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1.  Community-driven development for computational biology at Sprints, Hackathons and Codefests 
BMC Bioinformatics  2014;15(Suppl 14):S7.
Computational biology comprises a wide range of technologies and approaches. Multiple technologies can be combined to create more powerful workflows if the individuals contributing the data or providing tools for its interpretation can find mutual understanding and consensus. Much conversation and joint investigation are required in order to identify and implement the best approaches.
Traditionally, scientific conferences feature talks presenting novel technologies or insights, followed up by informal discussions during coffee breaks. In multi-institution collaborations, in order to reach agreement on implementation details or to transfer deeper insights in a technology and practical skills, a representative of one group typically visits the other. However, this does not scale well when the number of technologies or research groups is large.
Conferences have responded to this issue by introducing Birds-of-a-Feather (BoF) sessions, which offer an opportunity for individuals with common interests to intensify their interaction. However, parallel BoF sessions often make it hard for participants to join multiple BoFs and find common ground between the different technologies, and BoFs are generally too short to allow time for participants to program together.
This report summarises our experience with computational biology Codefests, Hackathons and Sprints, which are interactive developer meetings. They are structured to reduce the limitations of traditional scientific meetings described above by strengthening the interaction among peers and letting the participants determine the schedule and topics. These meetings are commonly run as loosely scheduled "unconferences" (self-organized identification of participants and topics for meetings) over at least two days, with early introductory talks to welcome and organize contributors, followed by intensive collaborative coding sessions. We summarise some prominent achievements of those meetings and describe differences in how these are organised, how their audience is addressed, and their outreach to their respective communities.
Hackathons, Codefests and Sprints share a stimulating atmosphere that encourages participants to jointly brainstorm and tackle problems of shared interest in a self-driven proactive environment, as well as providing an opportunity for new participants to get involved in collaborative projects.
PMCID: PMC4255748  PMID: 25472764
2.  ptRNApred: computational identification and classification of post-transcriptional RNA 
Nucleic Acids Research  2014;42(22):e167.
Non-coding RNAs (ncRNAs) are known to play important functional roles in the cell. However, their identification and recognition in genomic sequences remains challenging. In silico methods, such as classification tools, offer a fast and reliable way for such screening and multiple classifiers have already been developed to predict well-defined subfamilies of RNA. So far, however, out of all the ncRNAs, only tRNA, miRNA and snoRNA can be predicted with a satisfying sensitivity and specificity. We here present ptRNApred, a tool to detect and classify subclasses of non-coding RNA that are involved in the regulation of post-transcriptional modifications or DNA replication, which we here call post-transcriptional RNA (ptRNA). It (i) detects RNA sequences coding for post-transcriptional RNA from the genomic sequence with an overall sensitivity of 91% and a specificity of 94% and (ii) predicts ptRNA-subclasses that exist in eukaryotes: snRNA, snoRNA, RNase P, RNase MRP, Y RNA or telomerase RNA. AVAILABILITY: The ptRNApred software is open for public use on
PMCID: PMC4267668  PMID: 25303994
3.  Genetic Control of Spontaneous Arthritis in a Four-Way Advanced Intercross Line 
PLoS ONE  2013;8(10):e75611.
Identifying the genetic basis of complex diseases, such as rheumatoid arthritis, remains a challenge that requires experimental models to reduce the genetic and environmental variability. Numerous loci for arthritis have been identified in induced animal models; however, few spontaneous models have been genetically studied. Therefore, we generated a four-way advanced intercross line (AIL) from four inbred strains, including BXD2/TyJ which spontaneously develops autoimmune arthritis. A genome-wide scan for spontaneous arthritis was performed in a cohort of 366 mice of the fourth generation (G4) of this cross. Five loci contributing to clinical phenotypes were identified in chromosomes 3, 7, 13, 18, and X. Three of the loci found in this study, confirm previously identified loci; whereas two of them are novel loci. Interesting candidate genes for the loci are highlighted. This study provides a genetic overview of spontaneous arthritis in mice and aids to solve the genetic etiology of rheumatoid arthritis and to gain a better understanding of the disease.
