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Which algorithm or algorithms are considered the standard or state-of-the-art for multiple sequence alignment?

How big is the need for better algorithms? How many sequences need to be alignment in a typical test? I am trying to understand how important problem this is in bioinformatics.

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    $\begingroup$ This would be better asked on biostars.org, the bioinformatics stack exchange. For the typical number of sequences aligned: you cannot say that. There are so many different uses of MSAs and too many different datasets one could have to be able to give one typical example. $\endgroup$
    – skymningen
    Commented Jul 30, 2014 at 8:51
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    $\begingroup$ @skymninge Bioinformatics are on-topic here, and Biostars is not affiliated with SE in any way now (it was a SE 1.0 site, which means they used the SE software but SE wasn't involved otherwise in running the site) $\endgroup$
    – Mad Scientist
    Commented Jul 30, 2014 at 9:36
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    $\begingroup$ @Mad Scientist Still, the chances that this type of theoretical question will be answered on Biostars are higher. The answered bioinformatics questions on this site are usually more practical approaches/use cases/... I did not imply the question is off-topic otherwise I would have flagged it as such. I just wanted to be helpful. $\endgroup$
    – skymningen
    Commented Jul 30, 2014 at 9:43
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    $\begingroup$ All bioinformatics questions are on-topic. Just that if someone requires technical help then biostars has more people who can help. $\endgroup$
    – WYSIWYG
    Commented Jul 30, 2014 at 12:00
  • $\begingroup$ having said that, this question seems quite broad to me. More details need to be provided. $\endgroup$
    – WYSIWYG
    Commented Jul 30, 2014 at 12:01

3 Answers 3

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My Vote goes to Mafft(insi) as it have ~86% accuracy and results in ~1.2 hour. Though fastest will be kalign takes only ~3 minutes to finish with an accuracy of 74.3%.

For testing:

For each of the 218 reference alignments in the benchmark, we applied eight alignment programs, resulting in a total of 1744 automatically constructed MSAs. The overall quality of these automatic alignments was measured using the Column Score (CS) described in Methods.

enter image description here FIGURE 1: Overall alignment performance for each of the MSA programs tested.

(A) Overall Accuracy

(B) Total run time for constructing all alignments (a log10 scale is used for display purposes).

doi:10.1371/journal.pone.0018093.g003

Compared Tools


(source: plosone.org)

Source and Photo Credits:

A Comprehensive Benchmark Study of Multiple Sequence Alignment Methods: Current Challenges and Future Perspectives

PS: This is from an old paper of 2011. If you want the new statistics you can always test on your own, by the process described in the source paper.

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    $\begingroup$ Personally, I always use muscle + gblocks for proteins. Does the job well enough, IMO. $\endgroup$
    – 5heikki
    Commented Jul 30, 2014 at 12:19
  • $\begingroup$ @5heikki: I agree. Muscle is actually good. $\endgroup$
    – Devashish Das
    Commented Jul 30, 2014 at 12:26
  • $\begingroup$ Always Clustalx FTW! $\endgroup$
    – user1357
    Commented Jul 30, 2014 at 21:20
  • $\begingroup$ I dont think any of them has a parallelization option. I think parallelization is possible for pairwise distance calculation step atleast. $\endgroup$
    – WYSIWYG
    Commented Jul 31, 2014 at 8:11
  • $\begingroup$ Bare in mind that the results from this benchmark are already very out of date. $\endgroup$
    – James
    Commented Nov 30, 2015 at 11:46
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The PRANK and PAGAN algorithms have both come out of the Loytynoja lab in Finnland, and are stirring up the pot a bit. They use inferred phylogenetic relationships as a parameter, and tend to yield a much more 'gappy' alignment, supposedly due to more accurate handling of indels. For easy alignments the method doesn't matter so much, but if the sequences are highly divergent it might be worth while checking out PAGAN and PRANK.

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Clustal has reinvented itself as Clustal Omega using Hidden Markov Models, and is particularly suited to the alignment of very many sequences.

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