ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

SUMMARY: Rapid technological advances have led to an explosion of biomedical data in recent years. The pace of change has inspired new collaborative approaches for sharing materials and resources to help train life scientists both in the use of cutting-edge bioinformatics tools and databases and in how to analyse and interpret large datasets. A prototype platform for sharing such training resources was recently created by the Bioinformatics Training Network (BTN). Building on this work, we have created a centralized portal for sharing training materials and courses, including a catalogue of trainers and course organizers, and an announcement service for training events. For course organizers, the portal provides opportunities to promote their training events; for trainers, the portal offers an environment for sharing materials, for gaining visibility for their work and promoting their skills; for trainees, it offers a convenient one-stop shop for finding suitable training resources and identifying relevant training events and activities locally and worldwide. AVAILABILITY AND IMPLEMENTATION: http://mygoblet.org/training-portal.

SUMMARY: Hadoop MapReduce-based approaches have become increasingly popular due to their scalability in processing large sequencing datasets. However, as these methods typically require in-depth expertise in Hadoop and Java, they are still out of reach of many bioinformaticians. To solve this problem, we have created SeqPig, a library and a collection of tools to manipulate, analyze and query sequencing datasets in a scalable and simple manner. SeqPigscripts use the Hadoop-based distributed scripting engine Apache Pig, which automatically parallelizes and distributes data processing tasks. We demonstrate SeqPig's scalability over many computing nodes and illustrate its use with example scripts. AVAILABILITY AND IMPLEMENTATION: Available under the open source MIT license at http://sourceforge.net/projects/seqpig/

The purpose of this article is to describe and analyse use-related problems when new information technology (IT) systems are adopted in organisations. Traditionally, IT adoption has been studied with the help of technology acceptance models (TAMs). However, an alternative approach, the activity system model, was used as a systemic and holistic tool to analyse and understand the problems relating to the adoption of an IT system in an organisational context. The data were collected using qualitative semi-structured interviews with 39 employees in three organisations. The results show that most of the problems were identified in the social context and only one-fifth of the problems were related to the employees' experiences of a lack of skills and competencies in using the IT systems. A practical implication is that the successful adoption and use of an IT tool requires interventions and innovations that also address the social mediatedness of the use. A theoretical implication is that the activity system model proved to be a useful yet rather complex tool for describing and analysing IT adoption problems.

SUMMARY: We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add submitted events, and these are subsequently accessed via web services. Thus, once an iAnn widget is incorporated into a website, it permanently shows timely relevant information as if it were native to the remote site. At the same time, announcements submitted to the repository are automatically disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available. AVAILABILITY: http://iann.pro/iannviewer CONTACT: [email protected].

Abstract Summary: Hadoop-BAM is a novel library for the scalable manipulation of aligned next-generation sequencing data in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM files that are processed using Hadoop. Hadoop-BAM solves the issues related to BAM data access by presenting a convenient API for implementing map and reduce functions that can directly operate on BAM records. It builds on top of the Picard SAM JDK, so tools that rely on the Picard API are expected to be easily convertible to support large-scale distributed processing. In this article we demonstrate the use of Hadoop-BAM by building a coverage summarizing tool for the Chipster genome browser. Our results show that Hadoop offers good scalability, and one should avoid moving data in and out of Hadoop between analysis steps. Availability: Available under the open-source MIT license at http://sourceforge.net/projects/hadoop-bam/ Contact: [email protected] Supplementary information: Supplementary material is available at Bioinformatics online.

Identification of reliable molecular markers that show differential expression between distinct groups of samples has remained a fundamental research problem in many large-scale profiling studies, such as those based on DNA microarray or mass-spectrometry technologies. Despite the availability of a wide spectrum of statistical procedures, the users of the high-throughput platforms are still facing the crucial challenge of deciding which test statistic is best adapted to the intrinsic properties of their own datasets. To meet this challenge, we recently introduced an adaptive procedure, named ROTS (Reproducibility-Optimized Test Statistic), which learns an optimal statistic directly from the given data, and whose relative benefits have previously been shown in comparison with state-of-the-art procedures for detecting differential expression. Using gene expression microarray and mass-spectrometry (MS)-based protein expression datasets as case studies, we illustrate here the practical usage and advantages of ROTS toward detecting reliable marker lists in clinical transcriptomic and proteomic studies. In a public leukemia microarray dataset, the procedure could improve the sensitivity of the gene marker lists detected with high specificity. When applied to a recent LC-MS dataset, involving plasma samples from severe burn patients, the procedure could identify several peptide markers that remained undetected in the conventional analysis, thus demonstrating the effectiveness of ROTS also for global quantitative proteomic studies. To promote its widespread usage, we have made freely available efficient implementations of ROTS, which are easily accessible either as a stand-alone R-package or as integrated in the open-source data analysis software Chipster.

Programmatic access to data and tools through the web using so-called web services has an important role to play in bioinformatics. In this article, we discuss the most popular approaches based on SOAP/WS-I and REST and describe our, a cross section of the community, experiences with providing and using web services in the context of biological sequence analysis. We briefly review main technological approaches as well as best practice hints that are useful for both users and developers. Finally, syntactic and semantic data integration issues with multiple web services are discussed.