Moritz Smolka, Philipp Rescheneder, Michael C Schatz, Arndt von Haeseler, Fritz J Sedlazeck
{"title":"Teaser: Individualized benchmarking and optimization of read mapping results for NGS data.","authors":"Moritz Smolka, Philipp Rescheneder, Michael C Schatz, Arndt von Haeseler, Fritz J Sedlazeck","doi":"10.1186/s13059-015-0803-1","DOIUrl":null,"url":null,"abstract":"<p><p>Mapping reads to a genome remains challenging, especially for non-model organisms with lower quality assemblies, or for organisms with higher mutation rates. While most research has focused on speeding up the mapping process, little attention has been paid to optimize the choice of mapper and parameters for a user's dataset. Here, we present Teaser, a software that assists in these choices through rapid automated benchmarking of different mappers and parameter settings for individualized data. Within minutes, Teaser completes a quantitative evaluation of an ensemble of mapping algorithms and parameters. We use Teaser to demonstrate how Bowtie2 can be optimized for different data. </p>","PeriodicalId":48922,"journal":{"name":"Genome Biology","volume":"16 ","pages":"235"},"PeriodicalIF":12.3000,"publicationDate":"2015-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13059-015-0803-1","citationCount":"25","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-015-0803-1","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 25
Abstract
Mapping reads to a genome remains challenging, especially for non-model organisms with lower quality assemblies, or for organisms with higher mutation rates. While most research has focused on speeding up the mapping process, little attention has been paid to optimize the choice of mapper and parameters for a user's dataset. Here, we present Teaser, a software that assists in these choices through rapid automated benchmarking of different mappers and parameter settings for individualized data. Within minutes, Teaser completes a quantitative evaluation of an ensemble of mapping algorithms and parameters. We use Teaser to demonstrate how Bowtie2 can be optimized for different data.
期刊介绍:
Genome Biology is a leading research journal that focuses on the study of biology and biomedicine from a genomic and post-genomic standpoint. The journal consistently publishes outstanding research across various areas within these fields.
With an impressive impact factor of 12.3 (2022), Genome Biology has earned its place as the 3rd highest-ranked research journal in the Genetics and Heredity category, according to Thomson Reuters. Additionally, it is ranked 2nd among research journals in the Biotechnology and Applied Microbiology category. It is important to note that Genome Biology is the top-ranking open access journal in this category.
In summary, Genome Biology sets a high standard for scientific publications in the field, showcasing cutting-edge research and earning recognition among its peers.