HMMerge: An ensemble method for multiple sequence alignment

Minhyuk Park, Tandy Warnow

Research output: Contribution to journalArticlepeer-review


Motivation: Despite advances in method development for multiple sequence alignment over the last several decades, the alignment of datasets exhibiting substantial sequence length heterogeneity, especially when the input sequences include very short sequences (either as a result of sequencing technologies or of large deletions during evolution) remains an inadequately solved problem. Results: We present HMMerge, a method to compute an alignment of datasets exhibiting high sequence length heterogeneity, or to add short sequences into a given 'backbone' alignment. HMMerge builds on the technique from its predecessor alignment methods, UPP and WITCH, which build an ensemble of profile HMMs to represent the backbone alignment and add the remaining sequences into the backbone alignment using the ensemble. HMMerge differs from UPP and WITCH by building a new 'merged' HMM from the ensemble, and then using that merged HMM to align the query sequences. We show that HMMerge is competitive with WITCH, with an advantage over WITCH when adding very short sequences into backbone alignments.

Original languageEnglish (US)
Article numbervbad052
JournalBioinformatics Advances
Issue number1
StatePublished - 2023
Externally publishedYes

ASJC Scopus subject areas

  • Genetics
  • Molecular Biology
  • Structural Biology
  • Computer Science Applications


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