{{short description|Protein multiple-sequence alignment program}}
In bioinformatics and proteomics, '''ProbCons''' is an open source software for probabilistic consistency-based multiple alignment of amino acid sequences. It is one of the most efficient protein multiple sequence alignment programs, since it has repeatedly demonstrated a statistically significant advantage in accuracy over similar tools, including Clustal and MAFFT.<ref>{{cite journal |doi=10.1101/gr.2821705 |vauthors=Do CB, Mahabhashyam MS, Brudno M, Batzoglou S |year=2005 |title=PROBCONS: Probabilistic Consistency-based Multiple Sequence Alignment |journal=Genome Research |volume=15 |issue=2 |pages=330–340 |pmid=15687296 |pmc=546535}}</ref><ref>{{Cite book|title=Multiple Sequence Alignment Methods|volume = 1079|last=Roshan|first=Usman|date=2014-01-01|publisher=Humana Press|isbn=9781627036450|editor-last=Russell|editor-first=David J|series=Methods in Molecular Biology|pages=147–153|language=English|doi=10.1007/978-1-62703-646-7_9|pmid = 24170400|chapter = Multiple Sequence Alignment Using Probcons and Probalign}}</ref>
==Algorithm== The following describes the basic outline of the ProbCons algorithm.<ref>[http://www.bioinf.uni-freiburg.de//Lehre/Courses/2011_WS/V_BioinfoII/slides_probcons.pdf Lecture "Bioinformatics II" at University of Freiburg]</ref>
===Step 1: Reliability of an alignment edge=== For every pair of sequences compute the probability that letters <math>x_i</math> and <math>y_i</math> are paired in <math>a^*</math> an alignment that is generated by the model.
<math display=block>\begin{align} P(x_i \sim y_i|x,y) \ \overset{\underset{\mathrm{def}}{}}{=}& \ \Pr[x_i \sim y_i \text{ in some } a|x,y] \\[8pt] =& \ \sum_{\text{alignment } a \atop {\text{with }x_i - y_i}} \Pr[a|x,y] \\[2pt] =& \ \sum_{\text{alignment } a} \mathbf{1}\{x_i - y_i \in a\} \Pr[a|x,y] \end{align}</math>
(Where <math>\mathbf{1}\{x_i \sim y_i \in a\}</math> is equal to 1 if <math>x_i</math> and <math>y_i</math> are in the alignment and 0 otherwise.)
===Step 2: Maximum expected accuracy=== The accuracy of an alignment <math>a^*</math> with respect to another alignment <math>a</math> is defined as the number of common aligned pairs divided by the length of the shorter sequence.
Calculate expected accuracy of each sequence:
<math display=block>\begin{align} E_{\Pr[a|x,y]}(\operatorname{acc}(a^*,a)) & = \sum_{a}\Pr[a|x,y] \operatorname{acc}(a^*,a) \\ & = \frac{1}{\min(|x|,|y|)} \cdot \sum_{a}\mathbf{1}\{x_i \sim y_i \in a\} \Pr[a|x,y]\\ & = \frac{1}{\min(|x|,|y|)} \cdot \sum_{x_i - y_i} P(x_i \sim y_j|x,y) \end{align}</math>
This yields a maximum expected accuracy (MEA) alignment:
<math display=block> E(x,y) = \arg\max_{a^*} \; E_{\Pr[a|x,y]}(\operatorname{acc}(a^*,a)) </math>
===Step 3: Probabilistic Consistency Transformation=== All pairs of sequences x,y from the set of all sequences <math>\mathcal{S}</math> are now re-estimated using all intermediate sequences z:
<math display=block> P'(x_i - y_i|x,y) = \frac{1}{|\mathcal{S}|} \sum_{z} \sum_{1 \leq k \leq |z|} P(x_i \sim z_i|x,z) \cdot P(z_i \sim y_i|z,y) </math>
This step can be iterated.
===Step 4: Computation of guide tree=== Construct a guide tree by hierarchical clustering using MEA score as sequence similarity score. Cluster similarity is defined using weighted average over pairwise sequence similarity.
===Step 5: Compute MSA=== Finally compute the MSA using progressive alignment or iterative alignment.
== See also == * Sequence alignment software * Clustal * MUSCLE * AMAP * T-Coffee * Probalign
==References== {{Reflist}}
==External links== *{{Official website|http://probcons.stanford.edu/}}
Category:Computational phylogenetics