Parallelization of MAFFT for large-scale multiple sequence alignments
By Tsukasa Nakamura, Kazunori D. Yamada, Kentaro Tomii, Kazutaka Katoh. Bioinform., 2018
893 citations recorded by the source corpus.
Abstract Summary We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. Availability and implementation This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html. Supplementary information Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/0ed1a2dcdff11d08b881652c7ad7a88063e4b713
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In Molecular Biology
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In Spectroscopy
Citations in the corpus
- INfrastructure for a PHAge REference Database: Identification of Large-Scale Biases in the Current Collection of Cultured Phage Genomes (2021, positive): “39–41 Additional information was overlaid using IToL templates that are generated through INPHARED.”
- Major clades and a revised classification of Magnolia and Magnoliaceae based on whole plastid genome sequences via genome skimming (2020, methodology): “…that likely occurred among the members of Magnoliaceae, we extracted the 86 coding sequences (CDSs) by Geneious Prime (Kearse et al., 2012), followed by alignment with MAFFT v.7.409 (Nakamura et al., 2018), and sequence concatenation using AMAS (Borowiec, 2016) for the downstream analyses.”
- Capturing single-copy nuclear genes, organellar genomes, and nuclear ribosomal DNA from deep genome skimming data for plant phylogenetics: A case study in Vitaceae (2021, methodology): “All sequences from plastome, mitogenome, nrDNA repeats, and SCNs assembled here were aligned separately by MAFFT v. 7.475 (Nakamura et al., 2018) with default parameters.”
- Petabase-scale sequence alignment catalyses viral discovery (2020, neutral): “407) 86 and filtered by TrimAL (v1.”
- ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, methodology): “This is done by clustering sequences at 95% sequence identity using CD-HIT [84, 85], aligning the sequences and labels of each cluster using MAFFT [86] and propagating the labels along each column.”