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Journal article
Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences
mSystems, v 7(2), pp e0003522-e0003522
26 Apr 2022
PMID: 35311562
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Next-generation sequencing has been essential to the global response to the COVID-19 pandemic. As of January 2022, nearly 7 million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences are available to researchers in public databases. Sequence databases are an abundant resource from which to extract biologically relevant and clinically actionable information. As the pandemic has gone on, SARS-CoV-2 has rapidly evolved, involving complex genomic changes that challenge current approaches to classifying SARS-CoV-2 variants. Deep sequence learning could be a potentially powerful way to build complex sequence-to-phenotype models. Unfortunately, while they can be predictive, deep learning typically produces "black box" models that cannot directly provide biological and clinical insight. Researchers should therefore consider implementing emerging methods for visualizing and interpreting deep sequence models. Finally, researchers should address important data limitations, including (i) global sequencing disparities, (ii) insufficient sequence metadata, and (iii) screening artifacts due to poor sequence quality control.
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Details
- Title
- Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences
- Creators
- Bahrad A. Sokhansanj - Drexel UniversityGail L. Rosen - Drexel University
- Publication Details
- mSystems, v 7(2), pp e0003522-e0003522
- Publisher
- Amer Soc Microbiology
- Number of pages
- 8
- Grant note
- 1919691; 2107108 / National Science Foundation (NSF); National Research Foundation of Korea
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000788061300028
- Scopus ID
- 2-s2.0-85129079484
- Other Identifier
- 991019169629504721
UN Sustainable Development Goals (SDGs)
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Microbiology