Education and training are two essential ingredients for a successful career. On one hand, universities provide students a curriculum for specializing in one's field of study, and on the other, internships complement coursework and provide invaluable training experience for a fruitful career. Consequently, undergraduates and graduates are encouraged to undertake an internship during the course of their degree. The opportunity to explore one's research interests in the early stages of their education is important for students because it improves their skill set and gives their career a boost. In the long term, this helps to close the gap between skills and employability among students across the globe and balance the research capacity in the field of computational biology. However, training opportunities are often scarce for computational biology students, particularly for those who reside in less-privileged regions. Aimed at helping students develop research and academic skills in computational biology and alleviating the divide across countries, the Student Council of the International Society for Computational Biology introduced its Internship Program in 2009. The Internship Program is committed to providing access to computational biology training, especially for students from developing regions, and improving competencies in the field. Here, we present how the Internship Program works and the impact of the internship opportunities so far, along with the challenges associated with this program.

SUMMARY: Determining genetic factors underlying various phenotypes is hindered by the involvement of multiple genes acting cooperatively. Over the past years, disease-gene prioritization has been central to identify genes implicated in human disorders. Special attention has been paid on using physical interactions between the proteins encoded by the genes to link them with diseases. Such methods exploit the guilt-by-association principle in the protein interaction network to uncover novel disease-gene associations. These methods rely on the proximity of a gene in the network to the genes associated with a phenotype and require a set of initial associations. Here, we present GUILDify, an easy-to-use web server for the phenotypic characterization of genes. GUILDify offers a prioritization approach based on the protein-protein interaction network where the initial phenotype-gene associations are retrieved via free text search on biological databases. GUILDify web server does not restrict the prioritization to any predefined phenotype, supports multiple species and accepts user-specified genes. It also prioritizes drugs based on the ranking of their targets, unleashing opportunities for repurposing drugs for novel therapies. AVAILABILITY AND IMPLEMENTATION: Available online at http://sbi.imim.es/GUILDify.php

This report summarizes the scientific content and activities of the annual symposium organized by the Student Council of the International Society for Computational Biology (ISCB), held in conjunction with the Intelligent Systems for Molecular Biology (ISMB) / European Conference on Computational Biology (ECCB) conference in Berlin, Germany, on July 19, 2013.

The past decade has witnessed dramatic advances in genome sequencing and a substantial shift in the number of genome wide association studies (GWAS). These efforts have expanded considerably our knowledge on the sequential variations in Human DNA and their consequences on the human biology. Nevertheless, complex genetic disorders often involve products of multiple genes acting cooperatively and pinpointing the decisive elements of such disease pathways remains a challenge. Network biology recently proved its use in identifying candidate disease genes based on the simple observation that proteins translated by phenotypically related genes tend to interact, the so called guilt-by-association principle.

BACKGROUND: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties. RESULTS: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php. CONCLUSIONS: BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.