Abstract
During the past decade, genomics has been drawing more and more attention, thanks to the introduction of fast and accurate sequencing strategies. Accumulation of data is fast and the amount of information to be managed and integrated is snowballing. While new variants are discovered every day, we still do not know enough about the human genome to have a final understanding of all the implications that they could have from a clinical point of view. When inherited diseases are considered, variants clinical classification may change over time, in relation to new discoveries. In this scenario, software solutions that help operators in the analysis and maintenance of constantly changing genomic data are relevant in the field of modern molecular medicine. In this paper we present GLIMS (short for Genomics Laboratory Information Management System), an open-source laboratory information management system for genomic data that allows to deal with time-evolving variant annotations. This solution answers to the need of genomic laboratories to keep up with their knowledge about variants and annotations, so as to provide patients with up-to-date reports. We illustrate the architecture of GLIMS modules that are in charge of keeping the database of variants updated and reclassifying patients’ variants. Then, we demonstrate (via the use of GLIMS) that variant clinical classifications are changing rapidly even in ClinVar, one of the most known and cited genomic databases, thus underlining the need for a tool that tracks changes over time.
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Notes
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As of this writing, we are perfecting an imminent release on a publicly-available repository. In the meantime, please reach the corresponding author for the source code.
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It is important to note that VCF accepted events are always fired asynchronously, as the process of importing even small-sized VCF files has to be considered a long-running operation.
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Acknowledgments
This work is partially funded by the EU H2020 Framework Programme under project WITDOM (project no. 644371).
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Catallo, I. et al. (2019). An Open-Source Tool for Managing Time-Evolving Variant Annotation. In: Bartoletti, M., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham. https://doi.org/10.1007/978-3-030-14160-8_1
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DOI: https://doi.org/10.1007/978-3-030-14160-8_1
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