Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

MOTIVATION: The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. RESULTS: We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap's successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. AVAILABILITY: https://bitbucket.org/ibima/moldrugeffectsdb. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

BACKGROUND: Next generation sequencing (NGS) technologies enable studies and analyses of the diversity of both T and B cell receptors (TCR and BCR) in human and animal systems to elucidate immune functions in health and disease. Over the last few years, several algorithms and tools have been developed to support respective analyses of raw sequencing data of the immune repertoire. These tools focus on distinct aspects of the data processing and require a strong bioinformatics background. To facilitate the analysis of T and B cell repertoires by less experienced users, software is needed that combines the most common tools for repertoire analysis. RESULTS: We introduce a graphical user interface (GUI) providing a complete analysis pipeline for processing raw NGS data for human and animal TCR and BCR clonotype determination and advanced differential repertoire studies. It provides two applications. ClonoCalc prepares the raw data for downstream analyses. It combines a demultiplexer for barcode splitting and employs MiXCR for paired-end read merging and the extraction of human and animal TCR/BCR sequences. ClonoPlot wraps the R package tcR and further contributes self-developed plots for the descriptive comparative investigation of immune repertoires. CONCLUSION: This workflow reduces the amount of programming required to perform the respective analyses and supports both communication and training between scientists and technicians, and across scientific disciplines. The Open Source development in Java and R is modular and invites advanced users to extend its functionality. Software and documentation are freely available at https://bitbucket.org/ClonoSuite/clonocalc-plot .

Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in data-driven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

In silico molecular docking is used to predict how a small molecule, the ligand, interacts with a target protein, its receptor. Together with experimental methods like NMR or X-ray crystallography, industrial and academic groups use it for their investigation of compounds with the potential to modulate the protein's function and become a lead molecule for drug development. The interpretation of raw data, from NMR, mass spectrometry or crystallography, is greatly assisted by computers. Biochemists can, perhaps better than anybody else, perform individual analyses for the computational modeling of interactions. However, an extension towards virtual screening of compound libraries, i.e. the computational docking of thousands or even millions of ligands to a target receptor, is often perceived as technically challenging and computationally too expensive in the biochemical community. Here we describe how to integrate spare resources of regular desktop computers on and off campus using the Berkeley Open Infrastructure for Network Computing (BOINC) infrastructure for volunteer grid computing. We have brought both the BOINC server and the Auto Dock software suite into the Debian and Ubuntu Linux distributions, and provide detailed instructions to help render the implementation of a large-scale high-throughput docking (HTD) project straightforward. Thus, this increased availability of computational resources, protocols and source code opens up the possibility of many new self-run projects and collaborations, for those who may be adopting the technology for the first time. On the technical level, we expect to observe more contributors from the Open Source and academic communities. On the biological side, we anticipate new and faster progress on commercially less interesting and so-called 'neglected' diseases.

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.

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 http://www.xqtl.org. CONTACT: [email protected].

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

SUMMARY: This work presents two independent approaches for a seamless integration of computational grids with the bioinformatics workflow suite Taverna. These are supported by a unique relational database to link applications with grid resources and presents those as workflow elements. A web portal facilitates its collaborative maintenance. The first approach implements a gateway service to handle authentication certificates and all communication with the grid. It reads the database to spawn web services for workflow elements which are in turn used by Taverna. The second approach lets Taverna communicate with the grid on its own, by means of a newly developed plug-in. It reads the database and executes the needed tasks directly on the grid. While the gateway service is non-intrusive, the plug-in has technical advantages, e.g. by allowing data to remain on the grid while being passed between workflow elements. AVAILABILITY: http://grid.inb.uni-luebeck.de/

UNLABELLED: For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data. AVAILABILITY: A web interface and source code are offered at https://webtools.imbs.uni-luebeck.de/snptogo

The adoption of agent technologies and multi-agent systems constitutes an emerging area in bioinformatics. In this article, we report on the activity of the Working Group on Agents in Bioinformatics (BIOAGENTS) founded during the first AgentLink III Technical Forum meeting on the 2nd of July, 2004, in Rome. The meeting provided an opportunity for seeding collaborations between the agent and bioinformatics communities to develop a different (agent-based) approach of computational frameworks both for data analysis and management in bioinformatics and for systems modelling and simulation in computational and systems biology. The collaborations gave rise to applications and integrated tools that we summarize and discuss in context of the state of the art in this area. We investigate on future challenges and argue that the field should still be explored from many perspectives ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages to be used by information agents, and to the adoption of agents for computational grids.

