MOTIVATION: Pleiotropic SNPs are associated with multiple traits. Such SNPs can help pinpoint biological processes with an effect on multiple traits or point to a shared etiology between traits. We present PolarMorphism, a new method for the identification of pleiotropic SNPs from genome-wide association studies (GWAS) summary statistics. PolarMorphism can be readily applied to more than two traits or whole trait domains. PolarMorphism makes use of the fact that trait-specific SNP effect sizes can be seen as Cartesian coordinates and can thus be converted to polar coordinates r (distance from the origin) and theta (angle with the Cartesian x-axis, in the case of two traits). r describes the overall effect of a SNP, while theta describes the extent to which a SNP is shared. r and theta are used to determine the significance of SNP sharedness, resulting in a P-value per SNP that can be used for further analysis. RESULTS: We apply PolarMorphism to a large collection of publicly available GWAS summary statistics enabling the construction of a pleiotropy network that shows the extent to which traits share SNPs. We show how PolarMorphism can be used to gain insight into relationships between traits and trait domains and contrast it with genetic correlation. Furthermore, pathway analysis of the newly discovered pleiotropic SNPs demonstrates that analysis of more than two traits simultaneously yields more biologically relevant results than the combined results of pairwise analysis of the same traits. Finally, we show that PolarMorphism is more efficient and more powerful than previously published methods. AVAILABILITY AND IMPLEMENTATION: code: https://github.com/UMCUGenetics/PolarMorphism, results: 10.5281/zenodo.5844193. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Despite the fact that structural variants (SVs) play an important role in cancer, methods to predict their effect, especially for SVs in non-coding regions, are lacking, leaving them often overlooked in the clinic. Non-coding SVs may disrupt the boundaries of Topologically Associated Domains (TADs), thereby affecting interactions between genes and regulatory elements such as enhancers. However, it is not known when such alterations are pathogenic. Although machine learning techniques are a promising solution to answer this question, representing the large number of interactions that an SV can disrupt in a single feature matrix is not trivial. RESULTS: We introduce svMIL: a method to predict pathogenic TAD boundary-disrupting SV effects based on multiple instance learning, which circumvents the need for a traditional feature matrix by grouping SVs into bags that can contain any number of disruptions. We demonstrate that svMIL can predict SV pathogenicity, measured through same-sample gene expression aberration, for various cancer types. In addition, our approach reveals that somatic pathogenic SVs alter different regulatory interactions than somatic non-pathogenic SVs and germline SVs. AVAILABILITY AND IMPLEMENTATION: All code for svMIL is publicly available on GitHub: https://github.com/UMCUGenetics/svMIL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting. RESULTS: We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit. AVAILABILITY AND IMPLEMENTATION: The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar ([email protected]). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

Cancer affects millions of people worldwide. With the advent of novel DNA sequencing technologies,whole genome sequencing (WGS) is becoming an integral part of cancer diagnostics that can potentially enable tailored treatments of individual patients. To alleviate these problems,a user-friendly, portable and extendable SV calling workflow, sv-callers developed, that includes four state-of-the-art tools to detect SVs in cancer genomes using on-premises HPC systems. The workflow's parallel execution environment enables to scale from a single computer to high-performance compute clusters with minimal effort. The workflow supports SV analysis in either germline or somatic mode, and requires a list of (paired) WGS samples including a reference genome as input. Users may change the workflow parameters and/or software versions using the YAML configuration files. We performed SV analyses on single and paired (tumor/normal) WGS samples, and report on the results obtained using different academic HPC systems.

