Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.
An R package that simplifies the processing of RNA sequencing data, hiding the complex interplay of the required packages behind a single functionality. easyRNASeq calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.
A command-line software program to go from a de novo transcriptome assembly to gene-level counts. Corset takes a set of reads that have been multi-mapped to the transcriptome (where multiple alignments per read were reported) and hierarchically clusters the transcripts based on the proportion of shared reads and expression patterns. It will report the clusters and gene-level counts for each sample, which are easily tested for differential expression with count based tools such as edgeR and DESeq.
Identifies templated and non-templated 5’-isomiRs and 3’-isomiRs. isomiR2Function allows the expression quantification using empirical bayes hierarchal model (EBSeq) and/or negative binomial distribution (DESeq). It permits to customize length range, seed corrupt tolerance and minimum sequencing depth for isomiR identification. The tool facilitates the large-scale discovery of isomiRs across the plant species.
Analyzes RNA-seq data sources. Consexpression identifies differentially expressed genes (DEG) from RNA-seq data. This software offers a consensus based on nine methods (baySeq, DESeq, EBSeq, edgeR, limma+voom, NOIseq, SAMseq, DESeq2, sleuth) for the evaluation of gene expression and returns the differential expressed genes indicate via an integrated consensus option built with five DEG identification methods.
Allows users to perform differential expression (DE) analysis of tag count data. The Tag Count Comparison purpose is to reduce potential differentially expressed genes (DEGs) before performing the data normalization. It provides tools to perform multi-step normalization methods based on DEG elimination strategy (DEGES). This tool supplies a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq.
Automates all the steps in file preparation, computation and result comparison. DEB is an online pipeline that integrates three algorithms into one place: (i) EdgeR designed for the analysis of replicated count-based expression data, (ii) DESeq, similar to edgeR, and (iii) bayseq, which assumes a negative binomial distribution for the data and derives an empirically determined prior distribution from the entire dataset.
Provides efficient gene-permuting gene-set enrichment analysis (GSEA) methods for small replicate RNA-seq data. AbsFilterGSEA provides three modes of gene-permuting GSEA methods: (i) original two-tailed GSEA, (ii) absolute one-tailed GSEA and (iii) the ordinary GSEA filtered with absolute GSEA results. It accepts a raw read count matrix and normalizes it using a DESeq median method. It also accepts an already normalized dataset. The core GSEA was coded with C++ and the results were cross-checked with those from the original GSEA R-code.
A user-friendly platform for performance evaluation of differential expression analysis based on RNA-seq data. DEAR-O currently includes four of the most popular tools: DESeq, DESeq2, edgeR and Cuffdiff2. Based on the DEAR-O platform, researchers can evaluate the performance of different tools, or the same tool with different versions, with a customised number of biological replicates using already curated RNA-seq datasets. We also initiated an online forum for discussion of RNA-seq differential expression analysis. Through this forum, new useful tools and benchmarking datasets can be introduced.
Includes functions to build restriction enzyme cut site (RECS) map, and distributes mapped sequences on the map with five different approaches. REDseq is a Bioconductor package for building genomic map of restriction enzyme sites REmap, assigning sequencing tags to RE sites using five different strategies, visualizing genome-wide distribution of differentially cut regions with the REmap as reference and the distance distribution of sequence tags to corresponding RE sites, generating count table for identifying statistically significant RE sites using edgeR or DEseq.
Identify potential interactions of miRNA-target gene interactions from miRNA and mRNA expression data. AnamiR contains functions for statistical test, databases of miRNA-target gene interaction and functional analysis. The anamiR package provides a whole workflow, which contains important functions like “normalization” used to normalize the expression data with one of three methods, including normal, quantile, rank.invariant, “differExp_discrete” used to find the differential genes or miRNAs from given expression data with one of three statistical methods, including t.test, wilcox. test,limma and DESeq or “miR_converter” to convert the older miRNA annotation to the miRBase 21 version.
PhD ès Neurosciences, I worked 8 years on the brain and its diseases. I then specialized in bioinformatics (NGS, epigenetics) and worked in CEA and GENETHON before to join OMICX and help OMICtools community.
I am Dr. Madhu Sudhana Saddala working as a Postdoctoral Fellow in Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, USA. Currently working on NGS data analysis of retinal and macular disease of eye.