Motivation: Genome-wide measurements of genetic and epigenetic alterations are generating more and more high-dimensional binary data. The special mathematical characteristics of binary data make the direct use of the classical principal component analysis (PCA) model to explore low-dimensional structures less obvious. Although there are several PCA alternatives for binary data in the psychometric, data analysis and machine learning literature, they are not well known to the bioinformatics community. Results: In this article, we introduce the motivation and rationale of some parametric and nonparametric versions of PCA specifically geared for binary data. Using both realistic simulations of binary data as well as mutation, CNA and methylation data of the Genomic Determinants of Sensitivity in Cancer 1000 (GDSC1000), the methods were explored for their performance with respect to finding the correct number of components, overfit, finding back the correct low-dimensional structure, variable importance, etc. The results show that if a low-dimensional structure exists in the data, that most of the methods can find it. When assuming a probabilistic generating process is underlying the data, we recommend to use the parametric logistic PCA model, while when such an assumption is not valid and the data are considered as given, the nonparametric Gifi model is recommended. Availability: The codes to reproduce the results in this article are available at the homepage of the Biosystems Data Analysis group (www.bdagroup.nl).

Motivation: In biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets. Results: We present iTOP, a methodology to infer a topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics. Availability and implementation: An implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

MOTIVATION: Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. RESULTS: To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. AVAILABILITY AND IMPLEMENTATION: TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem). CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).

What is more inspiring than a discussion with the leading scientists in your field? As a student or a young researcher, you have likely been influenced by mentors guiding you in your career and leading you to your current position. Any discussion with or advice from an expert is certainly very helpful for young people. But how often do we have the opportunity to meet experts? Do we make the most out of these situations? Meetings organized for young scientists are a great opportunity not only for the attendees: they are an opportunity for experts to meet bright students and learn from them in return. In this article, we introduce several successful events organized by Regional Student Groups all around the world, bridging the gap between experts and young scientists. We highlight how rewarding it is for all participants: young researchers, experts, and organizers. We then discuss the various benefits and emphasize the importance of organizing and attending such meetings. As a young researcher, seeking mentorship and additional skills training is a crucial step in career development. Keep in mind that one day, you may be an inspiring mentor, too.

Sharing results, techniques, and challenges is paramount to advance our understanding of any field of science. In the scientific community this exchange of ideas is mainly made possible through national and international conferences. Scientists have the opportunity to showcase their work, receive feedback, and improve their presentation skills. However, conferences can be large and intimidating for young researchers. In addition, for many of the more prestigious conferences, the very high number of submissions and low selection rate are major limitations to aspiring young researchers aiming to present their work to the scientific community. To improve student participation and proliferation of information, regional student groups have successfully organized conferences and symposia specifically aimed at students. This gives more students the opportunity to present their work and receive valuable experience and insight from peers and leaders in the field. At the same time, it is an ideal way for students to gain familiarity with the conference experience. In this paper, we highlight some of the benefits of participating in such student conferences, and we review the challenges we have encountered when organizing them. Both topics are illustrated in detail with examples from different ISCB Student Council Regional Student Groups.

Exchanging ideas with like-minded, enthusiastic people interested in the same topic is crucial for the advancement of a scientist's career. Several Regional Student Groups (RSGs) of the International Society for Computational Biology (ISCB) Student Council have cooperated in the last six years to organize scientific workshops and conferences. With motivated students, it is possible to create a memorable event for fellow scientists; in doing so, the organizers gain valuable experiences. While collaborating across borders and time zones can be difficult, feedback from event organizers was always positive. When limited resources are juxtaposed with great ideas and a network of contacts, the outcome is always an amazing experience, despite organizers being separated geographically across different countries.

