ContextMap 2: fast and accurate context-based RNA-seq mapping

Abstract

BackgroundMapping of short sequencing reads is a crucial step in the analysis of RNA sequencing (RNA-seq) data. ContextMap is an RNA-seq mapping algorithm that uses a context-based approach to identify the best alignment for each read and allows parallel mapping against several reference genomes.ResultsIn this article, we present ContextMap 2, a new and improved version of ContextMap. Its key novel features are: (i) a plug-in structure that allows easily integrating novel short read alignment programs with improved accuracy and runtime; (ii) context-based identification of insertions and deletions (indels); (iii) mapping of reads spanning an arbitrary number of exons and indels. ContextMap 2 using Bowtie, Bowtie 2 or BWA was evaluated on both simulated and real-life data from the recently published RGASP study.ConclusionsWe show that ContextMap 2 generally combines similar or higher recall compared to other state-of-the-art approaches with significantly higher precision in read placement and junction and indel prediction. Furthermore, runtime was significantly lower than for the best competing approaches. ContextMap 2 is freely available at http://www.bio.ifi.lmu.de/ContextMap.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0557-5) contains supplementary material, which is available to authorized users.

Simulation-based comprehensive benchmarking of RNA-seq aligners

“…We identified 14 algorithms which satisfy these four basic requirements: CLC Genomics Workbench v8.5 (http://www.qiagenbioinformatics.com/products/clc-genomics-workbench/), ContextMap2 v2.6.0 (ref. 2 ), CRAC v2.4.0 (ref. 3 ), GSNAP v2015-9-29 (ref.…”

A systematic NGS-based approach for contaminant detection and functional inference

“…Our first goal was to extract exogenous reads from the input NGS reads by performing greedy alignments. Similar to the initial screening step in published methods [18, 25, 26], our method thoroughly discards host-related reads (Step I to IV in Fig. 1A).…”

GeneQC: A quality control tool for gene expression estimation based on RNA-sequencing reads mapping

“…For conciseness, we refer to these genomic regions simply as "genes." This issue has been observed in many diploid species, including human and other mammals and Arabidopsis (Anders and Huber, 2012;Anders, et al, 2015;Bonfert, et al, 2015;Garber, et al, 2011;Wang, et al, 2009), as well as many multiploid species.…”

“…Research involving RNA-Seq data produces genetic expression profiles, in which a discrete expression value for each annotated gene for that species is identified. These gene expression profiles are extracted through computational RNA-Seq analysis pipelines (Anders, et al, 2015;Andrews, 2010;Bonfert, et al, 2015;Chang, et al, 2015;Dobin, et al, 2013;Grabherr, et al, 2011;Kim, et al, 2015;Kong, 2011;Li and Dewey, 2011;Pertea, et al, 2016;Pertea, et al, 2015;Philippe, et al, 2013;Trapnell, et al, 2009;Wang, et al, 2010;Wang, et al, 2009;Wu, et al, 2013;Wu, et al, 2016;Yuan, et al, 2017), which can be analyzed further to identify differentially expressed genes between treatment groups (Anders and Huber, 2012;Pimentel, et al, 2017;Ritchie, et al, 2015;Robinson, et al, 2010;Trapnell, et al, 2012), enriched functional gene modules (Chen, et al, 2009;Pathan, et al, 2015;Subramanian, et al, 2005;Zhou and Su, 2007), co-expression networks , and to generate visualizations to assist in broad interpretations between treatment groups (Ge, 2017;Goff, et al, 2013;Harshbarger, et al, 2017;McDermaid, et al, 2018;Nelson, et al, 2017;Nueda, et al, 2017;Perkel, 2018;Powell, 2015;Younesy, et al, 2015), among other applications.…”