Rodrigo Santamaría

BicOverlapper: A tool for bicluster visualization

UNLABELLED BicOverlapper is a tool to visualize biclusters from gene-expression matrices in a way that helps to compare biclustering methods, to unravel trends and to highlight relevant genes and conditions. A visual approach can complement biological and statistical analysis and reduce the time spent by specialists interpreting the results of biclustering algorithms. The technique is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. AVAILABILITY The BicOverlapper software and supplementary material are available at http://vis.usal.es/bicoverlapper

A visual analytics approach for understanding biclustering results from microarray data.

BackgroundMicroarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results.ResultsWe present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapperConclusionThe main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.

APID interactomes: providing proteome-based interactomes with controlled quality for multiple species and derived networks

APID (Agile Protein Interactomes DataServer) is an interactive web server that provides unified generation and delivery of protein interactomes mapped to their respective proteomes. This resource is a new, fully redesigned server that includes a comprehensive collection of protein interactomes for more than 400 organisms (25 of which include more than 500 interactions) produced by the integration of only experimentally validated protein–protein physical interactions. For each protein–protein interaction (PPI) the server includes currently reported information about its experimental validation to allow selection and filtering at different quality levels. As a whole, it provides easy access to the interactomes from specific species and includes a global uniform compendium of 90,379 distinct proteins and 678,441 singular interactions. APID integrates and unifies PPIs from major primary databases of molecular interactions, from other specific repositories and also from experimentally resolved 3D structures of protein complexes where more than two proteins were identified. For this purpose, a collection of 8,388 structures were analyzed to identify specific PPIs. APID also includes a new graph tool (based on Cytoscape.js) for visualization and interactive analyses of PPI networks. The server does not require registration and it is freely available for use at http://apid.dep.usal.es.

Systems biology of infectious diseases: a focus on fungal infections.

The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.

TagClusters: Semantic Aggregation of Collaborative Tags beyond TagClouds

TagClouds is a popular visualization for the collaborative tags. However it has some instinct problems such as linguistic issues, high semantic density and poor understanding of hierarchical structure and semantic relation between tags. In this paper we investigate the ways to support semantic understanding of collaborative tags and propose an improved visualization named TagClusters. Based on the semantic analysis of the collaborative tags in Last.fm, the semantic similar tags are clustered into different groups and the visual distance represents the semantic similarity between tags, and thus the visualization offers a better semantic understanding of collaborative tags. A comparative evaluation is conducted with TagClouds and TagClusters based on the same tags collection. The results indicate that TagClusters has advantages in supporting efficient browsing, searching, impression formation and matching. In the future work, we will explore the possibilities of supporting tag recom-mendation and tag-based Music Retrieval based on TagClusters.

Overlapping Clustered Graphs: Co-authorship Networks Visualization

The analysis of scientific articles produced by different groups of authors helps to identify and characterize research groups and collaborations among them. Although this is a quite studied area, some issues, such as quick understanding of groups and visualization of large social networks still pose some interesting challenges. In order to contribute to this study, we present a solution based in Overlapper, a tool for the visualization of overlapping groups that makes use of an enhanced variation of force-directed graphs. For a real case study, the tool has been applied to articles in the DBLP database.

RUN and FYVE domain–containing protein 4 enhances autophagy and lysosome tethering in response to Interleukin-4

Interleukin-4 boosts the capacity of dendritic cells to present endogenous antigens on MHC II and to resist bacterial infection through a mechanism shown to be partially dependent on RUFY4 expression.

Low level genome mistranslations deregulate the transcriptome and translatome and generate proteotoxic stress in yeast

BackgroundOrganisms use highly accurate molecular processes to transcribe their genes and a variety of mRNA quality control and ribosome proofreading mechanisms to maintain intact the fidelity of genetic information flow. Despite this, low level gene translational errors induced by mutations and environmental factors cause neurodegeneration and premature death in mice and mitochondrial disorders in humans. Paradoxically, such errors can generate advantageous phenotypic diversity in fungi and bacteria through poorly understood molecular processes.ResultsIn order to clarify the biological relevance of gene translational errors we have engineered codon misreading in yeast and used profiling of total and polysome-associated mRNAs, molecular and biochemical tools to characterize the recombinant cells. We demonstrate here that gene translational errors, which have negligible impact on yeast growth rate down-regulate protein synthesis, activate the unfolded protein response and environmental stress response pathways, and down-regulate chaperones linked to ribosomes.ConclusionsWe provide the first global view of transcriptional and post-transcriptional responses to global gene translational errors and we postulate that they cause gradual cell degeneration through synergistic effects of overloading protein quality control systems and deregulation of protein synthesis, but generate adaptive phenotypes in unicellular organisms through activation of stress cross-protection. We conclude that these genome wide gene translational infidelities can be degenerative or adaptive depending on cellular context and physiological condition.

Methods to bicluster validation and comparison in microarray data

There are lots of validation indexes and techniques to study clustering results. Biclustering algorithms have been applied in Systems Biology, principally in DNA Microarray analysis, for the last years, with great success. Nowadays, there is a big set of biclustering algorithms each one based in different concepts, but there are few intercomparisons that measure their performance. We review and present here some numerical measures, new and evolved from traditional clustering validation techniques, to allow comparisons and validation of biclustering algorithms.

BicOverlapper 2.0: Visual Analysis for Gene Expression

Motivation: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. Results: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. Availability and implementation: BicOverlapper 2.0 is available at: http://vis.usal.es/bicoverlapper2 Contact: rodri@usal.es