SUMMARY: RaNA-Seq is a cloud platform for the rapid analysis and visualization of RNA-Seq data. It performs a full analysis in minutes by quantifying FASTQ files, calculating quality control metrics, running differential expression analyses and enabling the explanation of results with functional analyses. Our analysis pipeline applies generally accepted and reproducible protocols that can be applied with two simple steps in its web interface. Analysis results are presented as interactive graphics and reports, ready for their interpretation and publication. AVAILABILITY: RaNA-Seq web service is freely available online at https://ranaseq.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

SUMMARY: Non-ribosomal peptide synthetases (NRPSs) are multi-modular enzymes, which biosynthesize many important peptide compounds produced by bacteria and fungi. Some studies have revealed that an individual domain within the NRPSs shows significant substrate selectivity. The discovery and characterization of non-ribosomal peptides are of great interest for the biotechnological industries. We have applied computational mining methods in order to build a database of NRPSs modules that bind to specific substrates. We have used this database to build a hidden Markov model predictor of substrates that bind to a given NRPS. AVAILABILITY: The database and the predictor are freely available on an easy-to-use website at www.nrpssp.com. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

MOTIVATION: Exploration and analysis of interactome networks at systems level requires unification of the biomolecular elements and annotations that come from many different high-throughput or small-scale proteomic experiments. Only such integration can provide a non-redundant and consistent identification of proteins and interactions. APID2NET is a new tool that works with Cytoscape to allow surfing unified interactome data by querying APID server (http://bioinfow.dep.usal.es/apid/) to provide interactive analysis of protein-protein interaction (PPI) networks. The program is designed to visualize, explore and analyze the proteins and interactions retrieved, including the annotations and attributes associated to them, such as: GO terms, InterPro domains, experimental methods that validate each interaction, PubMed IDs, UniProt IDs, etc. The tool provides interactive graphical representation of the networks with all Cytoscape capabilities, plus new automatic tools to find concurrent functional and structural attributes along all protein pairs in a network. AVAILABILITY: http://bioinfow.dep.usal.es/apid/apid2net.html. SUPPLEMENTARY INFORMATION: Installation Guide and User's Guide are supplied at the Web site indicated above.

MOTIVATION: Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better. RESULTS: We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.