CS-BLAST

CS-BLAST (Context-Specific BLAST) is a tool that searches a protein sequence that extends BLAST (Basic Local Alignment Search Tool), using context-specific mutation probabilities. More specifically, CS-BLAST derives context-specific amino-acid similarities on each query sequence from short windows on the query sequences. Using CS-BLAST doubles sensitivity and significantly improves alignment quality without a loss of speed in comparison to BLAST. CSI-BLAST (Context-Specific Iterated BLAST) is the context-specific analog of PSI-BLAST (Position-Specific Iterated BLAST), which computes the mutation profile with substitution probabilities and mixes it with the query profile. CSI-BLAST (Context-Specific Iterated BLAST) is the context specific analog of PSI-BLAST (Position-Specific Iterated BLAST). Both of these programs are available as web-server and are available for free download.

CS-BLAST (Context-Specific BLAST) is a tool that searches a protein sequence that extends BLAST (Basic Local Alignment Search Tool), using context-specific mutation probabilities. More specifically, CS-BLAST derives context-specific amino-acid similarities on each query sequence from short windows on the query sequences. Using CS-BLAST doubles sensitivity and significantly improves alignment quality without a loss of speed in comparison to BLAST. CSI-BLAST (Context-Specific Iterated BLAST) is the context-specific analog of PSI-BLAST (Position-Specific Iterated BLAST), which computes the mutation profile with substitution probabilities and mixes it with the query profile. CSI-BLAST (Context-Specific Iterated BLAST) is the context specific analog of PSI-BLAST (Position-Specific Iterated BLAST). Both of these programs are available as web-server and are available for free download. S(a,b) = const \times \log \left ( \frac{P(a|b)}{P(a)} \right ) where P(a|b) denotes the probability of amino acid a mutating into amino acid b [2]. In a large set of sequence alignments, counting the number of amino acids as well as the number of aligned pairs (a, b) will allow you to derive the probabilities P(a|b) and P(a) . Since protein sequences need to maintain a stable structure, a residue’s substitution probabilities are largely determined by the structural context of where it is found. As a result, substitution matrices are trained for structural contexts. Since context information is encoded in transition probabilities between states, mixing mutation probabilities from substitution matrices weighted for corresponding states achieves improved alignment qualities when compared to standard substitution matrices. CS-BLAST improves further upon this concept. The figure illustrates the sequence to sequence and profile to sequence equivalence with the alignment matrix. The query profile results from the artificial mutations in which the bar heights are proportional to the corresponding amino acid probabilities. (A FIGURE NEEDS TO GO HERE THIS IS THE CAPTION) “Sequence search/alignment algorithms find the path that maximizes the sum of similarity scores (color-coded blue to red). Substitution matrix scores are equivalent to profile scores if the sequence profile (colored histogram) is generated from the query sequence by adding artificial mutations with the substitution matrix pseudocount scheme. Histogram bar heights represent the fraction of amino acids in profile columns”. Given an observed variable x and a target variable y , a generative model defines the probabilities P(x, y) and P(y) separately. In order to predict the unobserved target variable, y , Bayes’ theorem, P(y|x) = \left ( \frac{P(x|y)P(y)}{[\textstyle \sum_{y}P(x|y)P(y) \displaystyle]} \right ) is used. A generative model, as the name suggests, allows one to generate new data points (x, y) . The joint distribution is described as P(x,y) = P(x|y)P(y) . To train a generative model, the following equation is used to maximize the joint probability \prod\left ( \frac{P(x_n,y_n)}{trainingData(x_n,y_n)} \right ) . The discriminative model is a logistic regression maximum entropy classifier. With the discriminative model, the goal is to predict a context specific substitution probability given a query sequence. The discriminative approach for modeling substitution probabilities, P(a|C_l) where C_l describes a sequence of amino acids around position l of a sequence, is based on K context states. Context states are characterized by parameters emission weight ( v_k(a) ), bias weight ( \pi_k ), and context weight ( \lambda_k(j,a) ) [2]. Emission probabilities from a context state are given by the emission weights as follows for d=1 to 20 : P(a|k) = \left ( \frac{exp(v_k(a))}{\sum exp(v_k(a'))} \right ) where P(a|k) is the emission probability and is the context state. In the discriminative approach, probability for a context state k given context C_l is modeled directly by the exponential of an affine function of the context account profile where C_l(j,a) is the context count profile with a normalization constant Z(C_l) normalizes the probability to 1. This equation is as follows where the first summation takes j=-d to d and the second summation takes a=1 to 20 : P(k|C_l) = \left ( \frac{1}{Z(C_l)}exp(\pi_k + \pi\sum\sum \lambda_k(j,a)(C_l(j,a)) \right) . As with the generative model, target distribution is obtained by mixing the emission probabilities of each context state weighted by the similarity.

See also

Sequence alignment software Multiple sequence alignment Position-specific scoring matrix BLAST (Basic Local Alignment Search Tool) HH-suite software package

References

[1] Alva, Vikram, Seung-Zin Nam, Johannes Söding, and Andrei N. Lupas. “The MPI Bioinformatics Toolkit as an Integrative Platform for Advanced Protein Sequence and Structure Analysis.” Nucleic Acids Research 44.Web server Issue (2016): W410-415. NCBI. Web. 2 Nov. 2016. [2] Angermüller, Christof, Andreas Biegert, and Johannes Söding. “Discriminative Modelling of Context-specific Amino Acid Substitution Properties” BIOINFORMATICS 28.24 (2012): 3240-247. Oxford Journals. Web. 2 Nov. 2016. [3] Astschul, Stephen F., et al. “Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Programs.” Nucleic Acids Research 25.17 (1997): 3389-402. Oxford University Press. Print [4] Bigert, A., and J. Söding. “Sequence Context-specific Profiles for Homology Searching.” Proceedings of the National Academy of Sciences 106.10 (2009): 3770-3775. PNAS. Web. 23 Oct. 2016.

External links

CS-BLAST — free server at LMU Munich CS-BLAST — free server at Max-Planck Institute in Tuebingen CS-BLAST source code

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