RNA-seq Tools Research Articles

SaseR: Juggling offsets unlocks RNA-seq tools for fast and Scalable differential usage, Aberrant Splicing and Expression Retrieval.

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. Here, we show that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Our method, saseR, is much faster than state-of-the-art methods, dramatically outperforms these to detect aberrant splicing, and provides a single workflow for various short- and long-read RNA-seq applications.

Good software development practices improve citations of RNA seq tools

Good software development practices improve citations of RNA seq tools

Unique Splicing of Lrp5 in the Brain: A New Player in Neurodevelopment and Brain Maturation.

Low-density lipoprotein receptor-related protein 5 (LRP5) is a constitutively expressed receptor with observed roles in bone homeostasis, retinal development, and cardiac metabolism. However, the function of LRP5 in the brain remains unexplored. This study investigates LRP5's role in the central nervous system by conducting an extensive analysis using RNA-seq tools and in silico assessments. Two protein-coding Lrp5 transcripts are expressed in mice: full-length Lrp5-201 and a truncated form encoded by Lrp5-202. Wt mice express Lrp5-201 in the liver and brain and do not express the truncated form. Lrp5-/- mice express Lrp5-202 in the liver and brain and do not express Lrp5-201 in the liver. Interestingly, Lrp5-/- mouse brains show full-length Lrp5-201 expression, suggesting that LRP5 has a role in preserving brain function during development. Functional gene enrichment analysis on RNA-seq unveils dysregulated expression of genes associated with neuronal differentiation and synapse formation in the brains of Lrp5-/- mice compared to Wt mice. Furthermore, Gene Set Enrichment Analysis highlights downregulated expression of genes involved in retinol and linoleic acid metabolism in Lrp5-/- mouse brains. Tissue-specific alternative splicing of Lrp5 in Lrp5-/- mice supports that the expression of LRP5 in the brain is needed for the correct synthesis of vitamins and fatty acids, and it is indispensable for correct brain development.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Systematic benchmarking of statistical methods to assess differential expression of circular RNAs.

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics.

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Expression and Variation of the Genes Involved in Rhizobium Nodulation in Red Clover.

Red clover (Trifolium pratense L.) is an important forage crop and serves as a major contributor of nitrogen input in pasture settings because of its ability to fix atmospheric nitrogen. During the legume-rhizobial symbiosis, the host plant undergoes a large number of gene expression changes, leading to development of root nodules that house the rhizobium bacteria as they are converted into nitrogen-fixing bacteroids. Many of the genes involved in symbiosis are conserved across legume species, while others are species-specific with little or no homology across species and likely regulate the specific plant genotype/symbiont strain interactions. Red clover has not been widely used for studying symbiotic nitrogen fixation, primarily due to its outcrossing nature, making genetic analysis rather complicated. With the addition of recent annotated genomic resources and use of RNA-seq tools, we annotated and characterized a number of genes that are expressed only in nodule forming roots. These genes include those encoding nodule-specific cysteine rich peptides (NCRs) and nodule-specific Polycystin-1, Lipoxygenase, Alpha toxic (PLAT) domain proteins (NPDs). Our results show that red clover encodes one of the highest number of NCRs and ATS3-like/NPDs, which are postulated to increase nitrogen fixation efficiency, in the Inverted-Repeat Lacking Clade (IRLC) of legumes. Knowledge of the variation and expression of these genes in red clover will provide more insights into the function of these genes in regulating legume-rhizobial symbiosis and aid in breeding of red clover genotypes with increased nitrogen fixation efficiency.

Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP)

BackgroundTranscriptional regulation is a fundamental mechanism underlying biological functions. In recent years, a broad array of RNA-Seq tools have been used to measure transcription levels in biological experiments, in whole organisms, tissues, and at the single cell level. Collectively, this is a vast comparative dataset on transcriptional processes across organisms. Yet, due to technical differences between the studies (sequencing, experimental design, and analysis) extracting usable comparative information and conducting meta-analyses remains challenging.ResultsWe introduce Comparative RNA-Seq Metadata Analysis Pipeline (CoRMAP), a meta-analysis tool to retrieve comparative gene expression data from any RNA-Seq dataset using de novo assembly, standardized gene expression tools and the implementation of OrthoMCL, a gene orthology search algorithm. It employs the use of orthogroup assignments to ensure the accurate comparison of gene expression levels between experiments and species. Here we demonstrate the use of CoRMAP on two mouse brain transcriptomes with similar scope, that were collected several years from each other using different sequencing technologies and analysis methods. We also compare the performance of CoRMAP with a functional mapping tool, previously published.ConclusionCoRMAP provides a framework for the meta-analysis of RNA-Seq data from divergent taxonomic groups. This method facilitates the retrieval and comparison of gene expression levels from published data sets using standardized assembly and analysis. CoRMAP does not rely on reference genomes and consequently facilitates direct comparison between diverse studies on a range of organisms.

