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  • 1
    Online Resource
    Online Resource
    Mapping the human genetic architecture of COVID-19
    Springer Science and Business Media LLC ; 2021
    In:  Nature Vol. 600, No. 7889 ( 2021-12-16), p. 472-477
    Details
    In: Nature, Springer Science and Business Media LLC, Vol. 600, No. 7889 ( 2021-12-16), p. 472-477
    Abstract: The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    URL: Article
    DOI: 10.1038/s41586-021-03767-x
    RVK:
    TA 1000
    RVK:
    UA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 2
    Online Resource
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    Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 19, No. 1 ( 2022-1-1), p. 121-134
    Details
    In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 19, No. 1 ( 2022-1-1), p. 121-134
    Type of Medium: Online Resource
    ISSN: 1545-5963 , 1557-9964 , 2374-0043
    URL: Article
    DOI: 10.1109/TCBB.2020.3042309
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 2158957-4
    SSG: 12
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  • 3
    Online Resource
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    High Performance Integration Pipeline for Viral and Epitope Sequences
    MDPI AG ; 2022
    In:  BioTech Vol. 11, No. 1 ( 2022-03-21), p. 7-
    Details
    In: BioTech, MDPI AG, Vol. 11, No. 1 ( 2022-03-21), p. 7-
    Abstract: With the spread of COVID-19, sequencing laboratories started to share hundreds of sequences daily. However, the lack of a commonly agreed standard across deposition databases hindered the exploration and study of all the viral sequences collected worldwide in a practical and homogeneous way. During the first months of the pandemic, we developed an automatic procedure to collect, transform, and integrate viral sequences of SARS-CoV-2, MERS, SARS-CoV, Ebola, and Dengue from four major database institutions (NCBI, COG-UK, GISAID, and NMDC). This data pipeline allowed the creation of the data exploration interfaces VirusViz and EpiSurf, as well as ViruSurf, one of the largest databases of integrated viral sequences. Almost two years after the first release of the repository, the original pipeline underwent a thorough refinement process and became more efficient, scalable, and general (currently, it also includes epitopes from the IEDB). Thanks to these improvements, we constantly update and expand our integrated repository, encompassing about 9.1 million SARS-CoV-2 sequences at present (March 2022). This pipeline made it possible to design and develop fundamental resources for any researcher interested in understanding the biological mechanisms behind the viral infection. In addition, it plays a crucial role in many analytic and visualization tools, such as ViruSurf, EpiSurf, VirusViz, and VirusLab.
    Type of Medium: Online Resource
    ISSN: 2673-6284
    URL: Article
    DOI: 10.3390/biotech11010007
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 3056390-2
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  • 4
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    Processing genome-wide association studies within a repository of heterogeneous genomic datasets
    Springer Science and Business Media LLC ; 2023
    In:  BMC Genomic Data Vol. 24, No. 1 ( 2023-03-03)
    Details
    In: BMC Genomic Data, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2023-03-03)
    Abstract: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. Results To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. Conclusions As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
    Type of Medium: Online Resource
    ISSN: 2730-6844
    URL: Article
    DOI: 10.1186/s12863-023-01111-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2041497-3
    detail.hit.zdb_id: 3058779-7
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  • 5
    Online Resource
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    GenoSurf: metadata driven semantic search system for integrated genomic datasets
    Oxford University Press (OUP) ; 2019
    In:  Database Vol. 2019 ( 2019-01-01)
    Details
    In: Database, Oxford University Press (OUP), Vol. 2019 ( 2019-01-01)
    Abstract: Many valuable resources developed by world-wide research institutions and consortia describe genomic datasets that are both open and available for secondary research, but their metadata search interfaces are heterogeneous, not interoperable and sometimes with very limited capabilities. We implemented GenoSurf, a multi-ontology semantic search system providing access to a consolidated collection of metadata attributes found in the most relevant genomic datasets; values of 10 attributes are semantically enriched by making use of the most suited available ontologies. The user of GenoSurf provides as input the search terms, sets the desired level of ontological enrichment and obtains as output the identity of matching data files at the various sources. Search is facilitated by drop-down lists of matching values; aggregate counts describing resulting files are updated in real time while the search terms are progressively added. In addition to the consolidated attributes, users can perform keyword-based searches on the original (raw) metadata, which are also imported; GenoSurf supports the interplay of attribute-based and keyword-based search through well-defined interfaces. Currently, GenoSurf integrates about 40 million metadata of several major valuable data sources, including three providers of clinical and experimental data (TCGA, ENCODE and Roadmap Epigenomics) and two sources of annotation data (GENCODE and RefSeq); it can be used as a standalone resource for targeting the genomic datasets at their original sources (identified with their accession IDs and URLs), or as part of an integrated query answering system for performing complex queries over genomic regions and metadata.
