ProbCons

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.^[1]^[2]

Algorithm

The following describes the basic outline of the ProbCons algorithm.^[3]

Step 1: Reliability of an alignment edge

For every pair of sequences compute the probability that letters $x_{i}$ and $y_{i}$ are paired in $a^{*}$ an alignment that is generated by the model.

${\begin{aligned}P(x_{i}\sim y_{i}|x,y)\ {\overset {\underset {\mathrm {def} }{}}{=}}&\ \Pr[x_{i}\sim y_{i}{\text{ in some }}a|x,y]\\[8pt]=&\ \sum _{{\text{alignment }}a \atop {{\text{with }}x_{i}-y_{i}}}\Pr[a|x,y]\\[2pt]=&\ \sum _{{\text{alignment }}a}\mathbf {1} \{x_{i}-y_{i}\in a\}\Pr[a|x,y]\end{aligned}}$

(Where $\mathbf {1} \{x_{i}\sim y_{i}\in a\}$ is equal to 1 if $x_{i}$ and $y_{i}$ are in the alignment and 0 otherwise.)

Step 2: Maximum expected accuracy

The accuracy of an alignment $a^{*}$ with respect to another alignment $a$ is defined as the number of common aligned pairs divided by the length of the shorter sequence.

Calculate expected accuracy of each sequence:

${\begin{aligned}E_{\Pr[a|x,y]}(\operatorname {acc} (a^{*},a))&=\sum _{a}\Pr[a|x,y]\operatorname {acc} (a^{*},a)\\&={\frac {1}{\min(|x|,|y|)}}\cdot \sum _{a}\mathbf {1} \{x_{i}\sim y_{i}\in a\}\Pr[a|x,y]\\&={\frac {1}{\min(|x|,|y|)}}\cdot \sum _{x_{i}-y_{i}}P(x_{i}\sim y_{j}|x,y)\end{aligned}}$

This yields a maximum expected accuracy (MEA) alignment:

$E(x,y)=\arg \max _{a^{*}}\;E_{\Pr[a|x,y]}(\operatorname {acc} (a^{*},a))$

Step 3: Probabilistic Consistency Transformation

All pairs of sequences x,y from the set of all sequences ${\mathcal {S}}$ are now re-estimated using all intermediate sequences z:

$P'(x_{i}-y_{i}|x,y)={\frac {1}{|{\mathcal {S}}|}}\sum _{z}\sum _{1\leq k\leq |z|}P(x_{i}\sim z_{i}|x,z)\cdot P(z_{i}\sim y_{i}|z,y)$

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.

References

^ Do CB, Mahabhashyam MS, Brudno M, Batzoglou S (2005). "PROBCONS: Probabilistic Consistency-based Multiple Sequence Alignment". Genome Research. 15 (2): 330–340. doi:10.1101/gr.2821705. PMC 546535. PMID 15687296.
^ Roshan, Usman (2014-01-01). "Multiple Sequence Alignment Using Probcons and Probalign". In Russell, David J (ed.). Multiple Sequence Alignment Methods. Methods in Molecular Biology. Vol. 1079. Humana Press. pp. 147–153. doi:10.1007/978-1-62703-646-7_9. ISBN 9781627036450. PMID 24170400.
^ Lecture "Bioinformatics II" at University of Freiburg

External links

Official website

[1] Do CB, Mahabhashyam MS, Brudno M, Batzoglou S (2005). "PROBCONS: Probabilistic Consistency-based Multiple Sequence Alignment". Genome Research. 15 (2): 330–340. doi:10.1101/gr.2821705. PMC 546535. PMID 15687296.

[2] Roshan, Usman (2014-01-01). "Multiple Sequence Alignment Using Probcons and Probalign". In Russell, David J (ed.). Multiple Sequence Alignment Methods. Methods in Molecular Biology. Vol. 1079. Humana Press. pp. 147–153. doi:10.1007/978-1-62703-646-7_9. ISBN 9781627036450. PMID 24170400.

[3] Lecture "Bioinformatics II" at University of Freiburg

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