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CoGAPS / Coordinated Gene Activity in Pattern Sets
An R/C++ package to identify patterns and biological process activity in transcriptomic data. CoGAPS provides an integrated package for isolating gene expression driven by a biological process, enhancing inference of biological processes from transcriptomic data. It improves on other enrichment measurement methods by combining a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS) with a threshold-independent statistic inferring activity on gene sets. coGAPS infers biological activity by identifying overlapping, coregulated sets of genes and applying Z-score based statistics. It can be used to isolate transcription factor (TF) or BP activity in datasets of thousands of genes and tens to thousands of samples. The software is provided as open source C++ code built on top of JAGS software with an R interface.
Maps the functional networks of long or circular forms of non-coding RNAs (ncRNA). circlncRNAnet allows in-depth analyses of ncRNA biology. The software provides three features: (1) a framework for processing of user-defined next-generation sequencing (NGS)-based expression data, (2) assigning and assessing the regulatory networks of ncRNAs selected by users and (3) a workflow suitable for all types of ncRNAs. It can be used to get multiple lines of functionally relevant information on the circular RNA/ long non-coding RNA (circRNAs/lncRNAs) of users’ interest.
COMAN / Comprehensive Metatranscriptomics Analysis
Processes uploaded raw reads automatically to ultimately achieve functional assignments. COMAN is a web-based application for functional characterisation and comprehensive analysis of high-throughput metatranscriptomic data. It serves as a platform to translate the non-interpretable raw sequencing reads to data tables and high-standard figures that can be easily handled and further analysed. This web app can be run by experimentalists without programming experience and without the hassle of changing tools or working environments for answering their biologically relevant questions.
AMEN / Annotation Mapping Expression and Network
Enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein -protein interaction (PPI), expression profiling and proteomics data. AMEN provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. AMEN facilitates the design and execution of optimized procedures for processing, analysis and interpretation of multifaceted high-throughput data.
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