ProbCons is an open source 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]
The following describes the basic outline of the ProbCons algorithm. [3]
For every pair of sequences compute the probability that letters and are paired in an alignment that is generated by the model.
(Where is equal to 1 if and are in the alignment and 0 otherwise.)
The accuracy of an alignment with respect to another alignment is defined as the number of common aligned pairs divided by the length of the shorter sequence.
Calculate expected accuracy of each sequence:
This yields a maximum expected accuracy (MEA) alignment:
All pairs of sequences x,y from the set of all sequences are now re-estimated using all intermediate sequences z:
This step can be iterated.
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.
Finally compute the MSA using progressive alignment or iterative alignment.
ProbCons is an open source 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]
The following describes the basic outline of the ProbCons algorithm. [3]
For every pair of sequences compute the probability that letters and are paired in an alignment that is generated by the model.
(Where is equal to 1 if and are in the alignment and 0 otherwise.)
The accuracy of an alignment with respect to another alignment is defined as the number of common aligned pairs divided by the length of the shorter sequence.
Calculate expected accuracy of each sequence:
This yields a maximum expected accuracy (MEA) alignment:
All pairs of sequences x,y from the set of all sequences are now re-estimated using all intermediate sequences z:
This step can be iterated.
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.
Finally compute the MSA using progressive alignment or iterative alignment.