BACKGROUND: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations. RESULTS: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers. CONCLUSIONS: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

MOTIVATION: Protocols to generate strand-specific transcriptomes with next-generation sequencing platforms have been used by the scientific community roughly since 2008. Strand-specific reads allow for detection of antisense events and a higher resolution of expression profiles enabling extension of current transcript annotations. However, applications making use of this strandedness information are still scarce. RESULTS: Here we present a tool (Janus), which focuses on the identification of transcriptional active regions in antisense orientation to known and novel transcribed elements of the genome. Janus can compare the antisense events of multiple samples and assigns scores to identify mutual expression of either transcript in a sense/antisense pair, which could hint to regulatory mechanisms. Janus is able to make use of single-nucleotide variant (SNV) and methylation data, if available, and reports the sense to antisense ratio of regions in the vicinity of the identified genetic and epigenetic variation. Janus interrogates positions of heterozygous SNVs to identify strand-specific allelic imbalance. AVAILABILITY: Janus is written in C/C++ and freely available at http://www.ikmb.uni-kiel.de/janus/janus.html under terms of GNU General Public License, for both, Linux and Windows 64×. Although the binaries will work without additional downloads, the software depends on bamtools (https://github.com/pezmaster31/bamtools) for compilation. A detailed tutorial section is included in the first section of the supplemental material and included as brief readme.txt in the tutorial archive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Liquid chromatography mass spectrometry (LC-MS) maps in shotgun proteomics are often too complex to select every detected peptide signal for fragmentation by tandem mass spectrometry (MS/MS). Standard methods for precursor ion selection, commonly based on data dependent acquisition, select highly abundant peptide signals in each spectrum. However, these approaches produce redundant information and are biased towards high-abundance proteins. RESULTS: We present two algorithms for inclusion list creation that formulate precursor ion selection as an optimization problem. Given an LC-MS map, the first approach maximizes the number of selected precursors given constraints such as a limited number of acquisitions per RT fraction. Second, we introduce a protein sequence-based inclusion list that can be used to monitor proteins of interest. Given only the protein sequences, we create an inclusion list that optimally covers the whole protein set. Additionally, we propose an iterative precursor ion selection that aims at reducing the redundancy obtained with data dependent LC-MS/MS. We overcome the risk of erroneous assignments by including methods for retention time and proteotypicity predictions. We show that our method identifies a set of proteins requiring fewer precursors than standard approaches. Thus, it is well suited for precursor ion selection in experiments with limited sample amount or analysis time. CONCLUSIONS: We present three approaches to precursor ion selection with LC-MALDI MS/MS. Using a well-defined protein standard and a complex human cell lysate, we demonstrate that our methods outperform standard approaches. Our algorithms are implemented as part of OpenMS and are available under http://www.openms.de.

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

Transcription factors are proteins that regulate gene expression by binding to cis-regulatory sequences such as promoters and enhancers. In embryonic stem (ES) cells, binding of the transcription factors OCT4, SOX2 and NANOG is essential to maintain the capacity of the cells to differentiate into any cell type of the developing embryo. It is known that transcription factors interact to regulate gene expression. In this study we show that combinatorial binding is strongly associated with co-localization of the transcriptional co-activator Mediator, H3K27ac and increased expression of nearby genes in embryonic stem cells. We observe that the same loci bound by Oct4, Nanog and Sox2 in ES cells frequently drive expression in early embryonic development. Comparison of mouse and human ES cells shows that less than 5% of individual binding events for OCT4, SOX2 and NANOG are shared between species. In contrast, about 15% of combinatorial binding events and even between 53% and 63% of combinatorial binding events at enhancers active in early development are conserved. Our analysis suggests that the combination of OCT4, SOX2 and NANOG binding is critical for transcription in ES cells and likely plays an important role for embryogenesis by binding at conserved early developmental enhancers. Our data suggests that the fast evolutionary rewiring of regulatory networks mainly affects individual binding events, whereas "gene regulatory hotspots" which are bound by multiple factors and active in multiple tissues throughout early development are under stronger evolutionary constraints.

UNLABELLED: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments. AVAILABILITY: Available online at http://genge.molgen.mpg.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Biological Mass Spectrometry is used to analyse peptides and proteins. A mass spectrum generates a list of measured mass to charge ratios and intensities of ionised peptides, which is called a peak-list. In order to classify the underlying amino acid sequence, the acquired spectra are usually compared with synthetic ones. Development of suitable methods of direct peak-list comparison may be advantageous for many applications. RESULTS: The pairwise peak-list comparison is a multistage process composed of matching of peaks embedded in two peak-lists, normalisation, scaling of peak intensities and dissimilarity measures. In our analysis, we focused on binary and intensity based measures. We have modified the measures in order to comprise the mass spectrometry specific properties of mass measurement accuracy and non-matching peaks. We compared the labelling of peak-list pairs, obtained using different factors of the pairwise peak-list comparison, as being the same or different to those determined by sequence database searches. In order to elucidate how these factors influence the peak-list comparison we adopted an analysis of variance type method with the partial area under the ROC curve as a dependent variable. CONCLUSION: The analysis of variance provides insight into the relevance of various factors influencing the outcome of the pairwise peak-list comparison. For large MS/MS and PMF data sets the outcome of ANOVA analysis was consistent, providing a strong indication that the results presented here might be valid for many various types of peptide mass measurements.

