iRAP - an integrated RNA-seq Analysis Pipeline

Fonseca, Nuno A.; Petryszak, Robert; Marioni, John C.; Brāzma, Alvis

doi:10.1101/005991

Cited by 34 publications

(35 citation statements)

References 28 publications

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“…Similarly, pipelines have been proposed to allow the reanalysis of expression data that provide useful functionality but limit the number of samples that can be analyzed (D'Antonio et al, 2015), have limited visualization outputs (Fonseca et al, 2014), or require the user to process their own data before uploading to a visualization tool (Nussbaumer et al, 2014). In most cases, visualization tools are static and do not allow meaningful comparison of data.…”

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confidence: 99%

expVIP: a Customizable RNA-seq Data Analysis and Visualization Platform

2016

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The majority of transcriptome sequencing (RNA-seq) expression studies in plants remain underutilized and inaccessible due to the use of disparate transcriptome references and the lack of skills and resources to analyze and visualize these data. We have developed expVIP, an expression visualization and integration platform, which allows easy analysis of RNA-seq data combined with an intuitive and interactive interface. Users can analyze public and user-specified data sets with minimal bioinformatics knowledge using the expVIP virtual machine. This generates a custom Web browser to visualize, sort, and filter the RNA-seq data and provides outputs for differential gene expression analysis. We demonstrate expVIP's suitability for polyploid crops and evaluate its performance across a range of biologically relevant scenarios. To exemplify its use in crop research, we developed a flexible wheat (Triticum aestivum) expression browser (www.wheat-expression.com) that can be expanded with user-generated data in a local virtual machine environment. The open-access expVIP platform will facilitate the analysis of gene expression data from a wide variety of species by enabling the easy integration, visualization, and comparison of RNA-seq data across experiments.The global demand for staple crops is predicted to double by 2050 (FAO, 2009;Tilman et al., 2011), which will require an annual increase in yield of approximately 2.4% (Ray et al., 2013). However, currently, yields of the major crops maize (Zea mays), rice (Oryza sativa), wheat (Triticum aestivum), and soybean (Glycine max) are increasing only at 1.6%, 1%, 0.9%, and 1.3% per year, respectively (Ray et al., 2013). The advent of the genomics era represents a great opportunity to accelerate the pace of yield increase in staple crops, for example, by facilitating novel breeding strategies (Heffner et al., 2009) and providing unprecedented numbers of genetic markers (Bevan and Uauy, 2013). In particular, transcriptome sequencing (RNA-seq) is a widely adopted genomics approach in crops due to its relatively low cost (Wang et al., 2009), its suitability for nonmodel organisms (Ekblom and Galindo, 2011), and the multiple downstream applications of the data generated. These features have driven the generation of a wealth of expression data with over 9,000 RNAseq samples currently available at public repositories, such as the National Center for Biotechnology Information (NCBI)/ENA for the major agricultural crops (Table I).Although several public databases containing gene expression data for plant species exist (Lawrence et al., 2007;Ouyang et al., 2007;Dash et al., 2012), these resources do not make full use of the expression data available in SRAs, frequently relying on a subset of experiments or microarray data. Similarly, pipelines have been proposed to allow the reanalysis of expression data that provide useful functionality but limit the number of samples that can be analyzed (D'Antonio et al., 2015), have limited visualization outputs (Fonseca et al., 2014), or require t...

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confidence: 99%

expVIP: a Customizable RNA-seq Data Analysis and Visualization Platform

2016

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“…RNA-seq libraries were analyzed with iRAP (Fonseca et al 2014), using TopHat2 (Kim et al 2013) to map reads to the reference genome (NCBIM37) and HTSeq-count ) to assign reads to the Ensembl release 67 gene annotation (Flicek et al 2014) using default parameters, and by excluding mitochondrial and sex chromsome encoded genes.…”

Section: Rna-seq Analysismentioning

confidence: 99%

High-resolution mapping of transcriptional dynamics across tissue development reveals a stable mRNA–tRNA interface

Schmitt

Rudolph

Karagianni³

et al. 2014

Genome Res.

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110

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The genetic code is an abstraction of how mRNA codons and tRNA anticodons molecularly interact during protein synthesis; the stability and regulation of this interaction remains largely unexplored. Here, we characterized the expression of mRNA and tRNA genes quantitatively at multiple time points in two developing mouse tissues. We discovered that mRNA codon pools are highly stable over development and simply reflect the genomic background; in contrast, precise regulation of tRNA gene families is required to create the corresponding tRNA transcriptomes. The dynamic regulation of tRNA genes during development is controlled in order to generate an anticodon pool that closely corresponds to messenger RNAs. Thus, across development, the pools of mRNA codons and tRNA anticodons are invariant and highly correlated, revealing a stable molecular interaction interlocking transcription and translation.

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“…Expression Atlas includes large landmark baseline studies on human subjects or cell lines, such as GTEx, CCLE, ENCODE, BLUEPRINT, HipSci, as well as differential studies on human diseases in human patients or animal models. Analyses of bulk or single cell RNA-seq data sets are performed using our open source pipeline iRAP (Fonseca 2014). Expression Atlas can be searched by gene, gene set and experimental condition queries (Figure 4a).…”

Section: Pcawg-scoutmentioning

confidence: 99%