PMCID: PMC3795728  PMID: 24146764
4.  Combining genetic mapping with genome-wide expression in experimental autoimmune encephalomyelitis highlights a gene network enriched for T cell functions and candidate genes regulating autoimmunity 
Human Molecular Genetics  2013;22(24):4952-4966.
The experimental autoimmune encephalomyelitis (EAE) is an autoimmune disease of the central nervous system commonly used to study multiple sclerosis (MS). We combined clinical EAE phenotypes with genome-wide expression profiling in spleens from 150 backcross rats between susceptible DA and resistant PVG rat strains during the chronic EAE phase. This enabled correlation of transcripts with genotypes, other transcripts and clinical EAE phenotypes and implicated potential genetic causes and pathways in EAE. We detected 2285 expression quantitative trait loci (eQTLs). Sixty out of 599 cis-eQTLs overlapped well-known EAE QTLs and constitute positional candidate genes, including Ifit1 (Eae7), Atg7 (Eae20-22), Klrc3 (eEae22) and Mfsd4 (Eae17). A trans-eQTL that overlaps Eae23a regulated a large number of small RNAs and implicates a master regulator of transcription. We defined several disease-correlated networks enriched for pathways involved in cell-mediated immunity. They include C-type lectins, G protein coupled receptors, mitogen-activated protein kinases, transmembrane proteins, suppressors of transcription (Jundp2 and Nr1d1) and STAT transcription factors (Stat4) involved in interferon signaling. The most significant network was enriched for T cell functions, similar to genetic findings in MS, and revealed both established and novel gene interactions. Transcripts in the network have been associated with T cell proliferation and differentiation, the TCR signaling and regulation of regulatory T cells. A number of network genes and their family members have been associated with MS and/or other autoimmune diseases. Combining disease and genome-wide expression phenotypes provides a link between disease risk genes and distinct molecular pathways that are dysregulated during chronic autoimmune inflammation.
PMCID: PMC3836475  PMID: 23900079
5.  xQTL workbench: a scalable web environment for multi-level QTL analysis 
Bioinformatics  2012;28(7):1042-1044.
Summary: xQTL workbench is a scalable web platform for the mapping of quantitative trait loci (QTLs) at multiple levels: for example gene expression (eQTL), protein abundance (pQTL), metabolite abundance (mQTL) and phenotype (phQTL) data. Popular QTL mapping methods for model organism and human populations are accessible via the web user interface. Large calculations scale easily on to multi-core computers, clusters and Cloud. All data involved can be uploaded and queried online: markers, genotypes, microarrays, NGS, LC-MS, GC-MS, NMR, etc. When new data types come available, xQTL workbench is quickly customized using the Molgenis software generator.
Availability: xQTL workbench runs on all common platforms, including Linux, Mac OS X and Windows. An online demo system, installation guide, tutorials, software and source code are available under the LGPL3 license from
PMCID: PMC3315722  PMID: 22308096
6.  Bioinformatics tools and database resources for systems genetics analysis in mice—a short review and an evaluation of future needs 
Briefings in Bioinformatics  2011;13(2):135-142.
During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.
PMCID: PMC3294237  PMID: 22396485
QTL mapping; database; mouse; systems genetics
7.  Community-driven computational biology with Debian Linux 
BMC Bioinformatics  2010;11(Suppl 12):S5.
The Open Source movement and its technologies are popular in the bioinformatics community because they provide freely available tools and resources for research. In order to feed the steady demand for updates on software and associated data, a service infrastructure is required for sharing and providing these tools to heterogeneous computing environments.
The Debian Med initiative provides ready and coherent software packages for medical informatics and bioinformatics. These packages can be used together in Taverna workflows via the UseCase plugin to manage execution on local or remote machines. If such packages are available in cloud computing environments, the underlying hardware and the analysis pipelines can be shared along with the software.
Debian Med closes the gap between developers and users. It provides a simple method for offering new releases of software and data resources, thus provisioning a local infrastructure for computational biology. For geographically distributed teams it can ensure they are working on the same versions of tools, in the same conditions. This contributes to the world-wide networking of researchers.
