MAGUS+eHMMs: improved multiple sequence alignment accuracy for fragmentary sequences.

ABSTRACT:

Summary

Multiple sequence alignment is an initial step in many bioinformatics pipelines, including phylogeny estimation, protein structure prediction and taxonomic identification of reads produced in amplicon or metagenomic datasets, etc. Yet, alignment estimation is challenging on datasets that exhibit substantial sequence length heterogeneity, and especially when the datasets have fragmentary sequences as a result of including reads or contigs generated by next-generation sequencing technologies. Here, we examine techniques that have been developed to improve alignment estimation when datasets contain substantial numbers of fragmentary sequences. We find that MAGUS, a recently developed MSA method, is fairly robust to fragmentary sequences under many conditions, and that using a

SUBMITTER: Shen C 

PROVIDER: S-EPMC8796358 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature