One of the main goals of RNA-seq experiments is to identify the differentially expressed genes in two or more conditions. Such genes are selected based on a combination of expression change threshold and score cutoff, which are usually based on P values generated by statistical modeling. Normalization in the context of differential analysis is essential in order to account for the presence of systematic variation between samples as well as differences in library composition.
Assembles transcripts, estimates their abundances, and tests for differential expression and regulation in RNA-Seq samples. Cufflinks assembles individual transcripts from RNA-seq reads that have been aligned to the genome. This software is able to infer the splicing structure of each gene because reads from multiple splice variants for a given gene can be found in a sample. Quantification of transcript abundances is also possible by preferring a reference annotation to assembling the reads.
Integrates workflow technology and in-built access to bioinformatics resources including remote data warehouses and tools. Galaxy permits users without programming skills to conduct computational analysis through the Web. It builds a succession of tools to perform multistep studies and is able to conserve the complete provenance of each analysis step. This platform offers drag and drop functionalities to ease the construction of workflows.
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.
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.
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.
A system to provide a flexible and usable Web environment for defining and running bioinformatics analyses. It embeds simple yet powerful data management features that allow the user to reproduce analyses and to combine tools using a hierarchical typing system. Mobyle offers invocation of services distributed over remote Mobyle servers, thus enabling a federated network of curated bioinformatics portals without the user having to learn complex concepts or to install sophisticated software.
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.