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

View ORCID ProfileSargun Nagpal, Ridam Pal, Ashima, Ananya Tyagi, Sadhana Tripathi, View ORCID ProfileAditya Nagori, Saad Ahmad, View ORCID ProfileHara Prasad Mishra, View ORCID ProfileRintu 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
  • ORCID record for Sargun Nagpal
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
  • ORCID record for Aditya Nagori
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
  • ORCID record for Hara Prasad Mishra
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
  • ORCID record for Rintu Kutum
  • For correspondence: tavpriteshsethi@iiitd.ac.in rintuk@iiitd.ac.in
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 rintuk@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 SARS-CoV-2 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 developed Strainflow, to learn the latent dimensions of 0.9 million high-quality SARS-CoV-2 genome sequences, and used machine learning algorithms to predict upcoming caseloads of SARS-CoV-2. In our Strainflow model, SARS-CoV-2 genome sequences were treated as documents, and codons as words to learn unsupervised codon embeddings (latent dimensions). We discovered that codon-level changes lead to a change in the entropy of the latent dimensions. We used a machine learning algorithm to find the most relevant latent dimensions called Dimensions of Concern (DoCs) of SARS-CoV-2 spike genes, and demonstrate their potential to provide a lead time for predicting new caseloads in several countries. The DoCs capture codons associated with global Variants of Concern (VOCs) and Variants of Interest (VOIs), and may be surveilled to predict country-specific emergence and spread of SARS-CoV-2 variants.

Highlights

  • We developed a genomic surveillance model for SARS-CoV-2 genome sequences, Strainflow, where sequences were treated as documents with words (codons) to learn the codon context of 0.9 million spike genes using the skip-gram algorithm.

  • Time series analysis of the information content (Entropy) of the latent dimensions learned by Strainflow shows a leading relationship with the monthly COVID-19 cases for seven countries (e.g., USA, Japan, India, and others).

  • Machine Learning modeling of the entropy of the latent dimensions helped us to develop an epidemiological early warning system for the COVID-19 caseloads.

  • The top codons associated with the most relevant latent dimensions (DoCs) were linked to SARS-CoV-2 variants, and these DoCs may be used as a surrogate to track the country-specific spread of the variants.

Figure
  • Download figure
  • Open in new tab

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • In this revised version, we have implemented fast tSNE for qualitative inspection of the latent dimensions (LD) of 0.9 million SARS-CoV-2 spike genes. Quantitative analyses of the LDs were performed using the fast sample entropy method. Also, we have used fast sample entropy instead of 'blips' to model COVID-19 caseloads.

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 August 26, 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
Reddit logo Twitter logo Facebook 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 (4683)
  • Biochemistry (10361)
  • Bioengineering (7675)
  • Bioinformatics (26337)
  • Biophysics (13528)
  • Cancer Biology (10686)
  • Cell Biology (15440)
  • Clinical Trials (138)
  • Developmental Biology (8497)
  • Ecology (12821)
  • Epidemiology (2067)
  • Evolutionary Biology (16860)
  • Genetics (11399)
  • Genomics (15478)
  • Immunology (10617)
  • Microbiology (25218)
  • Molecular Biology (10223)
  • Neuroscience (54472)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4342)
  • Plant Biology (9247)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2558)
  • Systems Biology (6781)
  • Zoology (1466)