UNLABELLED: Genetic linkage and association studies define quantitative trait loci (QTLs) and susceptibility loci (SLs) that influence the phenotype of polygenic traits. A web-accessible application was created to identify intergenomic consensuses to fine map QTLs and SLs in silico and select particularly promising candidate genes for such traits. Furthermore, this approach offers an empirical evaluation of animal models for their applicability to the study of human traits. AVAILABILITY: http://qtl.pzr.uni-rostock.de/qtlmix.php CONTACT: [email protected].

UNLABELLED: A supervised nonlinear interpolation significantly improves the reliability of conversions from genetic distances to physical distances as compared with the linear ones. A webaccessible application was created that addresses this question with a graphical presentation that may be wrapped by local installations. MOTIVATION: Genetic linkage maps and radiation hybrid (RH) maps are based on the rate of uncoupling between linked genetic markers. These are usually measured in centiMorgan (cM) when uncoupling is originated by natural recombination or in centiRay (cR) for chromosomes that are irradiated artificially to separate the markers. Physical maps arise from genome-wide DNA sequencing and are measured in bp. This work was originally motivated as an extension of the software application Expressionview (Fischer et al., 2003), exploring its spectrum of appliance combining different mapping systems. The relationship between physical and genetic maps is known to be not always linear (Yu et al., 2001). The shift from the linear model seems to depend on local idiosyncrasies of the chromosomes and the kind of genetic map used. The present application addresses this problem for the first time. AVAILABILITY: http://qtl.pzr.uni-rostock.de/cartographer.php

Steffen Möller, Michael D. R. Croning, Rolf Apweiler; Evaluation of methods for the prediction of membrane spanning regions , Bioinformatics, Volume 18, Is

MOTIVATION: A variety of tools are available to predict the topology of transmembrane proteins. To date no independent evaluation of the performance of these tools has been published. A better understanding of the strengths and weaknesses of the different tools would guide both the biologist and the bioinformatician to make better predictions of membrane protein topology. RESULTS: Here we present an evaluation of the performance of the currently best known and most widely used methods for the prediction of transmembrane regions in proteins. Our results show that TMHMM is currently the best performing transmembrane prediction program.

Abstract Summary: A collection of transmembrane proteins with annotated transmembrane regions, for which good experimental evidence exist, was created as a test or training set for algorithms to predict transmembrane regions in proteins. Availability: ftp://ftp.ebi.ac.uk/databases/testsets/transmembrane Contact: [email protected]

MOTIVATION: To cope with the increasing amount of sequence data, reliable automatic annotation tools are required. The TrEMBL database contains together with SWISS-PROT nearly all publicly available protein sequences, but in contrast to SWISS-PROT only limited functional annotation. To improve this situation, we had to develop a method of automatic annotation that produces highly reliable functional prediction using the language and the syntax of SWISS-PROT. RESULTS: An algorithm was developed and successfully used for the automatic annotation of a testset of unknown proteins. The predicted information included description, function, catalytic activity, cofactors, pathway, subcellular location, quaternary structure, similarity to other protein, active sites, and keywords. The algorithm showed a low coverage (10%), but a high specificity and reliability. AVAILABILITY: The results can be obtained by anonymous ftp from ftp.ebi.ac.uk/pub/databases/sp_tr_nrdb. The source code is available on request from the authors.

SUMMARY: Many databases in molecular biology face the problem that the ever increasing rate of data production can no longer be handled by traditional methods, especially human curation. Therefore, a number of projects are currently investigating methods for automated sequence annotation. This paper describes the EBI's approach to this problem for protein sequences by integration of arbitrary analysis programs into a distributed and highly flexible environment. Our software framework allows an individual treatment of sequences depending on their particular properties, which is achieved through a high-level description of the preconditions and capabilities of analysing modules. This not only improves the overall performance of the annotation process, as unnecessary steps are avoided, but also enhances its quality since dependencies between different modules are taken into account. We have implemented a prototype and use it in the production of TrEMBL releases. AVAILABILITY: Upon request.