Hauptseite > Publikationsdatenbank > RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics > print

001     22185
005     20200423203245.0
024 7 _ |2 pmid
|a pmid:22684630
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|a pmc:PMC3394330
024 7 _ |2 DOI
|a 10.1093/nar/gks540
024 7 _ |2 WOS
|a WOS:000306670900102
024 7 _ |2 ISSN
|a 0305-1048
024 7 _ |2 Handle
|a 2128/7513
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037 _ _ |a PreJuSER-22185
041 _ _ |a eng
082 _ _ |a 570
084 _ _ |2 WoS
|a Biochemistry & Molecular Biology
100 1 _ |0 P:(DE-HGF)0
|a Lohse, M.
|b 0
245 _ _ |a RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
260 _ _ |a Oxford
|b Oxford Univ. Press
|c 2012
300 _ _ |a W622 - W 627
336 7 _ |a Journal Article
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440 _ 0 |0 24082
|a Nucleic Acids Research
|v 40
|y W1
500 _ _ |a Funding for open access charge: The Max Planck Society; RobiNA was developed within the Plant KBBE-SAFQIM, the German Ministry of Education and Research (BMBF) [project 0315912].
520 _ _ |a 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.
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700 1 _ |0 P:(DE-HGF)0
|a Fernie, A.R.
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|a Lunn, J.E.
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|a Stitt, M.
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|a Usadel, B.
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856 7 _ |2 Pubmed Central
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