As a multiple sequence alignment tool, CAUSA has the following features:
(1) CAUSA is good for both molecular phylogenetic trees reconstruction and homology structure modeling;
As well known, DNA alignment is good for the construction of phylogenetic trees, but not appropriate for protein structure homology modeling; protein alignment is used for structure homology modeling, but not appropriate for construction of molecular phylogenetic tree in closely related proteins.
Traditionally, coding DNA sequences (CDSs) and its encoded protein sequences are aligned separately, so you have to work twice to construct two alignments: one DNA alignment and one protein alignment.
Using CAUSA, you only need to do a single job: CDSs and its encoded protein sequences are aligned simutanously, and it output a DNA alignment and a protein alignment, the DNA alignment could be used for the construction of phylogenetic trees, and the protein alignment could be used for structure modeling.
(2) The phylogeny trees inferred from the CAUSA protein alignment are always fully consistent with that of the CAUSA DNA alignment.
In traditional DNA or protein alignments, the phylogeny tree inferred from the protein alignment is often inconsistent with that of the corresponding DNA alignment.
(3) The phylogeny trees for different genes inferred from CAUSA alignment are generally more consistent with the species tree, and with each other.
In traditional DNA or protein alignments, the phylogeny trees inferred from different proteins are often inconsistent with each other, and with the species tree.
(4) Using CAUSA alignment, the phylogeny trees drawn by different method, such as ML, ME, NJ or UPGMA, and diferent Gaps/missing data treatment, such as “complete deletion”, “partial deletion” or “use all site”, are generally more consistent with each other.
In conventional DNA or protein only alignments, the phylogeny trees drawn by different method, such as ML, ME, NJ or UPGMA, and diferent Gaps/missing data treatment, such as “complete deletion”, “partial deletion” or “use all site”, are often inconsistent with each other.
(5) CAUSA more clearly explain how and why insertions and deletions lead to codon changes and protein structure evolution.
CAUSA takes a codon as a whole information unit while allowing gaps to be placed inside a codon, enlarges the state-space, enhances the information content, and improves the accuracy of alignment, shows some new mutation events, such as codon fusion, codon splitting, in-frame deletion and partial frame-shift.
(6) In addition, CAUSA is computationally much more efficient than 64-state codon level alignment methods.