    Type of Medium: Online Resource
    ISSN: 1758-0463
    URL: Article
    DOI: 10.1093/database/baz132
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2496706-3
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  • 6
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    VirusViz: comparative analysis and effective visualization of viral nucleotide and amino acid variants
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. 15 ( 2021-09-07), p. e90-e90
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. 15 ( 2021-09-07), p. e90-e90
    Abstract: Variant visualization plays an important role in supporting the viral evolution analysis, extremely valuable during the COVID-19 pandemic. VirusViz is a web-based application for comparing variants of selected viral populations and their sub-populations; it is primarily focused on SARS-CoV-2 variants, although the tool also supports other viral species (SARS-CoV, MERS-CoV, Dengue, Ebola). As input, VirusViz imports results of queries extracting variants and metadata from the large database ViruSurf, which integrates information about most SARS-CoV-2 sequences publicly deposited worldwide. Moreover, VirusViz accepts sequences of new viral populations as multi-FASTA files plus corresponding metadata in CSV format; a bioinformatic pipeline builds a suitable input for VirusViz by extracting the nucleotide and amino acid variants. Pages of VirusViz provide metadata summarization, variant descriptions, and variant visualization with rich options for zooming, highlighting variants or regions of interest, and switching from nucleotides to amino acids; sequences can be grouped, groups can be comparatively analyzed. For SARS-CoV-2, we manually collect mutations with known or predicted levels of severity/virulence, as indicated in linked research articles; such critical mutations are reported when observed in sequences. The system includes light-weight project management for downloading, resuming, and merging data analysis sessions. VirusViz is freely available at http://gmql.eu/virusviz/.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab478
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 7
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    Online Resource
    The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles
    Springer Science and Business Media LLC ; 2023
    In:  Genome Biology Vol. 24, No. 1 ( 2023-04-18)
    Details
    In: Genome Biology, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2023-04-18)
    Abstract: A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.
    Type of Medium: Online Resource
    ISSN: 1474-760X
    URL: Article
    DOI: 10.1186/s13059-023-02915-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2040529-7
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  • 8
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    The road towards data integration in human genomics: players, steps and interactions
    Oxford University Press (OUP) ; 2021
    In:  Briefings in Bioinformatics Vol. 22, No. 1 ( 2021-01-18), p. 30-44
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 22, No. 1 ( 2021-01-18), p. 30-44
    Abstract: Thousands of new experimental datasets are becoming available every day; in many cases, they are produced within the scope of large cooperative efforts, involving a variety of laboratories spread all over the world, and typically open for public use. Although the potential collective amount of available information is huge, the effective combination of such public sources is hindered by data heterogeneity, as the datasets exhibit a wide variety of notations and formats, concerning both experimental values and metadata. Thus, data integration is becoming a fundamental activity, to be performed prior to data analysis and biological knowledge discovery, consisting of subsequent steps of data extraction, normalization, matching and enrichment; once applied to heterogeneous data sources, it builds multiple perspectives over the genome, leading to the identification of meaningful relationships that could not be perceived by using incompatible data formats. In this paper, we first describe a technological pipeline from data production to data integration; we then propose a taxonomy of genomic data players (based on the distinction between contributors, repository hosts, consortia, integrators and consumers) and apply the taxonomy to describe about 30 important players in genomic data management. We specifically focus on the integrator players and analyse the issues in solving the genomic data integration challenges, as well as evaluate the computational environments that they provide to follow up data integration by means of visualization and analysis tools.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbaa080
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 9
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    Genomic and proteomic data integration for comprehensive biodata search
    EMBnet Stichting ; 2012
    In:  EMBnet.journal Vol. 18, No. B ( 2012-11-09), p. 89-
    Details
    In: EMBnet.journal, EMBnet Stichting, Vol. 18, No. B ( 2012-11-09), p. 89-
    Type of Medium: Online Resource
    ISSN: 2226-6089
    URL: Article
    DOI: 10.14806/ej.18.B.561
    Language: Unknown
    Publisher: EMBnet Stichting
    Publication Date: 2012
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  • 10
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    GeCoAgent: A Conversational Agent for Empowering Genomic Data Extraction and Analysis
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Computing for Healthcare Vol. 3, No. 1 ( 2022-01-31), p. 1-29
    Details
    In: ACM Transactions on Computing for Healthcare, Association for Computing Machinery (ACM), Vol. 3, No. 1 ( 2022-01-31), p. 1-29
    Abstract: With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools. This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.
    Type of Medium: Online Resource
    ISSN: 2691-1957 , 2637-8051
    URL: Article
    DOI: 10.1145/3464383
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 3026919-2
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