{"title":"Multiple sequence alignment based on structural properties","authors":"Bugra Ozer, Gizem Gezici, Cem Meydan, U. Sezerman","doi":"10.1109/HIBIT.2010.5478910","DOIUrl":null,"url":null,"abstract":"A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences. Main idea behind multiple sequence alignment is to see the similarities between input sequences, to be able to make phylogenetic analysis and other evolutionary conclusions. We propose a multiple sequence alignment method based on contact maps derived from structural data and network properties. We show that such methods may be useful in creating multiple alignments that can identify domains and similar structures where sequence identity is low.","PeriodicalId":215457,"journal":{"name":"2010 5th International Symposium on Health Informatics and Bioinformatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 5th International Symposium on Health Informatics and Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIBIT.2010.5478910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences. Main idea behind multiple sequence alignment is to see the similarities between input sequences, to be able to make phylogenetic analysis and other evolutionary conclusions. We propose a multiple sequence alignment method based on contact maps derived from structural data and network properties. We show that such methods may be useful in creating multiple alignments that can identify domains and similar structures where sequence identity is low.