This special issue includes the proceeding papers accepted for presentation at the 15th European Conference on Computational Biology (ECCB 2016), to be held from September 3 to 7, 2016 at the World Forum Convention Center in The Hague, The Netherlands. Details of the conference are available on the conference web site (www.eccb2016.org) and will later be archived at eccb.iscb.org/2016/. ECCB is the premier European conference in computational biology and bioinformatics, and together with ISMB (Intelligent Systems in Molecular Biology) and RECOMB (Research in Computational Molecular Biology), it is one of the major international conference series in this domain. The ECCB conferences are gathering about a thousand scientists and industry staff working at the intersection of a broad range of disciplines including computer science, mathematics, biology and medicine. New challenges are now emerging in these fields with the recent advances in low-cost ultra-fast sequencing, bio-imaging and big data. Computational analysis platforms are challenged by the enormous complexity of biological systems and the sheer amount of data resulting from high-throughput measuring techniques, such as single-cell or single-molecule measurements. As a consequence, databases and software are evolving rapidly, and new algorithms are required to improve computational analyses of massive biological or biomedical datasets. Recent advances are presented at the conference in the field of data interoperability and machine learning, in particular ‘deep learning’ and network-based analysis techniques. The impact on the field of public data repositories such as TCGA (Weinstein et al., 2013), ENCODE (ENCODE Project Consortium, 2012) and GDSC (Yang et al., 2013) is palpable with many submissions revolving around these resources. ECCB is held annually in a different country, while it is held jointly with the ISMB conference biennially. Going back in time, the fourteen previous editions of ECCB have been held in: Dublin, Ireland, together with ISMB (Moreau and Beerenwinkel, 2015); Strasbourg, France (Devignes, 2014); Berlin, Germany, together with ISMB (Ben-Tal, 2013); Basel, Switzerland (Schwede and Iber, 2012); Vienna, Austria, together with ISMB (Gaasterland and Vingron, 2011); Ghent, Belgium (Moreau and Heringa, 2010); Stockholm, Sweden, together with ISMB (Gusfield and Tramontano, 2009); Cagliari, Italy (Tramontano, 2008); Vienna, Austria, together with ISMB (Lengauer et al., 2007); Eilat, Israel (Wolfson and Safer, 2007); Madrid, Spain (Guigo et al., 2005); Glasgow, United Kingdom, together with ISMB (Thornton et al., 2004); Paris, France (Lenhof and Sagot, 2003); and Saarbrücken, Germany (Lengauer, 2002). The ECCB 2016 edition features keynote lectures by distinguished speakers. The opening keynote lecture will be delivered by the 2013 Breakthrough Prize-winner Hans Clevers (Hubrecht Institute and Princess Maxima Centre for Pediatric Oncology, Utrecht, The Netherlands). Further keynote presentations will be given by Amos Tanay (Weizmann Institute, Tel Aviv, Israel), John Marioni (EMBL-EBI, Hinxton, UK), Nuria Lopez-Bigas (Universitat Pompeu Fabra, Barcelona, Spain), Benedict Paten (UCSC, Santa Cruz, USA), Pauline Hogeweg (Utrecht University, Utrecht, The Netherlands) and Christina Leslie (Memorial Sloan Kettering Cancer Center, New York, USA). The conference topics span all areas of methodological developments for computational biology and innovative applications of computational methods to molecular biology and biomedicine. To present a more unified view of where the science has gone over recent years, new to ECCB this year is that the conference presentations are divided over five broad themes: (i) Data (organization, management, categorization, integration, analysis of data, knowledge discovery); (ii) Genome (sequence analysis, alignment, evolution, phylogeny, genetics, epigenetics, 3D conformation); (iii) Genes (expression, function, regulation, transcription, translation, geno/phenotype); (iv) Proteins (structure, function, alterations, assemblies, interactions, design, proteomics) and (v) Systems (systems biology, pathways, molecular networks, dynamics, signalling, multi-scale modelling). This year four different tracks were created for ECCB: two scientific and two application-oriented ones. On the scientific side, the Proceedings Track presents novel scientific contributions, while the Highlights Track showcases already published high-impact science in computational biology. These two tracks were coordinated and managed across the five themes by a board of 5 × 4 = 20 co-chairs, both overseen by a dedicated track chair. A novelty of ECCB this year is that we have given a prominent platform to applications in the new Application and ELIXIR tracks. The Application Track is an initiative to promote application of computational biology in industry and other