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools.

Simple SummaryMicroRNAs (miRNAs) have been under the spotlight for the last three decades. These non-coding RNAs seem to be dynamic regulators of mRNA stability and translation, in addition to interfering with transcription. Circulating miRNAs play a critical role in cell-to-cell interplay; therefore, they can serve as disease biomarkers. Meta-analysis of published data revealed that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against acute lymphoblastic leukemia (ALL) in children. Reanalysis of small RNA-seq data with novel tools identified significantly overexpressed members of the miR-128, miR-181, miR-130 and miR-17 families and significantly lower expression of miR-30, miR-24-2 and miR143~145 clusters, miR-574 and miR-618 in pediatric T-ALL cases compared with controls. Inconsistencies in methodology and study designs in most published material preclude reproducibility, and further cohort studies need to be conducted in order to empower novel tools, such as ALLSorts and RNAseqCNV.MicroRNAs (miRNAs) have been implicated in childhood acute lymphoblastic leukemia (ALL) pathogenesis. We performed a systematic review and meta-analysis of miRNA single-nucleotide polymorphisms (SNPs) in childhood ALL compared with healthy children, which revealed (i) that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against ALL occurrence in children; (ii) no significant association between rs2910164 genotypes in miR-146a and childhood ALL; and (iii) SNPs in DROSHA, miR-449b, miR-938, miR-3117 and miR-3689d-2 genes seem to be associated with susceptibility to B-ALL in childhood. A review of published literature on differential expression of miRNAs in children with ALL compared with controls revealed a significant upregulation of the miR-128 family, miR-130b, miR-155, miR-181 family, miR-210, miR-222, miR-363 and miR-708, along with significant downregulation of miR-143 and miR-148a, seem to have a definite role in childhood ALL development. MicroRNA signatures among childhood ALL subtypes, along with differential miRNA expression patterns between B-ALL and T-ALL cases, were scrutinized. With respect to T-ALL pediatric cases, we reanalyzed RNA-seq datasets with a robust and sensitive pipeline and confirmed the significant differential expression of hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-92a-2-5p, hsa-miR-128-3p (ranked first), hsa-miR-130b-3p and -5p, hsa-miR-181a-5p, -2-3p and -3p, hsa-miR-181b-5p and -3p, hsa-miR-145-5p and hsa-miR-574-3p, as described in the literature, along with novel identified miRNAs.

A KBase case study on genome-wide transcriptomics and plant primary metabolism in response to drought stress in Sorghum.

A better understanding of the genetic and metabolic mechanisms that confer stress resistance and tolerance in plants is key to engineering new crops through advanced breeding technologies. This requires a systems biology approach that builds on a genome-wide understanding of the regulation of gene expression, plant metabolism, physiology and growth. In this study, we examine the response to drought stress in Sorghum, as we leverage the tools for transcriptomics and plant metabolic modeling we have implemented at the U.S. Department of Energy Systems Biology Knowledgebase (KBase). KBase enables researchers worldwide to collaborate and advance research by uploading private or public data into the KBase Narrative Interface, analyzing it using a rich, extensible array of computational and data-analytics tools, and securely sharing scientific workflows and conclusions. We demonstrate how to use the current RNA-seq tools in KBase, applicable to both plants and microbes, to assemble and quantify long transcripts and identify differentially expressed genes effectively. More specifically, we demonstrate the utility of the platform by identifying key genes differentially expressed during drought-stress in Sorghum bicolor, an important sustainable production crop plant. We then show how we can use KBase tools to predict the membership of genes in metabolic pathways and examine expression data in the context of metabolic subsystems. We demonstrate the power of the platform by making the data, analysis and interpretation available to the biologists in the reproducible, re-usable, point-and-click format of a KBase Narrative thus promoting FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship.

Pyrpipe: a Python package for RNA-Seq workflows.

The availability of terabytes of RNA-Seq data and continuous emergence of new analysis tools, enable unprecedented biological insight. There is a pressing requirement for a framework that allows for fast, efficient, manageable, and reproducible RNA-Seq analysis. We have developed a Python package, (pyrpipe), that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in Python, in an object-oriented manner. pyrpipe provides access to popular RNA-Seq tools, within Python, via high-level APIs. Pipelines can be customized by integrating new Python code, third-party programs, or Python libraries. Users can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments, and enabling efficient resource management. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in Python and is compatible with Python versions 3.6 and higher. To illustrate the rich functionality of pyrpipe, we provide case studies using RNA-Seq data from GTEx, SARS-CoV-2-infected human cells, and Zea mays. All source code is freely available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source, from PyPI (https://pypi.org/project/pyrpipe), or from bioconda (https://anaconda.org/bioconda/pyrpipe). Documentation is available at (http://pyrpipe.rtfd.io).

