SMART-RDA: A Galaxy Workflow for RNA-Seq Data Analysis

Differential Expression Analysis for RNA-Seq Data

RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conc...

Dynamic Model for RNA-seq Data Analysis

By measuring messenger RNA levels for all genes in a sample, RNA-seq provides an attractive option to characterize the global changes in transcription. RNA-seq is becoming the widely used platform for gene expression profiling. However, real transcription signals in the RNA-seq data are confounded with measurement and sequencing errors and other random biological/technical variation. To extract...

Guidelines for RNA-Seq data analysis

RseqFlow: workflows for RNA-Seq data analysis

SUMMARY We have developed an RNA-Seq analysis workflow for single-ended Illumina reads, termed RseqFlow. This workflow includes a set of analytic functions, such as quality control for sequencing data, signal tracks of mapped reads, calculation of expression levels, identification of differentially expressed genes and coding SNPs calling. This workflow is formalized and managed by the Pegasus W...

Bioinformatics for RNA‐Seq Data Analysis

While RNA sequencing (RNA‐seq) has become increasingly popular for transcrip‐ tome profiling, the analysis of the massive amount of data generated by large‐scale RNA‐seq still remains a challenge. RNA‐seq data analyses typically consist of (1) accurate mapping of millions of short sequencing reads to a reference genome, including the identification of splicing events; (2) quantifying expression...