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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.

edgeR / empirical analysis of DGE in R

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Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.


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.


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A program to enable the visualisation and analysis of mapped sequence data. SeqMonk was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are: (i) Import of mapped data from mapped data (BAM/SAM/bowtie etc), (ii) Creation of data groups for visualisation and analysis, (iii) Visualisation of mapped regions against an annotated genom, (iv) Flexible quantitation of the mapped data to allow comparisons between data sets, (v) Statistical analysis of data to find regions of interest and (vi) Creation of reports containing data and genome annotation.


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.


A package based on a statistical method for detecting DMRs from WGBS (Whole Genome Bisulfite Sequencing) data without replicates. A key feature of DSS-single is to estimate biological variation when replicated data are not available. The method takes advantage of the spatial correlation of methylation levels: since the methylation levels from nearby CpG sites are similar, we can use nearby CpG sites as ‘pseudo-replicates’ to estimate dispersion. Simulations demonstrate that DSS-single has greater sensitivity and accuracy than existing methods, and an analysis of H1 versus IMR90 cell lines suggests that it also yields the most biologically meaningful results.


Allows user-friendly automated stage-wise analysis of high-throughput genomic data. stageR implements two-stage testing as a general paradigm for assessing high throughput experiments involving multiple hypotheses that can be aggregated. The procedure was optimized towards RNA-seq applications: differential transcript expression, differential transcript usage and differential gene expression analysis with simple and complex experimental designs. stageR achieves an optimal middle ground between biological resolution and statistical power while providing gene-level false discovery rate (FDR) control, which is beneficial for downstream biological interpretation and validation.


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.


Provides a linear model and normality based transformation method. Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIPseq count data or any large-scale count data. It transforms such datasets for parametric tests. Some pipelines are implemented: (i) library size/batch effect normalization, (ii) cell sub-population analysis and visualization, (iii) differential expression analysis or differential peak detection, (iv) highly variable gene discovery and visualization, (v) gene correlation network analysis and visualization, (vi) stable gene selection for scRNA-seq data and (vii) data imputation.


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.

EEGC / Engineering Evaluation by Gene Categorization

Evaluates cellular engineering processes in a systemic rather than marker-based fashion. EEGC integrates transcriptome profiling and functional analysis. It clusters genes into categories representing different states of (trans)differentiation. The tool performs functional and gene regulatory network analyses for each of the categories of the engineered cells, thus offering practical indications on the potential lack of the reprogramming protocol.


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 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.

TRAPLINE / Transparent Reproducible and Automated PipeLINE

Serves for RNAseq data processing, evaluation and prediction. TRAPLINE guides researchers through the NGS data analysis process in a transparent and automated state-of-the-art pipeline. It can detect protein-protein interactions (PPIs), miRNA targets and alternatively splicing variants or promoter enriched sites. This tool includes different modules for several functions: (1) it scans the list of differentially expressed genes; (2) it includes modules for miRNA target prediction; and (3) a module is implemented to identify verified interactions between proteins of significantly upregulated and downregulated mRNAs.


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.

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.

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.


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.