1,721,230 research outputs found
Data quality-aware genomic data integration
Full text linkGenomic data are growing at unprecedented pace, along with new protocols, update polices, formats and guidelines, terminologies and ontologies, which are made available every day by data providers. In this continuously evolving universe, enforcing quality on data and metadata is increasingly critical. While many aspects of data quality are addressed at each individual source, we focus on the need for a systematic approach when data from several sources are integrated, as such integration is an essential aspect for modern genomic data analysis. Data quality must be assessed from many perspectives, including accessibility, currency, representational consistency, specificity, and reliability.
In this article we review relevant literature and, based on the analysis of many datasets and platforms, we report on methods used for guaranteeing data quality while integrating heterogeneous data sources. We explore several real-world cases that are exemplary of more general underlying data quality problems and we illustrate how they can be resolved with a structured method, sensibly applicable also to other biomedical domains. The overviewed methods are implemented in a large framework for the integration of processed genomic data, which is made available to the research community for supporting tertiary data analysis over Next Generation Sequencing datasets, continuously loaded from many open data sources, bringing considerable added value to biological knowledge discovery
The Opportunity of Data-Driven Services for Viral Genomic Surveillance
Full text linkThe recent COVID-19 pandemic has posed novel challenges to the big data and knowledge management community. The unprecedented availability of viral genomes on public databases has made possible the data-driven exploration of viruses' evolution (especially of SARS-CoV-2, the virus responsible for the disease). Properties of data and knowledge in the genomic and virological domain may fuel data science methods for the identification and possible prediction of critical phenomena, such as the emergence of variants with improved transmissibility/virulence and recombined strains.
A number of tools have been produced to explore the variants' trends or suggest hypotheses on the evolutionary mechanisms of the virus. In this perspective, we elaborate on plausible directions of this field of research, which are still applicable to the SARS-CoV-2 virus but may become even more relevant in the context of new outbreaks (e.g., monkeypox, malaria, diphtheria). Expressly, we point to 1) data-driven identification of mutations or variants with potential impact; 2) data-driven identification of recombination events - creating opportunities to overcome selective pressure and adapt to new environments and hosts (e.g., livestock or humans). These directions can be framed within genomic surveillance measures, characterized by the possibility of tracking viruses by using their genome, which is collected, sequenced, and submitted to public databases by laboratories around the world.
If successful, genomic surveillance substantially supports the understanding of novel viral pathogens and of their dangerousness in terms of prevalence, infectivity, and transmissibility; the implemented services can be of great utility to decision-makers in healthcare. Here, we draw current trends, challenges, and future directions of data-driven services for genomic surveillance
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