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.
Simplifies quantitative investigation of comparative RNA-seq data. DESeq2 employs shrinkage estimators for dispersion and fold change. It counts the total number of reads that can be uniquely assigned to a gene. It serves for improved gene ranking and visualization, hypothesis tests above and below a threshold, and the regularized logarithm transformation for quality evaluation and clustering of over-dispersed count data. This version of DESeq uses shrinkage estimators for dispersion and fold change to ease quantitative analysis of comparative RNA-seq data.
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.
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.
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.
Offers a platform dedicated to features’ numbering or annotation of reads derived from both RNA-Seq and sRNA-seq. Mmannot provides a software able to consider multiple locations as well as to determine know many times features such as rRNAs or tRNAs have been sequenced. The application can generate files which are compatible with other software such as DESeq or EdgeR and can be run to investigate both single and paired-end reads.
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.
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.
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.
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.