Identifying suitable tools for variant detection and differential gene expression using RNA-seq data

Neurodegenerative diseases are the most predominate brain disorders around the globe and the affected populations are rapidly increasing. Recently, these diseases have been addressed using the data obtained from RNA-sequencing technology to reveal the changes in gene/transcript expression, effect of variants, and pathways involved in disease mechanisms. However, the observations mainly depend on the aligners/tools and the performance of existing RNA-seq tools on hg38 genome assembly has not yet been documented. In this study, we performed a systematic analysis of various spliced aligners, transcript assembling and variant calling tools based on both genomic assemblies (hg19/hg38) from hippocampus brain tissue. This helps to identify the best possible combination tools for hg38 annotation. In order to evaluate the identified variants from various pipelines, we compared them with expression Quantitative Trait Loci (eQTL) and Genome-Wide Association Study (GWAS). In addition, the identified differentially expressed genes (DG) were compared with microarray studies. From our analysis of variant calling, the combination of GATK (Genome Analysis Tool-kit) and STAR (Spliced Transcripts Alignment to a Reference) protocol yields a larger number of GWAS/eQTL variants compared to SAMtools (Sequence Alignment Map). We also identified a higher number of non-coding variants in hg38 compared to hg19 due to enhanced annotation. In the case of various DG pipelines, we found that the Salmon-based hg38 transcriptomic quantification yields a higher number of reported DG compared to other genome-based quantification methods. This study revealed that higher number of reads maps to multiple location of the genome with hg38 compared to hg19, and these spurious multi-mapped reads may affect the gene quantification techniques. We suggest that it is necessary to develop efficient algorithms, which can handle the multi-mapped reads and improve the performance of genome-based alignment quantification.

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

Abstract 2585: viGEN: An open source bioinformatics pipeline for viral RNA detection and quantification in human tumor samples

Abstract We present a novel pipeline for viral RNA detection and quantification in human RNA-seq data. Our pipeline has been tested on the TCGA liver cancer cohort, can not only detect the presence of a viral species, but also provide gene level read counts for individual viral species and extract viral-variants. Introduction Approximately 20% of human cancer types are associated with viral infection that is routinely detected in blood samples. However the extent and biological significance of viral presence/infection in actual tumor samples is generally unknown but could be measured using existing Human RNA-seq data from tumor samples. We have developed a bioinformatics pipeline viGEN combining existing and novel RNAseq tools that allows for detection and quantification of viral RNA in human RNAseq data. Methods The pipeline includes 4 major modules: The first module allows to align and filter out human RNA sequences; second module maps and count (remaining un-aligned) reads against reference genomes of all known and sequenced human viruses; the third module calculates quantitate read counts at the individual viral genes level thus allowing for downstream differential expression analysis of viral genes between experimental and controls groups. The fourth module calls variants in these viruses. To the best of our knowledge there are no publicly available pipelines or packages that would provide this type of complete analysis in one package. Customized solutions have been reported in the literature however were not made public. Results We used this pipeline to examine viruses present in RNA-seq data from 75 liver cancer patients in the TCGA data collection. Our pipeline allows conducting quantitative analysis at the gene level for visualization and detection of statistically significant differentially expressed viral genes between groups of patients known to be infected with both HBV and various subtypes of HCV. Once the viral genomes are detected at the genome level, we examine the differences between “Dead” and “Alive” samples at the viral-transcript level, and at the viral-variant level. Conclusion Our results show that it is possible to detect viral sequences from whole-transcriptome (RNA-seq) data in humans. We were able to not only quantify them at a viral-gene/CDS level, but also extract variants from the 75-sample dataset from TCGA. The results presented here are in correspondence with published literature and are a proof of concept of our pipeline. This pipeline can be used on cancer and non-cancer human RNA-seq or other NGS data to provide additional insights into the biological significance of viral infection in complex diseases, tumorigeneses and cancer immunology. Citation Format: Krithika Bhuvaneshwar, Lei Song, Yuriy Gusev. viGEN: An open source bioinformatics pipeline for viral RNA detection and quantification in human tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2585. doi:10.1158/1538-7445.AM2017-2585

Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species.

