FAME: fast and memory efficient multiple sequences alignment tool through compatible chain of roots

Abstract

Motivation Multiple sequence alignment (MSA) is important and challenging problem of computational biology. Most of the existing methods can only provide a short length multiple alignments in an acceptable time. Nevertheless, when the researchers confront the genome size in the multiple alignments, the process has required a huge processing space/time. Accordingly, using the method that can align genome size rapidly and precisely has a great effect, especially on the analysis of the very long alignments. Herein, we have proposed an efficient method, called FAME, which vertically divides sequences from the places that they have common areas; then they are arranged in consecutive order. Then these common areas are shifted and placed under each other, and the subsequences between them are aligned using any existing MSA tool. Results The results demonstrate that the combination of FAME and the MSA methods and deploying minimizer are capable to be executed on personal computer and finely align long length sequences with much higher sum-of-pair (SP) score compared to the standalone MSA tools. As we select genomic datasets with longer length, the SP score of the combinatorial methods is gradually improved. The calculated computational complexity of methods supports the results in a way that combining FAME and the MSA tools leads to at least four times faster execution on the datasets. Availability and implementation The source code and all datasets and run-parameters are accessible free on http://github.com/naznoosh/msa. Supplementary information Supplementary data are available at Bioinformatics online.

FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

“…To test FMAlign2’s performance on real datasets, we choose the long and similar datasets to serve as our benchmark. This dataset provided by Naznooshsadat et al (2020) includes five sequence sets of Variola ( VARV ), Mycoplasma genitalium ( M.genitalium ), Mycoplasma bovis ( M.bovis ), Streptococcus pneumoniae ( S.pneumoniae ), and Escherichia coli ( E.coli ). Each set contains an equal number of sequences but differs in average lengths, allowing us to assess the performance of the methods concerning the sequence length.…”

“…Unlike FAME ( Naznooshsadat et al 2020 ) and FMAlign ( Liu et al 2022 ), which use global chains, FMAlign2 segments sequences utilizing partial chains that appear in a subset of sequences. A global chain refers to a chain that exists in all sequences, with its substrings being completely identical across all sequences.…”

“…This strategy seeks common segments/minimizers to divide all the sequences and aligns every generated sub-sequence in parallel using the existing MSA method, enabling MSA methods to handle ultralong sequences more effectively. FAME ( Naznooshsadat et al 2020 ), a novel vertical-division-based method for aligning similar sequences, has gained attention in recent years. This method utilizes hash tables to detect k-mers and minimizers, potentially incorporating nonoverlapping anchors into a single chain.…”

Genetic surveillance reveals low, sustained malaria transmission with clonal replacement in Sao Tome and Principe

“…For STP samples with more than one sequence per amplicon per sample, the sequence with the highest read count was used. The concatenated sequences were aligned using the long sequence aligner FAME 45 . Sites with homology greater than 90% and sites containing more than 50% gaps were removed.…”