. R. Sochard +3 more
- 01 Jan 1965