MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments. (April 2020)

• Record Type: Journal Article
• Authors: Mier, Pablo; Andrade-Navarro, Miguel A
• Is Part Of: Evolutionary bioinformatics online. Volume 16(2020)
• Publication Date: 2020-04
• Subjects: Sequence analysis -- multiple sequence alignments -- computational biology -- web services -- motif finding; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934320916199
• Languages: English
• ISSNs: 1176-9343

Abstract

Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult to appreciate visually when many protein sequences are aligned. This new tool (MAGA;http://cbdm-01.zdv.uni-mainz.de/~munoz/maga/ ) locates positions in multiple sequence alignments differentially conserved in manually defined groups of sequences.