About: CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning
Creator	Shang, Jiayu Sun, Yanni
source	BioRxiv
abstract	The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub: https://github.com/KennthShang/CHEER.
has issue date	2020-03-27(xsd:dateTime)
bibo:doi	10.1101/2020.03.26.009001
has license	biorxiv
sha1sum (hex)	be9e8487ede5ab25339c49d8c67cd2442e122447
schema:url	https://doi.org/10.1101/2020.03.26.009001
resource representing a document's title	CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning
schema:publication	bioRxiv
resource representing a document's body	covid:be9e8487ede5ab25339c49d8c67cd2442e122447#body_text
is schema:about of	named entity 'CNNs' named entity 'species' named entity 'viral' named entity 'ACCUMULATION' named entity 'simulated' »more»

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