About: The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences

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About: The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences
Creator	Cotten, Matthew Constantinides, Bede Kouchaki, Samaneh Tapinos, Avraam Phan, My »more»
source	Medline; PMC
abstract	Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data.
has issue date	2019-04-26(xsd:dateTime)
bibo:doi	10.3390/v11050394
bibo:pmid	31035503
has license	cc-by
sha1sum (hex)	fe8673fd11cb2de090872b54b123acd6610ebfd1
schema:url	https://doi.org/10.3390/v11050394
resource representing a document's title	The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences
has PubMed Central identifier	PMC6563281
has PubMed identifier	31035503
schema:publication	Viruses
resource representing a document's body	covid:fe8673fd11cb2de090872b54b123acd6610ebfd1#body_text
is schema:about of	named entity 'populations' named entity 'virus' covid:arg/fe8673fd11cb2de090872b54b123acd6610ebfd1 named entity 'Our' named entity 'accurate' »more»

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