RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
Marc Lohse,Anthony Bolger,Axel Nagel,Alisdair R. Fernie,John E. Lunn,Mark Stitt,Björn Usadel +6 more
TLDR
RobiNA is an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface and supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state of the art biostatistical methods developed in the R/Bioconductor projects.Abstract:
Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/ robin.read more
Citations
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HTSeq—a Python framework to work with high-throughput sequencing data
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Journal ArticleDOI
De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis
Brian J. Haas,Alexie Papanicolaou,Moran Yassour,Moran Yassour,Manfred Grabherr,Philip D. Blood,Joshua C. Bowden,M. B. Couger,David Eccles,Bo Li,Matthias Lieber,Matthew D. MacManes,Michael Ott,Joshua Orvis,Nathalie Pochet,Nathalie Pochet,Francesco Strozzi,Nathan T. Weeks,Rick Westerman,Thomas William,Colin N. Dewey,Robert Henschel,Richard D. LeDuc,Nir Friedman,Aviv Regev +24 more
TL;DR: This protocol provides a workflow for genome-independent transcriptome analysis leveraging the Trinity platform and presents Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes.
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N 6 -methyladenosine-dependent RNA structural switches regulate RNA–protein interactions
TL;DR: It is found that m6A alters the local structure in mRNA and long non-coding RNA (lncRNA) to facilitate binding of heterogeneous nuclear ribonucleoprotein C (HNRNPC), an abundant nuclear RNA-binding protein responsible for pre-mRNA processing.
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SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data.
Chen Yuxin,Yongsheng Chen,Chunmei Shi,Huang Zhibo,Zhang Yong,Li Shengkang,Yan Li,Jia Ye,Chang Yu,Zhuo Li,Xiuqing Zhang,Jian Wang,Huanming Yang,Fang Lin,Qiang Chen +14 more
TL;DR: SOAPnuke is demonstrated as a tool with abundant functions for a “QC-Preprocess-QC” workflow and MapReduce acceleration framework that enables large scalability to distribute all the processing works to an entire compute cluster.
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Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads.
TL;DR: A novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length is devised, which achieves as yet unmatched accuracies for adapter trimming with low time bound.
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