Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning

Sargun Nagpal, Ridam Pal, Ashima, Ananya Tyagi, Sadhana Tripathi, Aditya Nagori, Saad Ahmad, Hara Prasad Mishra, Rintu Kutum, View ORCID ProfileTavpritesh Sethi
doi: https://doi.org/10.1101/2021.05.25.445601
Sargun Nagpal
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ridam Pal
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ashima
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ananya Tyagi
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sadhana Tripathi
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Aditya Nagori
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Saad Ahmad
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hara Prasad Mishra
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rintu Kutum
1Indraprastha Institute of Information Technology Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tavpritesh Sethi
1Indraprastha Institute of Information Technology Delhi, India
2All India Institute of Medical Sciences, New Delhi, India
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tavpritesh Sethi
  • For correspondence: tavpriteshsethi@iiitd.ac.in
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

The global efforts to control COVID-19 are threatened by the rapid emergence of novel variants that may display undesirable characteristics such as immune escape or increased pathogenicity. Early prediction of emerging strains could be vital to pandemic preparedness but remains an open challenge. Here, we derive Dimensions of Concern (DoCs) in the latent space of SARS-CoV-2 mutations and demonstrate their potential to provide a lead time for predicting the increase of new cases. We modeled viral DNA sequences as documents with codons treated as words to learn unsupervised word embeddings. We discovered that “blips’’ in latent dimensions of the learned embeddings were associated with mutations. Latent dimensions which harbored blips that consistently preceded and were predictive of new caseloads were analyzed further as Dimensions of Concern, DoCs. The DOCs captured CGG, CTG, AGG, AGT, GAC and, CAC codons associated with major global VoCs L452R, R190S, and D1118H, thus validating our approach biologically. Tracking these DOCs can provide a practical approach to predict country-specific emergence and spread of viral strains for genomic surveillance and is extensible to related challenges such as immune escape, pathogenicity modeling, and antimicrobial resistance.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted June 07, 2021.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning
Sargun Nagpal, Ridam Pal, Ashima, Ananya Tyagi, Sadhana Tripathi, Aditya Nagori, Saad Ahmad, Hara Prasad Mishra, Rintu Kutum, Tavpritesh Sethi
bioRxiv 2021.05.25.445601; doi: https://doi.org/10.1101/2021.05.25.445601
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Genomic Surveillance of COVID-19 Variants with Language Models and Machine Learning
Sargun Nagpal, Ridam Pal, Ashima, Ananya Tyagi, Sadhana Tripathi, Aditya Nagori, Saad Ahmad, Hara Prasad Mishra, Rintu Kutum, Tavpritesh Sethi
bioRxiv 2021.05.25.445601; doi: https://doi.org/10.1101/2021.05.25.445601

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3602)
  • Biochemistry (7567)
  • Bioengineering (5522)
  • Bioinformatics (20782)
  • Biophysics (10325)
  • Cancer Biology (7978)
  • Cell Biology (11635)
  • Clinical Trials (138)
  • Developmental Biology (6602)
  • Ecology (10200)
  • Epidemiology (2065)
  • Evolutionary Biology (13611)
  • Genetics (9539)
  • Genomics (12844)
  • Immunology (7919)
  • Microbiology (19538)
  • Molecular Biology (7657)
  • Neuroscience (42081)
  • Paleontology (308)
  • Pathology (1257)
  • Pharmacology and Toxicology (2201)
  • Physiology (3267)
  • Plant Biology (7038)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5426)
  • Zoology (1116)