fields beyond academia. Submissions to this track should cross the boundaries of traditional academic science, or show developments that are directly relevant beyond academia or have potential for it. Consequently, submissions relating to (pure) research were deemed out of scope. The ELIXIR Track, running for the first time at ECCB 2016, is managed by ELIXIR and focuses on developments relating to services and infrastructure within the ELIXIR nodes. ELIXIR is the pan-European life science infrastructural network (‘Data for the Life Sciences’). It coordinates, integrates and sustains bioinformatics resources across its member states, enabling users in academia and industry to access vital data, tools, standards, compute and training services for research. ELIXIR has chosen ECCB as their major dissemination platform, acting as co-organising sponsor. Following the call for Proceedings papers, we received 150 submissions. Submission authors were asked to rank the five themes for fit of their paper. These rankings were subsequently used to assign papers to themes and to arrive at an optimally balanced distribution of submissions over the themes. Within each theme, the theme co-chairs assigned papers to expert referees, taking care to avoid any conflict of interest. Together, the Programme Committee (PC) was composed of 217 reviewers and 35 co-reviewers. The reviewing and selection process was carried out using the EasyChair multi-track conference reviewing system (www.easychair.org). The review form explicitly differentiated between impact and suitability for ECCB on the one hand, and scientific quality and reproducibility on the other. This distinction was intended to create more clarity for both the reviewers and authors; the reviewing criteria were therefore explicitly stated in the submission guidelines. The added focus on reproducibility resulted in many authors opting to make their methods open source. After the reviewers reached a consensus, a final ranking and selection for each theme was carried out by the theme (co-)chairs. A total of 48 papers were conditionally accepted (acceptance ratio of 32%) based on a predefined number of acceptances per theme based on the distribution of initial assignments over the themes. The authors had two weeks to modify their papers according to the suggestions made by the reviewers and to respond to the reviewers’ comments, which was checked by the theme (co-)chairs. We thank the authors for incorporating these suggestions as they were given little time to carry out (minor) revisions. Essentially, these efforts contribute to the success and reputation of the ECCB conference! It is worth noting that many authors expressed their gratitude to the reviewers for their comments and suggestions. We also gratefully acknowledge the hard and diligent work performed by the reviewers over a short period of time. We believe that for all of the rejected submissions, the reviewers provided high-quality reports. We hope that authors of rejected manuscripts will benefit from these remarks in their future research. We thank all theme co-chairs for their availability throughout the reviewing process, for their very positive attitude in the final selection and for their valuable help in re-examining the modified submissions. The 48 accepted papers are included in this special issue. The Proceedings Track papers with their supplementary files are available free-for-view in electronic format from Oxford’s press journal Bioinformatics from September 1st, 2016. Highlight presentations were introduced at ISMB/ECCB 2007 in Vienna (Lengauer et al., 2007), and immediately became one of the most popular features of the conference. All original research papers that had been published in peer-review journals between 1 March, 2015, and the submission deadline of 29 March 2016, were eligible to be presented as a Highlight talk. After thorough consideration and discussion, the ECCB 2016 theme (co-)chairs selected 24 proposals out of 71 submissions, mainly on the criteria of compatibility with the ECCB objectives, wide impact in the life sciences and the potential for attracting a large audience to the conference. The Applications Track features 11 presentations, which were selected out of 31 submissions. At the time of writing, three Sponsored Talks will be delivered as part of the Applications Track by respectively The Hyve, Keygene and Data Computing. Sponsored Talks enable sponsors to showcase their innovations in computational biology and to highlight their scientific value. Finally, the ELIXIR track proved to be a very popular addition, with 50 high-quality submissions for only 12 presentation slots. Submissions to the Poster Track were evaluated based on a 250-word abstract and will be shown at the conference along the five main conference themes. In line with the new Application and ELIXIR tracks, there will also be