PMCID: PMC3040531  PMID: 21210984
9.  Cytoskeletal Rearrangements in Synovial Fibroblasts as a Novel Pathophysiological Determinant of Modeled Rheumatoid Arthritis 
PLoS Genetics  2005;1(4):e48.
Rheumatoid arthritis is a chronic inflammatory disease with a high prevalence and substantial socioeconomic burden. Despite intense research efforts, its aetiology and pathogenesis remain poorly understood. To identify novel genes and/or cellular pathways involved in the pathogenesis of the disease, we utilized a well-recognized tumour necrosis factor-driven animal model of this disease and performed high-throughput expression profiling with subtractive cDNA libraries and oligonucleotide microarray hybridizations, coupled with independent statistical analysis. This twin approach was validated by a number of different methods in other animal models of arthritis as well as in human patient samples, thus creating a unique list of disease modifiers of potential therapeutic value. Importantly, and through the integration of genetic linkage analysis and Gene Ontology–assisted functional discovery, we identified the gelsolin-driven synovial fibroblast cytoskeletal rearrangements as a novel pathophysiological determinant of the disease.
Rheumatoid arthritis (RA) is a chronic destructive disease that affects 1–3% of the general population, exacting substantial personal, social, and economic costs. Current treatments alleviate the symptoms and offer immediate relief for many patients but do not cure the disease. While the cause of the disease remains poorly understood, the completion of the Human Genome Project and the emergence of functional genomics and high-throughput technologies offer intriguing new possibilities. For example, expression profiling creates a molecular fingerprint of the disease status by quantifying the expression levels of thousand of genes simultaneously. Similarly, reverse genetics (the genetic modification of a particular gene in search of its function) allow for the creation of animal models of disease. To discover novel genes and/or cellular pathways involved in the development of the disease, the authors used two methods in an animal model of RA for large-scale expression profiling. They identified a large number of genes and molecular processes that are deregulated in the disease. Using this information, the authors described pathophysiologic determinants of RA and created a unique list of disease modifiers of potential therapeutic value.
PMCID: PMC1270006  PMID: 16254600
10.  Expressionview: visualization of quantitative trait loci and gene-expression data in Ensembl 
Genome Biology  2003;4(11):R77.
Expressionview is a software tool for combined visualization of gene-expression data and quantitative trait loci (QTL). The application is implemented as an extension to the Ensembl project and caters for a direct transition from microarray experiments of gene or protein expression levels to the genomic context of individual genes and QTL.
We present here a software tool for combined visualization of gene-expression data and quantitative trait loci (QTL). The application is implemented as an extension to the Ensembl project and caters for a direct transition from microarray experiments of gene or protein expression levels to the genomic context of individual genes and QTL. It supports the visualization of gene clusters and the selection of functional candidate genes in the context of research on complex traits.
PMCID: PMC329133  PMID: 14611663
11.  Genome-wide mapping of gene–microbiota interactions in susceptibility to autoimmune skin blistering 
Nature Communications  2013;4:2462.
Susceptibility to chronic inflammatory diseases is determined by immunogenetic and environmental risk factors. Resident microbial communities often differ between healthy and diseased states, but whether these differences are of primary aetiological importance or secondary to the altered inflammatory environment remains largely unknown. Here we provide evidence for host gene–microbiota interactions contributing to disease risk in a mouse model of epidermolysis bullosa acquisita, an autoantibody-induced inflammatory skin disease. Using an advanced intercross, we identify genetic loci contributing to skin microbiota variability, susceptibility to skin blistering and their overlap. Furthermore, by treating bacterial species abundances as covariates with disease we reveal a novel disease locus. The majority of the identified covariate taxa are characterized by reduced abundance being associated with increased disease risk, providing evidence of a primary role in protection from disease. Further characterization of these putative probiotic species or species assemblages offers promising potential for preventative and therapeutic treatment development.
The pathogenesis of inflammatory disorders afflicting the skin is multifactorial. Srinivas et al. show that diversity of the skin microbiota is a critical factor determining the susceptibility to epidermolysis bullosa acquisita, a chronic mucocutaneous autoimmune skin blistering disease.
PMCID: PMC3778513  PMID: 24042968

Results 1-11 (11)