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  • 1
    Electronic Resource
    Electronic Resource
    Amsterdam : Elsevier
    Nuclear Instruments and Methods 190 (1981), S. 565-570 
    ISSN: 0029-554X
    Source: Elsevier Journal Backfiles on ScienceDirect 1907 - 2002
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Physics
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2015-06-14
    Description: : Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes. Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/ . Contact: renardb@rki.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2016-07-30
    Description: Motivation: Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms. Results: We propose a new method: DUDes, a reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic Next-generation sequencing (NGS) samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor we propose a new approach: the deepest uncommon descendent. We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision. Availability and Implementation: DUDes is open source and it is available at http://sf.net/p/dudes Supplementary information: Supplementary data are available at Bioinformatics online. Contact: renardB@rki.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 2016-09-02
    Description: Motivation: Horizontal gene transfer (HGT) is a fundamental mechanism that enables organisms such as bacteria to directly transfer genetic material between distant species. This way, bacteria can acquire new traits such as antibiotic resistance or pathogenic toxins. Current bioinformatics approaches focus on the detection of past HGT events by exploring phylogenetic trees or genome composition inconsistencies. However, these techniques normally require the availability of finished and fully annotated genomes and of sufficiently large deviations that allow detection and are thus not widely applicable. Especially in outbreak scenarios with HGT-mediated emergence of new pathogens, like the enterohemorrhagic Escherichia coli outbreak in Germany 2011, there is need for fast and precise HGT detection. Next-generation sequencing (NGS) technologies facilitate rapid analysis of unknown pathogens but, to the best of our knowledge, so far no approach detects HGTs directly from NGS reads. Results: We present Daisy, a novel mapping-based tool for HGT detection. Daisy determines HGT boundaries with split-read mapping and evaluates candidate regions relying on read pair and coverage information. Daisy successfully detects HGT regions with base pair resolution in both simulated and real data, and outperforms alternative approaches using a genome assembly of the reads. We see our approach as a powerful complement for a comprehensive analysis of HGT in the context of NGS data. Availability and Implementation: Daisy is freely available from http://github.com/ktrappe/daisy . Contact: renardb@rki.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 5
    Publication Date: 2015-06-03
    Description: Article In the emerging field of valleytronics, the valley degree of freedom of electrons is exploited in addition to charge and spin for novel functionalities. Here, Renard et al. show how valley polarization can facilitate spin-polarization in a silicon-on-insulator quantum well. Nature Communications doi: 10.1038/ncomms8230 Authors: V. T. Renard, B. A. Piot, X. Waintal, G. Fleury, D. Cooper, Y. Niida, D. Tregurtha, A. Fujiwara, Y. Hirayama, K. Takashina
    Electronic ISSN: 2041-1723
    Topics: Biology , Chemistry and Pharmacology , Natural Sciences in General , Physics
    Published by Springer Nature
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  • 6
    Publication Date: 2015-11-20
    Description: There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 7
    Publication Date: 2014-02-26
    Description: Motivation:  The reliable identification of genes is a major challenge in genome research, as further analysis depends on the correctness of this initial step. With high-throughput RNA-Seq data reflecting currently expressed genes, a particularly meaningful source of information has become commonly available for gene finding. However, practical application in automated gene identification is still not the standard case. A particular challenge in including RNA-Seq data is the difficult handling of ambiguously mapped reads. Results:  We present GIIRA (Gene Identification Incorporating RNA-Seq data and Ambiguous reads), a novel prokaryotic and eukaryotic gene finder that is exclusively based on a RNA-Seq mapping and inherently includes ambiguously mapped reads. GIIRA extracts candidate regions supported by a sufficient number of mappings and reassigns ambiguous reads to their most likely origin using a maximum-flow approach. This avoids the exclusion of genes that are predominantly supported by ambiguous mappings. Evaluation on simulated and real data and comparison with existing methods incorporating RNA-Seq information highlight the accuracy of GIIRA in identifying the expressed genes. Availability and implementation:  GIIRA is implemented in Java and is available from https://sourceforge.net/projects/giira/ . Contact:   renardB@rki.de Supplementary Information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 8
    Publication Date: 2016-03-26
    Description: Motivation: Isobaric labelling techniques such as iTRAQ and TMT are popular methods for relative protein abundance estimation in proteomic studies. However, measurements are assessed at the peptide spectrum level and exhibit substantial heterogeneity per protein. Hence, clever summarization strategies are required to infer protein ratios. So far, current methods rely exclusively on quantitative values, while additional information on peptides is available, yet it is not considered in these methods. Methods: We present iPQF ( i sobaric P rotein Q uantification based on F eatures) as a novel peptide-to-protein summarization method, which integrates peptide spectra characteristics as well as quantitative values for protein ratio estimation. We investigate diverse features characterizing spectra reliability and reveal significant correlations to ratio accuracy in spectra. As a result, we developed a feature-based weighting of peptide spectra. Results: A performance evaluation of iPQF in comparison to nine different protein ratio inference methods is conducted on five published MS2 and MS3 datasets with predefined ground truth. We demonstrate the benefit of using peptide feature information to improve protein ratio estimation. Compared to purely quantitative approaches, our proposed strategy achieves increased accuracy by addressing peptide spectra reliability. Availability and implementation: The iPQF algorithm is available within the established R/Bioconductor package MSnbase (version ≥ 1.17.8). Contact: renardB@rki.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 9
    Publication Date: 2013-05-16
    Description: Motivation: Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. Results: We introduce a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, we introduce distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. We introduce use cases with focus on metagenomics and develop new analysis strategies to assess the validity of a reference genome with respect to (meta-) genomic read data. The framework is evaluated on simulated data as well as applied to a large - scale metagenomic study, for which we compute the validity of 75 microbial genomes. The results indicate that the choice and quality of reference genomes is vital for metagenomic analyses and that validation of coverage profiles is crucial to avoid incorrect conclusions. Availability: The code is freely available and can be downloaded from http://sourceforge.net/projects/fitgcp/ . Contact: RenardB@rki.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 10
    Publication Date: 2014-06-17
    Description: Motivation: Metaproteomic analysis allows studying the interplay of organisms or functional groups and has become increasingly popular also for diagnostic purposes. However, difficulties arise owing to the high sequence similarity between related organisms. Further, the state of conservation of proteins between species can be correlated with their expression level, which can lead to significant bias in results and interpretation. These challenges are similar but not identical to the challenges arising in the analysis of metagenomic samples and require specific solutions. Results: We introduce Pipasic (peptide intensity-weighted proteome abundance similarity correction) as a tool that corrects identification and spectral counting-based quantification results using peptide similarity estimation and expression level weighting within a non-negative lasso framework. Pipasic has distinct advantages over approaches only regarding unique peptides or aggregating results to the lowest common ancestor, as demonstrated on examples of viral diagnostics and an acid mine drainage dataset. Availability and implementation: Pipasic source code is freely available from https://sourceforge.net/projects/pipasic/ . Contact: RenardB@rki.de Supplementary information: Supplementary data are available at Bioinformatics online
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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