BackgroundRemarkable advances in Next Generation Sequencing (NGS) technologies, bioinformatics algorithms and computational technologies have significantly accelerated genomic research. However, complicated NGS data analysis still remains as a major bottleneck. RNA-seq, as one of the major area in the NGS field, also confronts great challenges in data analysis.ResultsTo address the challenges in RNA-seq data analysis, we developed a web portal that offers three integrated workflows that can perform end-to-end compute and analysis, including sequence quality control, read-mapping, transcriptome assembly, reconstruction and quantification, and differential analysis. The first workflow utilizes Tuxedo (Tophat, Cufflink, Cuffmerge and Cuffdiff suite of tools). The second workflow deploys Trinity for de novo assembly and uses RSEM for transcript quantification and EdgeR for differential analysis. The third combines STAR, RSEM, and EdgeR for data analysis. All these workflows support multiple samples and multiple groups of samples and perform differential analysis between groups in a single workflow job submission. The calculated results are available for download and post-analysis. The supported animal species include chicken, cow, duck, goat, pig, horse, rabbit, sheep, turkey, as well as several other model organisms including yeast, C. elegans, Drosophila, and human, with genomic sequences and annotations obtained from ENSEMBL.The RNA-seq portal is freely available from http://weizhongli-lab.org/RNA-seq.ConclusionsThe web portal offers not only bioinformatics software, workflows, computation and reference data, but also an integrated environment for complex RNA-seq data analysis for agricultural animal species. In this project, our aim is not to develop new RNA-seq tools, but to build web workflows for using popular existing RNA-seq methods and make these tools more accessible to the communities.

SePIA: RNA and small RNA sequence processing, integration, and analysis.

BackgroundLarge-scale sequencing experiments are complex and require a wide spectrum of computational tools to extract and interpret relevant biological information. This is especially true in projects where individual processing and integrated analysis of both small RNA and complementary RNA data is needed. Such studies would benefit from a computational workflow that is easy to implement and standardizes the processing and analysis of both sequenced data types.ResultsWe developed SePIA (Sequence Processing, Integration, and Analysis), a comprehensive small RNA and RNA workflow. It provides ready execution for over 20 commonly known RNA-seq tools on top of an established workflow engine and provides dynamic pipeline architecture to manage, individually analyze, and integrate both small RNA and RNA data. Implementation with Docker makes SePIA portable and easy to run. We demonstrate the workflow’s extensive utility with two case studies involving three breast cancer datasets. SePIA is straightforward to configure and organizes results into a perusable HTML report. Furthermore, the underlying pipeline engine supports computational resource management for optimal performance.ConclusionSePIA is an open-source workflow introducing standardized processing and analysis of RNA and small RNA data. SePIA’s modular design enables robust customization to a given experiment while maintaining overall workflow structure. It is available at http://anduril.org/sepia.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0099-z) contains supplementary material, which is available to authorized users.

RNASeqGUI: a GUI for analysing RNA-Seq data.

We present RNASeqGUI R package, a graphical user interface (GUI) for the identification of differentially expressed genes across multiple biological conditions. This R package includes some well-known RNA-Seq tools, available at www.bioconductor.org. RNASeqGUI package is not just a collection of some known methods and functions, but it is designed to guide the user during the entire analysis process. RNASeqGUI package is mainly addressed to those users who have little experience with command-line software. Therefore, thanks to RNASeqGUI, they can conduct analogous analyses using this simple graphical interface. Moreover, RNASeqGUI is also helpful for those who are expert R-users because it speeds up the usage of the included RNASeq methods drastically. RNASeqGUI package needs the RGTK2 graphical library to run. This package is open source and is freely available under General Public License at http://bioinfo.na.iac.cnr.it/RNASeqGUI/Download. Supplementary data are available at Bioinformatics online.

Quantitative transcriptome analysis using RNA-seq.

RNA-seq has emerged as the technology of choice to quantify gene expression. This technology is a convenient accurate tool to quantify diurnal changes in gene expression, gene discovery, differential use of promoters, and splice variants for all genes expressed in a single tissue. Thus, RNA-seq experiments provide sequence information and absolute expression values about transcripts in addition to relative quantification available with microarrays or qRT-PCR. The depth of information by sequencing requires careful assessment of RNA intactness and DNA contamination. Although the RNA-seq is comparatively recent, a standard analysis framework has emerged with the packages of Bowtie2, TopHat, and Cufflinks. With rising popularity of RNA-seq tools have become manageable for researchers without much bioinformatical knowledge or programming skills. Here, we present a workflow for a RNA-seq experiment from experimental planning to biological data extraction.