BACKGROUND: Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. RESULTS: We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. CONCLUSIONS: d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data.

UNLABELLED: Xdigitise is a software system for visualization of hybridization experiments giving the user facilities to analyze the corresponding images manually or automatically. Images of the high-density DNA arrays are displayed as well as the results of an external image analysis bundled with Xdigitise, e.g. the spot locations are marked and the duplicate correlations are shown by a color scale. AVAILABILITY: Xdigitise can be downloaded from http://www.molgen.mpg.de/~xdigitise.

BACKGROUND: Gene expression analyses based on complex hybridization measurements have increased rapidly in recent years and have given rise to a huge amount of bioinformatic tools such as image analyses and cluster analyses. However, the amount of work done to integrate and evaluate these tools and the corresponding experimental procedures is not high. Although complex hybridization experiments are based on a data production pipeline that incorporates a significant amount of error parameters, the evaluation of these parameters has not been studied yet in sufficient detail. RESULTS: In this paper we present simulation studies on several error parameters arising in complex hybridization experiments. A general tool was developed that allows the design of exactly defined hybridization data incorporating, for example, variations of spot shapes, spot positions and local and global background noise. The simulation environment was used to judge the influence of these parameters on subsequent data analysis, for example image analysis and the detection of differentially expressed genes. As a guide for simulating expression data real experimental data were used and model parameters were adapted to these data. Our results show how measurement error can be balanced by the analysis tools. CONCLUSIONS: We describe an implemented model for the simulation of DNA-array experiments. This tool was used to judge the influence of critical parameters on the subsequent image analysis and differential expression analysis. Furthermore the tool can be used to guide future experiments and to improve performance by better experimental design. Series of simulated images varying specific parameters can be downloaded from our web-site: http://www.molgen.mpg.de/~lh_bioinf/projects/simulation/biotech/

MOTIVATION: Image analysis is a major part of data evaluation for array hybridization experiments in molecular biology. The program presented here is designed to analyze automatically images from hybridization experiments with various arrangements: different kinds of probes (oligonucleotides or complex probes), different supports (nylon filters or glass slides), different labeling of probes (radioactively or fluorescently). The program is currently applied to oligonucleotide fingerprinting projects and complex hybridizations. The only precondition for the use of the program is that the targets are arrayed in a grid, which can be approximately transformed to an orthogonal equidistant grid by a projective mapping. RESULTS: We demonstrate that our program can cope with the following problems: global distortion of the grid, missing of grid nodes, local deviation of the spot from its specified grid position. This is checked by different quality measures. The image analysis of oligonucleotide fingerprint experiments on an entire genetic library is used, in clustering procedures, to group related clones together. The results show that the program yields automatically generated high quality input data for follow up analysis such as clustering procedures. AVAILABILITY: The executable files will be available upon request for academics.

In this article we describe a method for characterization of large cDNA clone libraries based on oligonucleotide fingerprints (OFPs). The main advantage of this technique lies in that, without sequencing, each clone is tagged in an almost unique way, which has a couple of interesting applications, e.g. clustering of clones that belong to the same gene or gene family followed by sequencing of representative clones for each cluster. Moreover, small clusters are likely to represent rarely expressed genes, which are difficult to find by common approaches. We will demonstrate that in the EST projects carried out in our lab the global redundancy is very low compared to similar projects described in the literature, and simultaneously the number of unknown (novel) genes detected using this method is very high. In addition OFPs can be used directly for data base mining, since the sequences of the oligos matching a specific clone is known Recent results are presented, which underline the potential of our method in finding novel genes or genes homologous to known data. We will also address future applications in gene expression profiling, and give an outline of the various bioinformatics tools, which have been developed so far and which are used for automated data processing and analysis.

