About: What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes

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About: What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes
Creator	Mande, Sharmila Nagpal, Sunil Srivastava, Divyanshu
source	BioRxiv
abstract	Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a ‘Bag of Words’ to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported recurrent mutations and phylogenetic clades for genomes. Additionally, we report the geographical distribution of the identified mutation signatures in SARS-CoV-2 genomes on the global map. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).
has issue date	2020-08-20(xsd:dateTime)
bibo:doi	10.1101/2020.08.20.258772
has license	biorxiv
sha1sum (hex)	24c534c1f480d949f6ceeab0a60c1d7958ee34af
schema:url	https://doi.org/10.1101/2020.08.20.258772
resource representing a document's title	What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes
schema:publication	bioRxiv
resource representing a document's body	covid:24c534c1f480d949f6ceeab0a60c1d7958ee34af#body_text
is schema:about of	named entity 'conventional' named entity 'genomes' named entity 'utility' named entity 'SARS-CoV-2' named entity 'classify' »more»

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