RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics

By Marc Lohse, Anthony M. Bolger, Axel Nagel, Alisdair R. Fernie, J. Lunn, Mark Stitt, Björn Usadel. Nucleic Acids Res., 2012

823 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9c5ad83fbe82fe61bcce56557ebb1ec15211f1df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data (2014, methodology): “The original A5 can not assemble 250nt reads, so we used Trimmomatic to cut the reads down to 150nt (discarding 100nt) prior to assembly.”
Deep Proteomics of the Xenopus laevis Egg using an mRNA-derived Reference Database (2014, neutral): “[44], resuspended in 10 mL 1% formic acid.”
Resolving the psyllid tree of life: phylogenomic analyses of the superfamily Psylloidea (Hemiptera) (2018, methodology): “A quality (Q) assessment of raw fastq files for each library was made using fastqc v.0.10.1 (www.bioinformatics.babraham.ac.uk/ projects/fastqc) prior to the removal of adapter sequences with trimmomatic v.0.30 (Lohse et al., 2012).”
RNA Sequencing and Analysis (2015, neutral): “User-friendly software tools designed to generate quality overviews include the FASTX-toolkit (http://hannonlab.cshl.edu/fastx_toolkit), the FastQC software (http:// www.bioinformatics.babraham.ac.uk/projects/fastqc), and the RobiNA package (Lohse et al. 2012).”
Enriching the ant tree of life: enhanced UCE bait set for genome‐scale phylogenetics of ants and other Hymenoptera (2017, methodology): “Using the FASTQ files, we cleaned and trimmed raw reads using ILLUMIPROCESSOR (Faircloth 2013), which is a wrapper around TRIMMOMATIC (Lohse et al. 2012; Del Fabbro et al. 2013), and we assembled reads de novo using a wrapper (phyluce_assembly_assemblo_trinity) around TRINITY v2013-02-25 (Grabherr…”
Longitudinal analysis of microbial interaction between humans and the indoor environment (2014, positive): “Quality filtering of shotgun reads was performed using Trimmomatic (27) Version 0.”
SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data (2017, methodology): “For example, RObiNA [22] provides 4 preprocessing modules to combined for different RNA-Seq Data.”
Phylogenomic analyses of species relationships in the genus Sabal (Arecaceae) using targeted sequence capture (2016, methodology): “Reads were then quality trimmed from their 30 ends using Trimmomatic v. 0.17 (Lohse et al., 2012) with a minimum Phred score of 20 and a sliding window of 4 bases.”
HTSeq—a Python framework to work with high-throughput sequencing data (2014, background): “, 2011) for quality assessment or Trimmomatic (Lohse et al., 2012) for trimming of reads.”
Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads (2014, methodology): “…of Smith-Waterman algorithm; Trimmomatic (http://www. usadellab.org/cms/index.php?page=trimmomatic), which is a part of an integrated tool RobiNA [11]; Scythe (https://github.com/vsbuffalo/scythe), which uses a Naive Bayesian approach to classify contaminants in reads; TrimGalore…”