  Many biological questions, including the estimation of deep evolutionary
histories and the detection of remote homology between protein sequences, rely
upon multiple sequence alignments (MSAs) and phylogenetic trees of large
datasets. However, accurate large-scale multiple sequence alignment is very
difficult, especially when the dataset contains fragmentary sequences. We
present UPP, an MSA method that uses a new machine learning technique - the
Ensemble of Hidden Markov Models - that we propose here. UPP produces highly
accurate alignments for both nucleotide and amino acid sequences, even on
ultra-large datasets or datasets containing fragmentary sequences. UPP is
available at https://github.com/smirarab/sepp.
