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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2016
    In:  BMC Genomics Vol. 17, No. S8 ( 2016-10)
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 17, No. S8 ( 2016-10)
    Type of Medium: Online Resource
    ISSN: 1471-2164
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2041499-7
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    SAGE Publications ; 2019
    In:  Bioinformatics and Biology Insights Vol. 13 ( 2019-01), p. 117793221882512-
    In: Bioinformatics and Biology Insights, SAGE Publications, Vol. 13 ( 2019-01), p. 117793221882512-
    Abstract: The eclosion of data acquisition technologies has shifted the bottleneck in molecular biology research from data acquisition to data analysis. Such is the case in Comparative Genomics, where sequence analysis has transitioned from genes to genomes of several orders of magnitude larger. This fact has revealed the need to adapt software to work with huge experiments efficiently and to incorporate new data-analysis strategies to manage results from such studies. In previous works, we presented GECKO, a software to compare large sequences; now we address the representation, browsing, data exploration, and post-processing of the massive amount of information derived from such comparisons. GECKO-MGV is a web-based application organized as client-server architecture. It is aimed at visual analysis of the results from both pairwise and multiple sequences comparison studies combining a set of common commands for image exploration with improved state-of-the-art solutions. In addition, GECKO-MGV integrates different visualization analysis tools while exploiting the concept of layers to display multiple genome comparison datasets. Moreover, the software is endowed with capabilities for contacting external-proprietary and third-party services for further data post-processing and also presents a method to display a timeline of large-scale evolutionary events. As proof-of-concept, we present 2 exercises using bacterial and mammalian genomes which depict the capabilities of GECKO-MGV to perform in-depth, customizable analyses on the fly using web technologies. The first exercise is mainly descriptive and is carried out over bacterial genomes, whereas the second one aims to show the ability to deal with large sequence comparisons. In this case, we display results from the comparison of the first Homo sapiens chromosome against the first 5 chromosomes of Mus musculus.
    Type of Medium: Online Resource
    ISSN: 1177-9322 , 1177-9322
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2423808-9
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2018
    In:  Bioinformatics Vol. 34, No. 5 ( 2018-03-01), p. 869-870
    In: Bioinformatics, Oxford University Press (OUP), Vol. 34, No. 5 ( 2018-03-01), p. 869-870
    Abstract: Nearly 10 years have passed since the first mobile apps appeared. Given the fact that bioinformatics is a web-based world and that mobile devices are endowed with web-browsers, it seemed natural that bioinformatics would transit from personal computers to mobile devices but nothing could be further from the truth. The transition demands new paradigms, designs and novel implementations. Results Throughout an in-depth analysis of requirements of existing bioinformatics applications we designed and deployed an easy-to-use web-based lightweight mobile client. Such client is able to browse, select, compose automatically interface parameters, invoke services and monitor the execution of Web Services using the service's metadata stored in catalogs or repositories. Availability and implementation mORCA is available at http://bitlab-es.com/morca/app as a web-app. It is also available in the App store by Apple and Play Store by Google. The software will be available for at least 2 years. Supplementary information Source code, final web-app, training material and documentation is available at http://bitlab-es.com/morca
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Mary Ann Liebert Inc ; 2018
    In:  Journal of Computational Biology Vol. 25, No. 8 ( 2018-08), p. 841-849
    In: Journal of Computational Biology, Mary Ann Liebert Inc, Vol. 25, No. 8 ( 2018-08), p. 841-849
    Type of Medium: Online Resource
    ISSN: 1557-8666
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2018
    detail.hit.zdb_id: 2030900-4
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  • 5
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Bioinformatics and Biology Insights Vol. 15 ( 2021-01), p. 117793222110214-
    In: Bioinformatics and Biology Insights, SAGE Publications, Vol. 15 ( 2021-01), p. 117793222110214-
    Abstract: Due to major breakthroughs in sequencing technologies throughout the last decades, the time and cost per sequencing experiment have reduced drastically, overcoming the data generation barrier during the early genomic era. Such a shift has encouraged the scientific community to develop new computational methods that are able to compare large genomic sequences, thus enabling large-scale studies of genome evolution. The field of comparative genomics has proven itself invaluable for studying the evolutionary mechanisms and the forces driving genome evolution. In this line, a full genome comparison study between 2 species requires a quadratic number of comparisons in terms of the number of sequences (around 400 chromosome comparisons in the case of mammalian genomes); however, when studying conserved syntenies or evolutionary rearrangements, many sequence comparisons can be skipped for not all will contain significant signals. Subsequently, the scientific community has developed fast heuristics to perform multiple pairwise comparisons between large sequences to determine whether significant sets of conserved similarities exist. The data generation problem is no longer an issue, yet the limitations have shifted toward the analysis of such massive data. Therefore, we present XCout, a Web-based visual analytics application for multiple genome comparisons designed to improve the analysis of large-scale evolutionary studies using novel techniques in Web visualization. XCout enables to work on hundreds of comparisons at once, thus reducing the time of the analysis by identifying significant signals between chromosomes across multiple species. Among others, XCout introduces several techniques to aid in the analysis of large-scale genome rearrangements, particularly (1) an interactive heatmap interface to display comparisons using automatic color scales based on similarity thresholds to ease detection at first sight, (2) an overlay system to detect individual signal contributions between chromosomes, (3) a tracking tool to trace conserved blocks across different species to perform evolutionary studies, and (4) a search engine to search annotations throughout different species.
