Blast2GO® is an ALL in ONE tool for functional annotation of (novel) sequences and the analysis of annotation data.

Paintomics is a web tool for the integration and visualization of transcriptomics and metabolomics data.
Currently Paintomics supports integrated visualization of about one hundred top species of different biological kingdoms and offers user the possibility to request any other organism present in the KEGG database.

Qualimap is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data.

maSigPro is a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments.
The method has been implemented in the statistical language R and is freely available from the Bioconductor contributed packages repository

NOISeq is a non-parametric approach for the identification of 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.
NOISeq is available as a package in Bioconductor.

The STATegraEMS is an Experiment Management System (EMS) designed for storage and annotation of complex NGS and omics experiments. The STATegraEMS supports different types of sequencing-based assays, proteomics and metabolomics data.

SQANTI is an automated pipeline for the classification and quality-control evaluation of long-read transcriptomes. SQANTI allows the user to maximize the analytical outcome of long read technologies as PacBio by providing the tools to deliver quality-evaluated and curated full-length transcriptomes.

spongeScan is a web tool designed to identify and visualize lncRNAs acting as putative miRNA sponges, by searching for multiple miRNA binding sites in lncRNAs.

Method for the multivariate analysis of multifactorial gene expression datasets
Download R package
Readme file

Multivariate approach to correct batch effects in multifactorial gene expression datasets.
Download package
Read tutorial

MORE is a method which facilitate the task of applying GLMs models to multi-omic data. The goal of MORE is to model gene expression as a function of experimental variables, such as treatment, and the potential regulators of a given gene. MORE has a several functionalities to filter regulator: variable selection (Lasso, ridge regression and ElasticNet), filter for regulators with low variation, for missing data, for correlated regulators and stepwise methods.

RGmatch is a flexible and easy-to-use tool to match genomic regions to the closest gene (also transcript or exon), which provides the area of the gene where the region overlaps. The algorithm can be applied to any organism as long as the genome annotation is available.

In multi-omic experiments, MultiPower estimates the sample size needed to achieve a given statistical power per omic and globally. Parameters require to estimate power can be either set by users or estimated from provided pilot data (recommended). MultiPower accepts both count data or normally distributed data and assumes that the means of two populations are to be compared.