special Application and ELIXIR poster tracks. A dedicated Education Poster Track was created to devote in-depth attention to education that is so crucial for the next generation of bioinformaticians. All poster abstracts are available on the conference web site. We also arranged with F1000Research (http://f1000research.com/) to publish the posters via a new ECCB2016 channel; submission is on a voluntary basis. At least sixteen exhibitor booths will be open throughout the conference in the central conference hall, which is well connected to the other activities at ECCB: The Hyve, EMBL-EBI, TimeLogic, Springer, ISCB Student Council, Goblet, ELIXIR, ISCB, Oxford University Press, ENPICOM, Data Computing, CRC Press, SIB, ELIXIR Denmark and Cambridge University Press. They will be presenting the latest scientific literature in the field of computational biology, bioinformatics, data stewardship, modeling and simulation, as well as new hardware, software and technology developments. The Hague Tourist Office, and the four organizing institutions (DTL, Netherlands Bioinformatics and System Biology Research School (BioSB), VU University Amsterdam and Delft University of Technology) will also be represented. During the weekend before the conference, a satellite meeting, 14 workshops and nine tutorials will take place. The Student Council of the International Society for Computational Biology (ISCB) organizes its 4th European Student Council Symposium (ESCS), chaired by Annika Jacobsen (Vrije Universiteit Amsterdam) and Kevin Schwahn (Universität Potsdam, Max Planck Institute of Molecular Plant Physiology). ESCS highlights will be published in F1000Research via the ISCB Student Council channel. The ECCB 2016 organizing committee congratulates all these dynamic young scientists for their enthusiasm, which is essential to the future of research in computational biology. The 14 workshops preceding the ECCB 2016 main meeting were selected out of a total of 29 applications, showing how popular ECCB has become as a venue for dissemination of computational biology research. The workshops, running all but one for a single day, provide participants with an informal setting to discuss technical issues, exchange research ideas, and to share practical experiences on a range of focused or emerging topics in computational biology. Taken together, the workshops demonstrate how extensively technologies have found their way into large-scale practical applications: • (W1) The 10th International Workshop on Machine Learning in Systems Biology, organised by (Juho Rousu, Aalto University, Finland), Dick de Ridder Wageningen University, The Netherlands), Harri Lähdesmäki (Aalto University, Finland) and Aalt—Jan van Dijk (Wageningen University, The Netherlands), is a two-day workshop with its own proceedings track, where accepted papers will be published in BMC Bioinformatics, • (W2) Network Inference: New Methods and New Data, organised by Anagha Joshi (Roslin Institute, University of Edinburgh, UK), Tom Michoel (Roslin Institute, University of Edinburgh, UK) and Eric Bonnet (Centre National de Génotypage, CEA, Paris, France) • (W3) Getting the Most out of Your Methods and Algorithms: A Workshop on How to Use Existing Datasets to Gain Novel Insight, chaired by Morris Swertz and Lude Franke (both at University Medical Centre Groningen, The Netherlands), • (W4) Digital Pathology Meets Bioinformatics, organised by Yves Sucaet (Vrije Universiteit Brussel, Belgium), Jeroen Van der Laak (UMC Radboud, Nijmegen, The Netherlands), Marius Nap (HistoGeneX, Belgium and Rigshospitalet Copenhagen, Denmark), Zev Leifer (New York College of Podiatric Medicine, USA), Yukako Yagi (Harvard Medical School, Cambridge and Massachusetts General Hospital, Boston, USA) and Raphaël Marée (Université de Liège, Belgium), • (W5) RepSeq 2016: Immune Repertoire Sequencing—Bioinformatics and Applications in Hematology and Immunology, organised by Jack Bartram (University College London, UK), Eva Froňková (Charles University Prague, Czech Republic), Mathieu Giraud, (CNRS, Lille, France—program co-chair), Peter N. Robinson (Charité Berlin, Germany), Mikaël Salson (Université de Lille, France—proceedings chair), Mikhail Shugay (Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia—program co-chair) and Andrew P. Stubbs (Erasmus MC, Rotterdam, The Netherlands), • (W6) FAIR Data and Data Stewardship, organised by Erik Schultes, Mark Thompson Marco Roos (all three at Leiden University Medical Centre, The Netherlands), Mark Wilkinson (Universidad Politecnica de Madrid, Spain), Luiz Olavo Bonino da Silva Santos (Dutch Techcentre for Life Sciences and Vrije Universiteit Amsterdam, The Netherlands), • (W7) Challenges and Approaches in Comprehensive and Informative Complex Network Analysis for Precision Medicine, organised by Igor Jurisica (University of Toronto, Canada), Natasa Przulj (University