    Type of Medium: Online Resource
    ISSN: 1177-9322 , 1177-9322
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2423808-9
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Bioinformatics Vol. 36, No. 14 ( 2020-07-30), p. 4203-4205
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 14 ( 2020-07-30), p. 4203-4205
    Abstract: Molecular docking is aimed at predicting the conformation of small-molecule (ligands) within an identified binding site (BS) in a target protein (receptor). Protein–ligand docking plays an important role in modern drug discovery and biochemistry for protein engineering. However, efficient docking analysis of proteins requires prior knowledge of the BS, which is not always known. The process which covers BS identification and protein–ligand docking usually requires the combination of different programs, which require several input parameters. This is furtherly aggravated when factoring in computational demands, such as CPU-time. Therefore, these types of simulation experiments can become a complex process for researchers without a background in computer sciences. Results To overcome these problems, we have designed an automatic computational workflow (WF) to process protein–ligand complexes, which runs from the identification of the possible BSs positions to the prediction of the experimental binding modes and affinities of the ligand. This open-access WF runs under the Galaxy platform that integrates public domain software. The results of the proposed method are in close agreement with state-of-the-art docking software. Availability and implementation Software is available at: https://pistacho.ac.uma.es/galaxy-bitlab. Contact euv@uma.es 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: 2020
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2019
    In:  Scientific Reports Vol. 9, No. 1 ( 2019-07-16)
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2019-07-16)
    Abstract: In the last decade, a technological shift in the bioinformatics field has occurred: larger genomes can now be sequenced quickly and cost effectively, resulting in the computational need to efficiently compare large and abundant sequences. Furthermore, detecting conserved similarities across large collections of genomes remains a problem. The size of chromosomes, along with the substantial amount of noise and number of repeats found in DNA sequences (particularly in mammals and plants), leads to a scenario where executing and waiting for complete outputs is both time and resource consuming. Filtering steps, manual examination and annotation, very long execution times and a high demand for computational resources represent a few of the many difficulties faced in large genome comparisons. In this work, we provide a method designed for comparisons of considerable amounts of very long sequences that employs a heuristic algorithm capable of separating noise and repeats from conserved fragments in pairwise genomic comparisons. We provide software implementation that computes in linear time using one core as a minimum and a small, constant memory footprint. The method produces both a previsualization of the comparison and a collection of indices to drastically reduce computational complexity when performing exhaustive comparisons. Last, the method scores the comparison to automate classification of sequences and produces a list of detected synteny blocks to enable new evolutionary studies.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2615211-3
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  The Journal of Supercomputing Vol. 79, No. 8 ( 2023-05), p. 8699-8728
    In: The Journal of Supercomputing, Springer Science and Business Media LLC, Vol. 79, No. 8 ( 2023-05), p. 8699-8728
    Abstract: The use of Graphics Processing Units to accelerate computational applications is increasingly being adopted due to its affordability, flexibility and performance. However, achieving top performance comes at the price of restricted data-parallelism models. In the case of sequence alignment, most GPU-based approaches focus on accelerating the Smith-Waterman dynamic programming algorithm due to its regularity. Nevertheless, because of its quadratic complexity, it becomes impractical when comparing long sequences, and therefore heuristic methods are required to reduce the search space. We present GPUGECKO, a CUDA implementation for the sequential, seed-and-extend sequence-comparison algorithm, GECKO. Our proposal includes optimized kernels based on collective operations capable of producing arbitrarily long alignments while dealing with heterogeneous and unpredictable load. Contrary to other state-of-the-art methods, GPUGECKO employs a batching mechanism that prevents memory exhaustion by not requiring to fit all alignments at once into the device memory, therefore enabling to run massive comparisons exhaustively with improved sensitivity while also providing up to 6x average speedup w.r.t. the CUDA acceleration of BLASTN.
    Type of Medium: Online Resource
    ISSN: 0920-8542 , 1573-0484
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1479917-0
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  • 9
    In: PLOS ONE, Public Library of Science (PLoS), Vol. 12, No. 7 ( 2017-7-18), p. e0181503-
    Type of Medium: Online Resource
    ISSN: 1932-6203
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2017
    detail.hit.zdb_id: 2267670-3
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  • 10
    Online Resource
    Online Resource
    Elsevier BV ; 2018
    In:  Heliyon Vol. 4, No. 12 ( 2018-12), p. e01057-
    In: Heliyon, Elsevier BV, Vol. 4, No. 12 ( 2018-12), p. e01057-
    Type of Medium: Online Resource
    ISSN: 2405-8440
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2018
    detail.hit.zdb_id: 2835763-2
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