What are you working on? You have certainly been asked that question many times, whether it be at a Saturday night party, during a discussion with your neighbors, or at a family gathering. Communicating with a lay audience about scientific subjects and making them attractive is a difficult task. But difficult or not, you will have to do it for many years, not only with your family and friends, but also with your colleagues and collaborators. So, better learn now! Although not usually taught, the ability to explain your work to others is an essential skill in science, where communication plays a key role. Using some examples of the French Regional Student Group activities, we discuss here (i) why it is important to have such communication skills, (ii) how you can get involved in these activities by using existing resources or working with people who have previous experience, and (iii) what you get out of this amazing experience. We aim to motivate you and provide you with tips and ideas to get involved in promoting scientific activities while getting all the benefits.

Intrinsically disordered regions have been associated with various cellular processes and are implicated in several human diseases, but their exact roles remain unclear. We previously defined two classes of conserved disordered regions in budding yeast, referred to as "flexible" and "constrained" conserved disorder. In flexible disorder, the property of disorder has been positionally conserved during evolution, whereas in constrained disorder, both the amino acid sequence and the property of disorder have been conserved. Here, we show that flexible and constrained disorder are widespread in the human proteome, and are particularly common in proteins with regulatory functions. Both classes of disordered sequences are highly enriched in regions of proteins that undergo tissue-specific (TS) alternative splicing (AS), but not in regions of proteins that undergo general (i.e., not tissue-regulated) AS. Flexible disorder is more highly enriched in TS alternative exons, whereas constrained disorder is more highly enriched in exons that flank TS alternative exons. These latter regions are also significantly more enriched in potential phosphosites and other short linear motifs associated with cell signaling. We further show that cancer driver mutations are significantly enriched in regions of proteins associated with TS and general AS. Collectively, our results point to distinct roles for TS alternative exons and flanking exons in the dynamic regulation of protein interaction networks in response to signaling activity, and they further suggest that alternatively spliced regions of proteins are often functionally altered by mutations responsible for cancer.

The International Society for Computational Biology (ISCB) Student Council was launched in 2004 to facilitate interaction between young scientists in the fields of bioinformatics and computational biology.Since then, the Student Council has successfully run events and programs to promote the development of the next generation of computational biologists.However, in its early years, the Student Council faced a major challenge, in that students from different geographical regions had different needs; no single activity or event could address the needs of all students.To overcome this challenge, the Student Council created the Regional Student Group (RSG) program.The program consists of locally organised and run student groups that address the specific needs of students in their region.These groups usually encompass a given country, and, via affiliation with the international Student Council, are provided with financial support, organisational support, and the ability to share information with other RSGs.In the last five years, RSGs have been created all over the world and organised activities that have helped develop dynamic bioinformatics student communities.In this article series, we present common themes emerging from RSG initiatives, explain their goals, and highlight the challenges and rewards through specific examples.This article, the first in the series, introduces the Student Council and provides a high-level overview of RSG activities.Our hope is that the article series will be a valuable source of information and inspiration for initiating similar activities in other regions and scientific communities. The RSG Article SeriesThis article series draws on the collective experience of the ISCB Student Council to demonstrate the effectiveness of a Regional Student Group (RSG) program.The goal of the series is to inspire students in the broader community to start similar initiatives.Each of the articles is written by authors from various RSGs that have had hands-on experience in the article topic.The articles share both recipes that worked well and formulas that did not.The topics covered include: interactive science, scientific meetings, career development, and the challenges and benefits of running and starting a Regional Student Group.See Table 1 for an outline of the series.

You finished your PhD, have been a postdoc for a while, and you start wondering, ''What's next?''Suppose you come to the conclusion that you want to stay in academia, and move up the ladder to become a principal investigator (PI).How does one reach this goal given that academia is one of the most competitive environments out there?And suppose you do manage to snatch your dream position, how do you make sure you hit the ground running?Here we report on the workshop ''P2P -From Postdoc To Principal Investigator'' that we organized at ISMB 2012 in Long Beach, California.The workshop addressed some of the challenges that many postdocs and newly appointed PIs are facing.Three experienced PIs,

