Motivation: The human leukocyte antigen (HLA) gene cluster plays a crucial role in adaptive immunity and is thus relevant in many biomedical applications. While next-generation sequencing data are often available for a patient, deducing the HLA genotype is difficult because of substantial sequence similarity within the cluster and exceptionally high variability of the loci. Established approaches, therefore, rely on specific HLA enrichment and sequencing techniques, coming at an additional cost and extra turnaround time. Result: We present OptiType, a novel HLA genotyping algorithm based on integer linear programming, capable of producing accurate predictions from NGS data not specifically enriched for the HLA cluster. We also present a comprehensive benchmark dataset consisting of RNA, exome and whole-genome sequencing data. OptiType significantly outperformed previously published in silico approaches with an overall accuracy of 97% enabling its use in a broad range of applications. Contact:szolek@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Mass spectrometry is a fundamental tool for discovery and analysis in the life sciences. With the rapid advances in mass spectrometry technology and methods, it has become imperative to provide a standard output format for mass spectrometry data that will facilitate data sharing and analysis. Initially, the efforts to develop a standard format for mass spectrometry data resulted in multiple formats, each designed with a different underlying philosophy. To resolve the issues associated with having multiple formats, vendors, researchers, and software developers convened under the banner of the HUPO PSI to develop a single standard. The new data format incorporated many of the desirable technical attributes from the previous data formats, while adding a number of improvements, including features such as a controlled vocabulary with validation tools to ensure consistent usage of the format, improved support for selected reaction monitoring data, and immediately available implementations to facilitate rapid adoption by the community. The resulting standard data format, mzML, is a well tested open-source format for mass spectrometer output files that can be readily utilized by the community and easily adapted for incremental advances in mass spectrometry technology.

Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow. We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies. OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at http://www.openms.de .

We present, on behalf of EuroGentest and the European Society of Human Genetics, guidelines for the evaluation and validation of next-generation sequencing (NGS) applications for the diagnosis of genetic disorders. The work was performed by a group of laboratory geneticists and bioinformaticians, and discussed with clinical geneticists, industry and patients’ representatives, and other stakeholders in the field of human genetics. The statements that were written during the elaboration of the guidelines are presented here. The background document and full guidelines are available as supplementary material. They include many examples to assist the laboratories in the implementation of NGS and accreditation of this service. The work and ideas presented by others in guidelines that have emerged elsewhere in the course of the past few years were also considered and are acknowledged in the full text. Interestingly, a few new insights that have not been cited before have emerged during the preparation of the guidelines. The most important new feature is the presentation of a ‘rating system’ for NGS-based diagnostic tests. The guidelines and statements have been applauded by the genetic diagnostic community, and thus seem to be valuable for the harmonization and quality assurance of NGS diagnostics in Europe.

Motivation: Experimental techniques in proteomics have seen rapid development over the last few years. Volume and complexity of the data have both been growing at a similar rate. Accordingly, data management and analysis are one of the major challenges in proteomics. Flexible algorithms are required to handle changing experimental setups and to assist in developing and validating new methods. In order to facilitate these studies, it would be desirable to have a flexible ‘toolbox’ of versatile and user-friendly applications allowing for rapid construction of computational workflows in proteomics. Results: We describe a set of tools for proteomics data analysis—TOPP, The OpenMS Proteomics Pipeline. TOPP provides a set of computational tools which can be easily combined into analysis pipelines even by non-experts and can be used in proteomics workflows. These applications range from useful utilities (file format conversion, peak picking) over wrapper applications for known applications (e.g. Mascot) to completely new algorithmic techniques for data reduction and data analysis. We anticipate that TOPP will greatly facilitate rapid prototyping of proteomics data evaluation pipelines. As such, we describe the basic concepts and the current abilities of TOPP and illustrate these concepts in the context of two example applications: the identification of peptides from a raw dataset through database search and the complex analysis of a standard addition experiment for the absolute quantitation of biomarkers. The latter example demonstrates TOPP's ability to construct flexible analysis pipelines in support of complex experimental setups. Availability: The TOPP components are available as open-source software under the lesser GNU public license (LGPL). Source code is available from the project website at Author Webpage Contact:oliver.kohlbacher@uni-tuebingen.de

Background: Trimming of adapter sequences from short read data is a common preprocessing step during NGS data analysis. When performing paired-end sequencing, the overlap between forward and reverse read can be used to identify excess adapter sequences. This is exploited by several previously published adapter trimming tools. However, our evaluation on amplicon-based data shows that most of the current tools are not able to remove all adapter sequences and that adapter contamination may even lead to spurious variant calls. Results: Here we present SeqPurge (https://github.com/imgag/ngs-bits), a highly-sensitive adapter trimmer that uses a probabilistic approach to detect the overlap between forward and reverse reads of Illumina sequencing data. SeqPurge can detect very short adapter sequences, even if only one base long. Compared to other adapter trimmers specifically designed for paired-end data, we found that SeqPurge achieves a higher sensitivity. The number of remaining adapter bases after trimming is reduced by up to 90 %, depending on the compared tool. In simulations with different error rates, we found that SeqPurge is also the most error-tolerant adapter trimmer in the comparison. Conclusion: SeqPurge achieves a very high sensitivity and a high error-tolerance, combined with a specificity and runtime that are comparable to other state-of-the-art adapter trimmers. The very good adapter trimming performance, complemented with additional features such as quality-based trimming and basic quality control, makes SeqPurge an excellent choice for the pre-processing of paired-end NGS data.