College London, UK) and Tijana Milenkovic (University of Notre Dame, USA), • (W8) Computing a Tissue: Modeling Multicellular Systems, organised by Walter de Back (TU Dresden, Germany), Sara Montagna (University of Bologna, Italy) and Roeland Merks (Center for Mathematics and Computer Science (CWI), Amsterdam and Leiden University, The Netherlands), • (W9) Computational Pan-Genomics, organised by Zamin Iqbal (University of Oxford, UK), Tobias Marschall (Saarland University and Max Planck Institute for Informatics, Saarbrücken, Germany) and Benedict Paten (3UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA), • (W10) BioNetVisA: from Biological Network Reconstruction to data visualisation and analysis in Molecular Biology and Medicine, organised by Inna Kuperstein, Emmanuel Barillot, Andrei Zinovyev (all three at Institut Curie, Paris, France), Hiroaki Kitano (Okinawa Institute of Science and Technology Graduate University, RIKEN Center for Integrative Medical Sciences, Japan), Minoru Kanehisa (Kyoto University, Japan), Samik Ghosh (Systems Biology Institute, Tokyo, Japan), Nicolas Le Novère (Babraham Institute, Cambridge, UK), Robin Haw (Ontario Institute for Cancer Research, Canada), Alfonso Valencia (Spanish National Bioinformatics Institute, Madrid, Spain) and Lodewyk Wessels (Netherlands Cancer Institute, Amsterdam, and Technical University Delft, The Netherlands), • (W11) Recent Computational Advances in Metagenomics, organised by Sophie Schbath, Valentin Loux and Mahendra Mariadassou (all at INRA, Jouy-en-Josas, France), • (W12) BioExcel: Advanced Simulations for Biomolecular Research, a SIG workshop organised by Rossen Apostolov (KTH, Stockholm, Sweden)), Alexandre Bonvin (Utrecht University, The Netherlands), Cath Brooksbank (EMBL-EBI, Hinxton, UK) and Ian Harrow (Ian Harrow Consulting, Whitstable, UK), • (W13) Clinical Bioinformatics as a Service, organised by Niko Beerenwinkel (ETH Zurich, and SIB Swiss Institute of Bioinformatics, Switzerland), Wolfgang Huber (EMBL, Heidelberg), Simon Tavaré (Cancer Research UK Cambridge Institute, UK) and Daniel Stekhoven and SIB Swiss Institute of Bioinformatics, Switzerland), • Computational Challenges of Data organised by University Medical Center, The Netherlands), (UMC Utrecht, The Netherlands) and Hans The Netherlands). • Data Analysis with and organised by Jeroen and The Netherlands), • Genome organised by (University of Spain) and (University of Spain), • and organised by (University of Spain), • Analysis and with organised by United • and for Modeling Biological Systems, organised by (University of Germany) and (University of Germany), • Analysis and for Data, organised by Medical Center, Amsterdam and University of Amsterdam, The Netherlands), • A to Research on organised by and (both at University Pompeu Fabra, Barcelona, Spain), • and their in Genome Data, organised by N. (University of Cambridge, UK), University of Science and and (University of UK), • into and its Applications in Bioinformatics and in Network organised by and (both at Hans Institute, and University Hospital, It is to thank all the and that are ECCB 2016 a and high-quality conference. of we are to the committee composed of the theme (co-)chairs and reviewers for their crucial and dedicated We are to the ECCB committee for their and to the of the conference. In the and by Committee and ECCB ECCB Yves ECCB ECCB and ECCB 2012) were Yves and for and The and of ISCB Society for Computational Biology) in the about ECCB 2016 at the international and for were We thank all provided to the conference. In to the co-organising ELIXIR, we gratefully acknowledge sponsors The Netherlands and USA) and the National Centre for Research (CNRS, We also as where also the opening will be the of The Hague, the and The Bioinformatics Centre the Poster We are to the Oxford University for the ECCB 2016 special issue. The F1000Research is for and the ECCB 2016 posters on their web The ECCB 2016 science has been in the of the efforts have been van (Netherlands Bioinformatics and Systems Biology Research Techcentre for Life ECCB 2016 for the selection (TU Delft, The Netherlands), ECCB 2016 for out the workshop selection (EMBL, Germany), ECCB 2016 Applications Track for and the new Applications Andrew and (all at ELIXIR Hinxton, UK) for taking care in of the first ELIXIR Applications and (University of Finland), ECCB 2016 Poster Track for and the ECCB 2016 to the of the conference, beyond the call of and we a in particular and van both at the Techcentre for Life Sciences for their and in this are also to and van of by Netherlands), ECCB 2016 (Dutch Techcentre for Life had a in the while van is more for also in Finally, all these efforts be the many participants from all over the of will to the conference in of scientific contributions, applications, or poster presentations and all for there and for to science at ECCB 2016 in The