peer reviewed

Student organizations are a great way to network and take a break from the rigors of the classroom.They provide a range of benefits beyond regular coursework and can be critical to having a well-rounded education.Many students are active in organizations at an undergraduate level, but the increased demands of a master's or PhD typically result in reduced participation at a graduate level.However, a student organization can equally provide benefits for a graduate student, especially if it is centered on the student's area of study.In this article, we focus on Regional Student Groups (RSGs).An RSG is a group of like-minded students across a geographical region with a common field of research.The group provides a support network and collaboration opportunities via a collection of individuals who ''speak the same language.''The RSG concept was created by the International Society for Computational Biology Student Council to address the needs of students in the field of computational biology in each region.Currently, the RSG program consists of over 20 regional student groups worldwide.In this article, we provide ten simple rules for how to start a regional student group in the hope that others will start up similar groups around the world.

Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

A scientific community consists of scientists working in a particular field of science and, most importantly, of their relationships and interactions. Beyond the traditional publication of research projects, discussions occurring during conferences, seminars, and even online through social networks or blogs enable ideas to spread more efficiently and are essential for building a lively and dynamic community. Activities such as organizing conferences and workshops, answering questions and discussing scientific ideas online, contributing to a scientific blog, or participating in open source software projects are typically thought of as outside classic research activity. Having scientists involved in those activities, however, is very important for the community to be dynamic and to promote fruitful discussions and collaborations. Scientific associations have an important role in enabling science by bringing people together and giving them a voice. Moreover, being involved in such activities is individually very rewarding because it enables scientists to acquire new skills not typically taught and to expand their network and interactions. For those reasons, I encourage young scientists to get involved in their scientific community. However, it should be noted that this involvement takes time during which you are not directly contributing to your research projects and publications. It is thus essential to balance those activities. The purpose of this paper is twofold: i) illustrate some of the benefits of being involved and, most importantly, discuss how to get there; and ii) give some concrete advice and rules to keep this involvement as effective and controlled as possible in order to serve the community and receive benefits in return without hampering your research activity. In scientific societies or associations, many tasks are accomplished by individuals who volunteer their time. Even tasks that appear to be merely administrative or clerical are essential for the scientific community and will make a difference in your field. In those volunteer organizations, projects are often driven by a single person or a very small team. Consequently, volunteers often have to take initiative and take things into their own hands. That is the context in which these rules should be of particular interest. I have been involved in the Student Council of the International Society for Computational Biology for five years, progressively taking on more responsibilities, in particular in the organization of conferences (co-chair of the symposium in Boston in 2010 and chair of the first European symposium in Ghent in 2010), but also more generally in the Student Council (I was secretary—one of the elected leaders—of the Student Council in 2009). In addition, I created the French Regional Student Group (RSG-France), which I chaired for two years. This paper is based on my experience in the bioinformatics community, but also on associative involvement I had outside science. Most examples are taken from the bioinformatics community, but I believe the rules are rather general and apply for other communities.

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.

This meeting report gives an overview of the keynote lectures and a selection of the student oral and poster presentations at the 6th International Society for Computational Biology Student Council Symposium that was held as a precursor event to the annual international conference on Intelligent Systems for Molecular Biology (ISMB). The symposium was held in Boston, MA, USA on July 9th, 2010.

MOTIVATION: Protein-protein interaction networks provide insights into the relationships between the proteins of an organism thereby contributing to a better understanding of cellular processes. Nevertheless, large-scale interaction networks are available for only a few model organisms. Thus, interologs are useful for a systematic transfer of protein interaction networks between organisms. However, no standard tool is available so far for that purpose. RESULTS: In this study, we present an automated prediction tool developed for all sequenced genomes available in Integr8. We also have developed a second method to predict protein-protein interactions in the widely used cyanobacterium Synechocystis. Using these methods, we have constructed a new network of 8783 inferred interactions for Synechocystis. AVAILABILITY: InteroPORC is open-source, downloadable and usable through a web interface at http://biodev.extra.cea.fr/interoporc/.