Natasha Pavlovikj, Kevin Begcy, S. Behera, Malachy T. Campbell, H. Walia, J. Deogun
{"title":"Analysis of transcriptome assembly pipelines for wheat","authors":"Natasha Pavlovikj, Kevin Begcy, S. Behera, Malachy T. Campbell, H. Walia, J. Deogun","doi":"10.1109/BIBM.2016.7822507","DOIUrl":null,"url":null,"abstract":"With advances in next-generation sequencing technologies, transcriptome sequencing has emerged as a powerful tool for performing transcriptome analysis for various organisms. Obtaining draft transcriptome of an organism is a complex multi-stage pipeline with several steps such as data cleaning, error correction and assembly. Based on the analysis performed in this paper, we conclude that the best assembly is produced when the error correction method is used with Velvet Oases and the “multi-k” strategy that combines the 5 k-mer assemblies with highest N50. Our results provide valuable insight for designing good de novo transcriptome assembly pipeline for a given application.","PeriodicalId":345384,"journal":{"name":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2016.7822507","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With advances in next-generation sequencing technologies, transcriptome sequencing has emerged as a powerful tool for performing transcriptome analysis for various organisms. Obtaining draft transcriptome of an organism is a complex multi-stage pipeline with several steps such as data cleaning, error correction and assembly. Based on the analysis performed in this paper, we conclude that the best assembly is produced when the error correction method is used with Velvet Oases and the “multi-k” strategy that combines the 5 k-mer assemblies with highest N50. Our results provide valuable insight for designing good de novo transcriptome assembly pipeline for a given application.