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  • 1
    In: Proteins: Structure, Function, and Bioinformatics, Wiley, Vol. 83, No. 10 ( 2015-10), p. 1887-1899
    Type of Medium: Online Resource
    ISSN: 0887-3585 , 1097-0134
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1475032-6
    SSG: 12
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  • 2
    In: Bioinformatics, Oxford University Press (OUP), Vol. 31, No. 1 ( 2015-01-01), p. 121-122
    Abstract: Motivation: Web-based workflow systems have gained considerable momentum in sequence-oriented bioinformatics. In structural bioinformatics, however, such systems are still relatively rare; while commercial stand-alone workflow applications are common in the pharmaceutical industry, academic researchers often still rely on command-line scripting to glue individual tools together. Results: In this work, we address the problem of building a web-based system for workflows in structural bioinformatics. For the underlying molecular modelling engine, we opted for the BALL framework because of its extensive and well-tested functionality in the field of structural bioinformatics. The large number of molecular data structures and algorithms implemented in BALL allows for elegant and sophisticated development of new approaches in the field. We hence connected the versatile BALL library and its visualization and editing front end BALLView with the Galaxy workflow framework. The result, which we call ballaxy, enables the user to simply and intuitively create sophisticated pipelines for applications in structure-based computational biology, integrated into a standard tool for molecular modelling. Availability and implementation:  ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools and an integration into the BALL framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy . Also, docker images for ballaxy are available at https://registry.hub.docker.com/u/anhi/ballaxy/dockerfile/ . ballaxy is licensed under the terms of the GPL. Supplementary information : Supplementary data are available at Bioinformatics online. Contact:  anna.hildebrandt@bioinf.uni-sb.de or andreas.hildebrandt@uni-mainz.de
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 7 ( 2021-05-17), p. 889-895
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 7 ( 2021-05-17), p. 889-895
    Abstract: Error correction is a fundamental pre-processing step in many Next-Generation Sequencing (NGS) pipelines, in particular for de novo genome assembly. However, existing error correction methods either suffer from high false-positive rates since they break reads into independent k-mers or do not scale efficiently to large amounts of sequencing reads and complex genomes. Results We present CARE—an alignment-based scalable error correction algorithm for Illumina data using the concept of minhashing. Minhashing allows for efficient similarity search within large sequencing read collections which enables fast computation of high-quality multiple alignments. Sequencing errors are corrected by detailed inspection of the corresponding alignments. Our performance evaluation shows that CARE generates significantly fewer false-positive corrections than state-of-the-art tools (Musket, SGA, BFC, Lighter, Bcool, Karect) while maintaining a competitive number of true positives. When used prior to assembly it can achieve superior de novo assembly results for a number of real datasets. CARE is also the first multiple sequence alignment-based error corrector that is able to process a human genome Illumina NGS dataset in only 4 h on a single workstation using GPU acceleration. Availabilityand implementation CARE is open-source software written in C++ (CPU version) and in CUDA/C++ (GPU version). It is licensed under GPLv3 and can be downloaded at https://github.com/fkallen/CARE. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 23 ( 2017-12-01), p. 3740-3748
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 23 ( 2017-12-01), p. 3740-3748
    Abstract: Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy. Results We introduce MetaCache—a novel software for read classification using the big data technique minhashing. Our approach performs context-aware classification of reads by computing representative subsamples of k-mers within both, probed reads and locally constrained regions of the reference genomes. As a result, MetaCache consumes significantly less memory compared to the state-of-the-art read classifiers Kraken and CLARK while achieving highly competitive sensitivity and precision at comparable speed. For example, using NCBI RefSeq draft and completed genomes with a total length of around 140 billion bases as reference, MetaCache’s database consumes only 62 GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512 GB RAM. Our experimental results further show that classification accuracy continuously improves when increasing the amount of utilized reference genome data. Availability and implementation MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 5
    In: ELECTROPHORESIS, Wiley, Vol. 27, No. 13 ( 2006-07), p. 2734-2746
    Abstract: Due to their extensive structural heterogeneity, the elucidation of glycosylation patterns in glycoproteins such as the subunits of human chorionic gonadotropin (hCG), hCG‐α, and hCG‐β, remains one of the most challenging problems in the proteomic analysis of post‐translational modifications. In consequence, glycosylation is usually studied after decomposition of the intact proteins to the proteolytic peptide level. However, by this approach all information about the combination of the different glycopeptides in the intact protein is lost. In this study we have, therefore, attempted to combine the results of glycan identification after tryptic digestion with molecular mass measurements on the native starting material of the new first WHO Reference Reagents (RR) for hCG‐α (99/720) and hCG‐β (99/650). Despite the extremely high number of possible combinations of the glycans identified in the tryptic peptides by HPLC‐MS ( 〉 1000 for hCG‐α and 〉 10 000 for hCG‐β), the mass spectra of intact hCG‐α and hCG‐β revealed only a limited number of glycoforms present in hCG preparations from pools of pregnancy urines. Peak annotations for hCG‐α were performed with the help of a bioinformatic algorithm that generated a database containing all possible modifications of the proteins, including modifications possibly introduced during sample preparation such as oxidation or truncation, for subsequent searches for combinations fitting the mass difference between the polypeptide backbone and the measured molecular masses. Fourteen different glycoforms of hCG‐α, containing biantennary, partly sialylized hybrid‐type glycans, including methionine‐oxidized and N ‐terminally truncated forms, were identified. Mass spectra of high quality were also obtained for hCG‐β, however, a database search mass accuracy of ±5 Da was insufficient to unambiguously assign the possible combinations of post‐translational modifications. In summary, mass spectrometric fingerprints of intact molecules were shown to be highly useful for the characterization of glycosylation patterns of different hCG preparations such as the new first WHO RR for immunoassays and could be the first step in establishing biophysical reference methods for hCG and related molecules.
    Type of Medium: Online Resource
    ISSN: 0173-0835 , 1522-2683
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2006
    detail.hit.zdb_id: 1475486-1
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2013
    In:  BMC Bioinformatics Vol. 14, No. 1 ( 2013-12)
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2013-12)
    Abstract: NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model. A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. Results In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction. In addition to this main result - the NightShift framework itself - we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline. Conclusion By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators. The framework can be downloaded from https://bitbucket.org/akdehof/nightshift . It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/ .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 7
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2022-12)
    Abstract: Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties. Results In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. Real data could be strongly reduced without losing relevant information. Our implementation scaled out up to 32 threads and supports acceleration by GPUs. Conclusions Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Availability Generated data and code are available at https://github.com/hildebrandtlab/mzBucket . Raw data is available at https://zenodo.org/record/5036526 .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 8
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2008-12)
    Type of Medium: Online Resource
    ISSN: 1471-2105
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2008
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2016
    In:  Nucleic Acids Research Vol. 44, No. 2 ( 2016-01-29), p. e19-e19
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 44, No. 2 ( 2016-01-29), p. e19-e19
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 10
    Online Resource
    Online Resource
    Walter de Gruyter GmbH ; 2006
    In:  Journal of Integrative Bioinformatics Vol. 3, No. 2 ( 2006-12-1), p. 148-161
    In: Journal of Integrative Bioinformatics, Walter de Gruyter GmbH, Vol. 3, No. 2 ( 2006-12-1), p. 148-161
    Abstract: Recent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications. Availability: BN++ is open source software available from our website at www.bnplusplus.org.
    Type of Medium: Online Resource
    ISSN: 1613-4516
    Language: Unknown
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2006
    detail.hit.zdb_id: 2147212-9
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