MOTIVATION: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation. RESULTS: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer. AVAILABILITY AND IMPLEMENTATION: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.

BACKGROUND: Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. APPROACH: Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization. RESULTS: We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy. CONCLUSION: We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale).

MOTIVATION: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). RESULTS: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. AVAILABILITY AND IMPLEMENTATION: Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA

Contributing to a student organization, such as the International Society for Computational Biology Student Council (ISCB-SC) and its Regional Student Group (RSG) program, takes time and energy. Both are scarce commodities, especially when you are trying to find your place in the world of computational biology as a graduate student. It comes as no surprise that organizing ISCB-SC-related activities sometimes interferes with day-to-day research and shakes up your priority list. However, we unanimously agree that the rewards, both in the short as well as the long term, make the time spent on these extracurricular activities more than worth it. In this article, we will explain what makes this so worthwhile: soft skills.

Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained.

BACKGROUND: Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. RESULTS: We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. CONCLUSIONS: The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

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

MOTIVATION: We propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels. RESULTS: Our method provably finds the global solution and is very efficient with runtimes of up to four orders of magnitude faster than the exhaustive search. This enables permutation procedures for determining accurate false positive rates and allows selection of the most parsimonious model. When applied to transcript levels measured in myeloid cells from 24 genotyped recombinant inbred mouse strains, we discovered that nine gene clusters are putatively modulated by a logical combination of trait loci rather than a single locus. A literature survey supports and further elucidates one of these findings. Due to our approach, optimal solutions for multi-locus logic models and accurate estimates of the associated false discovery rates become feasible. Our algorithm, therefore, offers a valuable alternative to approaches employing complex, albeit suboptimal optimization strategies to identify complex models. AVAILABILITY: The MATLAB code of the prototype implementation is available on: http://bioinformatics.tudelft.nl/ or http://bioinformatics.nki.nl/.

BACKGROUND: Tiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays. RESULTS: We have developed Hypergeometric Analysis of Tiling-arrays (HAT), and first evaluated its performance for tiling-array datasets from a Chromatin Immunoprecipitation study on chip (ChIP-on-chip) for the identification of genome-wide DNA binding profiles of transcription factor Cebpa (used for method comparison). Using this assay, we can refine the detection of regions-of-interest by illustrating that regions detected by HAT are more highly enriched for expected motifs in comparison with an alternative detection method (MAT). Subsequently, data from a retroviral insertional mutagenesis screen were used to examine the performance of HAT among different applications of tiling-array datasets. In both studies, detected regions-of-interest have been validated with (q)PCR. CONCLUSIONS: We demonstrate that HAT has increased specificity for analysis of tiling-array data in comparison with the alternative method, and that it accurately detects regions-of-interest in two different applications of tiling-arrays. HAT has several advantages over previous methods: i) as there is no single cut-off level for probe-intensity, HAT can detect regions-of-interest at various thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome, and across tiling-array platforms and iv) it employs a single user defined parameter: the significance level. Regions-of-interest are detected by computing the hypergeometric-probability, while controlling the Family Wise Error. Furthermore, the method does not require experimental replicates, common regions-of-interest are indicated, a sequence-of-interest can be examined for every detected region-of-interest, and flanking genes can be reported.

Retroviral insertional mutagenesis screens, which identify genes involved in tumor development in mice, have yielded a substantial number of retroviral integration sites, and this number is expected to grow substantially due to the introduction of high-throughput screening techniques. The data of various retroviral insertional mutagenesis screens are compiled in the publicly available Retroviral Tagged Cancer Gene Database (RTCGD). Integrally analyzing these screens for the presence of common insertion sites (CISs, i.e., regions in the genome that have been hit by viral insertions in multiple independent tumors significantly more than expected by chance) requires an approach that corrects for the increased probability of finding false CISs as the amount of available data increases. Moreover, significance estimates of CISs should be established taking into account both the noise, arising from the random nature of the insertion process, as well as the bias, stemming from preferential insertion sites present in the genome and the data retrieval methodology. We introduce a framework, the kernel convolution (KC) framework, to find CISs in a noisy and biased environment using a predefined significance level while controlling the family-wise error (FWE) (the probability of detecting false CISs). Where previous methods use one, two, or three predetermined fixed scales, our method is capable of operating at any biologically relevant scale. This creates the possibility to analyze the CISs in a scale space by varying the width of the CISs, providing new insights in the behavior of CISs across multiple scales. Our method also features the possibility of including models for background bias. Using simulated data, we evaluate the KC framework using three kernel functions, the Gaussian, triangular, and rectangular kernel function. We applied the Gaussian KC to the data from the combined set of screens in the RTCGD and found that 53% of the CISs do not reach the significance threshold in this combined setting. Still, with the FWE under control, application of our method resulted in the discovery of eight novel CISs, which each have a probability less than 5% of being false detections.