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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.

limma / Linear Models for Microarray Data

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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.

BitSeq / Bayesian inference of transcripts from sequencing data

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An application for inferring expression levels of individual transcripts from sequencing (RNA-Seq) data and estimating differential expression (DE) between conditions. An advantage of this approach is the ability to account for both technical uncertainty and intrinsic biological variance in order to avoid false DE calls. The technical contribution to the uncertainty comes both from finite read-depth and the possibly ambiguous mapping of reads to multiple transcripts.


Estimates expression at transcript level resolution and controls for variability evident across replicate libraries. Cuffdiff is an algorithm that detects differentially expressed transcripts and genes and reveals differential splicing and promoter-preference changes. It can identify genes that display altered inclusion of key features, such as DNA binding regions, in their protein products. Moreover, this software is able to generate accurate transcript-resolution estimates of changes in gene expression.


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Assists users in normalization, testing, and false discovery rate (FDR) estimation for RNA-sequencing data. PoissonSeq can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. This software is based on a Poisson log-linear model to achieve the sequencing data of general outcome type. The estimation method for FDR eliminates non-null genes from the pooled permutation of the assessment.


Analyzes parallel RNA sequence data to catalog transcripts and assess differential and alternative expression of known and predicted mRNA isoforms in cells and tissues. ALEXA-Seq comprises several functions: (1) creation of a database of expression and alternative expression sequence ‘features’, (2) mapping of short paired-end sequence reads to these features, (3) identification of features that are expressed above background noise while taking into account locus-by-locus noise, or (4) identification of features that are differentially expressed in samples.


Identifies differentially expressed genes from count data or previously normalized count data. NOISeq empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition. This reference distribution is then used to assess whether the M-D values computed between two conditions for a given gene are likely to be part of the noise or represent a true differential expression.


Serves for the functional analysis of gene expression and genomic data. Babelomics offers the possibility to explore the effects of alteration in gene expression levels or changes in genes sequences within a functional context. It provides user-friendly access to a full range of methods that cover: (1) primary data analysis; (2) a variety of tests for different experimental designs; and (3) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context.


Improves the annotation-agnostic approach to RNA-seq analysis by: (1) implementing a computationally efficient bump-hunting approach to identify DERs which permits genome-scale analyses in a large number of samples, (2) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (3) introducing a new set of data visualizations for expressed region analysis. derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis.


Facilitates analysis of microarrays and miRNA/RNA-seq data on laptops. oneChannelGUI can be used for quality control, normalization, filtering, statistical validation and data mining for single channel microarrays. It offers a comprehensive microarray analysis for Affymetrix 3′ (IVT) expression arrays as well as for the new generation of whole transcript arrays: human/mouse/rat exon 1.0 ST and human gene 1.0 ST arrays. oneChannelGUI inherits the core affylmGUI functionalities and permits a wider range of analysis allowing biologists to choose among different criteria and algorithms in order to analyze their data. It is a didactical tool since it could be used to introduce young life scientists to the use and interpretation of microarray data. For this purpose various data sets and exercises are available at the oneChannelGUI web site.


A collection of small RNA analysis tools. sRNAtoolbox is aimed to provide small RNA researchers with several useful tools including sRNA expression profiling from deep sequencing experiments and several downstream analysis tools. The center piece of sRNAtoolbox is sRNAbench, which allows the expression profiling and prediction of novel microRNAs in deep sequencing experiments. The other tools can be either launched on sRNAbench results, or independently using the appropriate file formats.


Simplifies the analysis for researchers less familiar with modelling and construction of design matrices, and issues such as outlier detection are handled automatically. BBSeq is an R package that performs likelihood ratio comparisons for the overall statistical significance of each included factor. This module is designed to make it easy to perform testing for a variety of experimental designs, with modifications for small sample sizes to take advantage of the mean–overdispersion relationship.

