Parallelization of MAFFT for large-scale multiple sequence alignments.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• The MAFFT (Multiple Alignment Fast Fourier Transform) program is a widely used tool for sequence alignment.
• The G-INS-1 option in MAFFT offers high accuracy for large datasets but is computationally intensive and impractical for large-scale analyses.
• There is a need for scalable alignment methods that maintain accuracy while reducing computational resource requirements.

Purpose of the Study:

• To introduce a scalable variant of the G-INS-1 algorithm in MAFFT.
• To enable parallel computation for aligning large numbers of sequences efficiently.
• To provide a practical solution for high-accuracy multiple sequence alignment of datasets exceeding 50,000 sequences.

Main Methods:

• Development of a scalable variant, G-large-INS-1, of the G-INS-1 algorithm.
• Implementation of parallel computation capabilities within the MAFFT software.
• Testing and validation of the G-large-INS-1 method on large-scale sequence datasets.

Main Results:

• The G-large-INS-1 variant achieves equivalent accuracy to the original G-INS-1 method.
• The new method is scalable and applicable to datasets with 50,000 or more sequences.
• Parallel computation significantly reduces the practical limitations of previous high-accuracy methods.

Conclusions:

• The G-large-INS-1 update to MAFFT provides a computationally efficient and accurate solution for large-scale multiple sequence alignment.
• This advancement democratizes the use of high-accuracy alignment methods for extensive biological datasets.
• The updated MAFFT program (version 7.355 and later) is now available for large-scale genomic and bioinformatics analyses.