MOTIVATION: The choice of probes is an important feature of hybridisation experiments. In this paper we present an algorithm that optimises probes with respect to a training set of sequences based on Shannon entropy as a quality criterion. The practical motivation for our algorithm is oligonucleotide fingerprinting, a method for the simultaneous identification of sequences (cDNA or genomic DNA) by their hybridisation tags according to a set of short probes such as octamers, although the algorithm is of course not restricted to that application. RESULTS: We can show that our method is superior to the selection of probes according to their frequencies, which is a widely used strategy, and to randomly chosen probe sets. The quality of probe sets is assessed by a simulation pipeline that entails the set of probes as a simulation parameter. The performance of probe sets trained on sequences from different organisms shows additionally that probes should be chosen with regard to the organism under analysis. Case studies are presented on how constraints (G+C-content, complexity of the individual probes) influence the selection process. AVAILABILITY: A description of the oligonucleotide fingerprinting pipeline is published on our web-page http://www.molgen.mpg.de/ approximately ag_onf/met.htm. An executable of the algorithm and probe lists designed for human and rodents can be downloaded from the ftp-site ftp://ftp.molgen.mpg.de/pub/mpimg/probe_design/.

We have developed a novel algorithm for cluster analysis that is based on graph theoretic techniques. A similarity graph is defined and clusters in that graph correspond to highly connected subgraphs. A polynomial algorithm to compute them efficiently is presented. Our algorithm produces a clustering with some provably good properties. The application that motivated this study was gene expression analysis, where a collection of cDNAs must be clustered based on their oligonucleotide fingerprints. The algorithm has been tested intensively on simulated libraries and was shown to outperform extant methods. It demonstrated robustness to high noise levels. In a blind test on real cDNA fingerprint data the algorithm obtained very good results. Utilizing the results of the algorithm would have saved over 70% of the cDNA sequencing cost on that data set. 1 Introduction Cluster analysis seeks grouping of data elements into subsets, so that elements in the same subset are in some sense more cl...

MOTIVATION: With the main focus of the Human Genome Project shifting to sequencing, bioinformatics support for constructing large-scale genomic maps of other organisms is still required. We attempt to provide for this with our work, aimed at the delivery of robust and user-friendly contig-building software on the WWW. RESULTS: We present a prototype distributed analytical environment for molecular biologists working in the area of genomic mapping. It consists of the WWW server for constructing contigs from users' data with a hypertext output connected to Java-based map visualization software. AVAILABILITY: Freely available on http://www.mpimg-berlin-dahlem.mpg. de/ approximately andy/server/ CONTACT: [email protected]

MOTIVATION: Massive oligonucleotide hybridization is one of the most promising technologies of functional genome analysis. The critical point is to design appropriate sets of oligonucleotides that can be used effectively in identification by hybridization. RESULTS: Using a genetic algorithm approach, we have attempted to design sets of oligo probes capable of identifying new genes belonging to a defined gene family within a cDNA or genomic library. It is not limited by oligonucleotide length and admits the letter 'N' in the structure of the oligonucleotides selected. One of the major advantages of this approach is the low homology required to identify functional families of sequences with little homology. We have designed the oligonucleotide sets that are most selective for the cDNA clones of transmembrane G protein-coupled receptors (GPCRs), a large family of proteins that form part of a modular system of extracellular signal transduction to the intracellular second messenger pathways. The accuracy of identification has been checked on the EST library containing 713 870 cDNA sequences. A set of 15 oligos between 7 and 14 bases in length has correctly identified 70% of the GPCR cDNA collection sequences with 0.02% false positives. AVAILABILITY: The developed software is available by ftp://ftp.bionet.nsc. ru/pub/biology/ and on the Web page http://www.bionet.nsc. ru/SRCG/Oligoselector/. CONTACT: [email protected]; sebastian. [email protected]

MOTIVATION: In the past decade, a vast amount of mapping data has been generated on the human X chromosome, without a mechanism which would provide a global view of exactly what has been achieved. Large datasets are available electronically, but in heterogeneous formats and with incompatible access modes. In addition, relationships between objects in different datasets are often not specified. RESULTS: We discuss the problem of integrating these data into one database and define a number of requirements that are vital for any integration approach. We have developed IXDB, the Integrated X chromosome database, which fulfils those requirements and aims at providing a global view on genomic data at a chromosomal level. IXDB represents a conceptual framework based on identifying, storing and analysing relationships between biological objects, and includes a series of tools to automate the integration of such information. It currently focuses on physical mapping data, as a starting point towards a map of the human X chromosome that should provide a uniform and global research resource for ongoing and future sequencing and functional studies. AVAILABILITY: IXDB is available at http://ixdb.mpimg-berlin-dahlem.mpg.de. The iace2ixdb software and a description of the Iace data format are available from the authors. CONTACT: [email protected]

Here we describe progress on a series of molecular techniques designed to bridge the gap between genetic and molecular distances in mammals. This is an essential step in the molecular cloning of genes defined by mammalian mutations, and in the molecular analysis of large regions of mammalian genomes. We summarize approaches for the physical and molecular analysis of genetic distances and describe the experimental, statistical and computational basis of a new approach to create ordered libraries of overlapping clones from large genomes.