DEIVA / Differential Gene Expression Interactive Visual Analysis

Enables locating and identifying single or multiple genes in an immediate, interactive, and intuitive manner. DEIVA offers a unique combination of design decisions that enable inspection and analysis of differential gene expression (DGE) statistical test results with an emphasis on ease of use. DEIVA is available for local use or as a web application to interactively identify and locate genes in a hexbin or scatter plot of DESeq2 or edgeR results.


Detects and visualizes of differential alternative transcription. DiffSplice is an ab initio method to detect alternative splicing isoforms that are differentially expressed under different conditions using high-throughput RNA-seq reads. This software directly localizes where differential splicing occurs, making it easier to identify exons involved in alternative transcription. It estimates the relative proportion of alternative transcription flows in every ASM and calculates the Jensen–Shannon divergence (JSD) to quantify the difference in transcription between samples.


A differential transcript expression (DTE) analysis algorithm. SDEAP estimates the number of conditions directly from the input samples using a Dirichlet mixture model and discovers alternative splicing events using a new graph modular decomposition algorithm. By taking advantage of the above technical improvement, SDEAP was able to outperform the other DTE analysis methods in extensive experiments on simulated data and real data with qPCR validation. The prediction of SDEAP also allows users to classify the samples of cancer subtypes and cell-cycle phases more accurately.


Allows users to characterize and quantify the set of all RNA molecules produced in cells. RseqFlow contains several modules that include: mapping reads to genome and transcriptome references, performing quality control (QC) of sequencing data, generating files for visualizing signal tracks based on the mapping results, calculating gene expression levels, identifying differentially expressed genes, calling coding single nucleotide polymorphisms (SNPs) and producing MRF and BAM files.


A comprehensive and user-friendly system for computational analysis of bacterial RNA-seq data. As input, Rockhopper takes RNA sequencing reads output by high-throughput sequencing technology (FASTQ, QSEQ, FASTA, SAM, or BAM files). Rockhopper supports the following tasks: reference based transcript assembly; de novo transcript assembly; normalizing data from different experiments; quantifying transcript abundance; testing for differential gene expression; characterizing operon structures; visualizing results in a genome browser.

RNA CoMPASS / RNA Comprehensive Multi-Processor Analysis System for Sequencing

Analyzes exogenous and human sequences from RNAseq data. RNA CoMPASS is a parallel computation pipeline that provides a graphic user interface built from several open-source programs such as Novoalign and SAMMate. The application reads both the unmapped reads for pathogen discovery and the mapped reads for host transcriptome analysis. The program supports files generated from single-end, paired-end, and/or directional sequencing strategies.

ABSSeq / ABSolute differences of RNA-Seq data

A RNA-Seq analysis method based on modelling absolute expression differences. ABSSeq infers differential gene expression (DE) through the absolute differences in gene expression and assumes the differences to be influenced by two sources of variation: that found for average gene expression levels and that found for the magnitude of differential expression. Our approach employs a NB distribution to model these two parts and, as a consequence, it is able to detect DE genes more effectively than existing methods, as demonstrated by our analysis of both real and simulated data.

RNAontheBENCH / RNA on the BENCHmark of expression by nCounter hybridisation

Benchmarks RNAseq analysis methods. RNAontheBENCH is a computational resource that harnesses some features: (i) a RNAseq dataset from 12 human induced pluripotent stem cell lines, (ii) a panel of 150 genes representative of the transcriptome, (iii) a set of genetically-determined, internal controls, (iv) a further validation dataset for which the same RNA extraction, (v) the reanalyses of the Sequencing Quality Control (SEQC) dataset, (vi) accuracy metrics based on relative expression across samples, and (vii) an in silico dataset specifically designed to further establish the accuracy of relative expression at the transcript level.


Tests significance of clustering in RNA-seq data. SigFuge allows to identify genomic loci exhibiting differential transcription patterns across many RNA-seq samples. It combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. SigFuge is presented as a method capable of unsupervised discovery of differential isoform events in RNA-seq. This approach of studying gene expression as per-base expression curves along transcriptome coordinates makes it possible to identify differential events without strictly constraining the analysis to proposed exon or transcript boundaries.