ENVirT: inference of ecological characteristics of viruses from metagenomic data

Duleepa Jayasundara; Damayanthi Herath; Damith Senanayake; Isaam Saeed; Cheng-Yu Yang; Yuan Sun; Bill C Chang; Sen-Lin Tang; Saman K Halgamuge

Journal article

ENVirT: inference of ecological characteristics of viruses from metagenomic data

Duleepa Jayasundara, Damayanthi Herath, Damith Senanayake, Isaam Saeed, Cheng-Yu Yang, Yuan Sun, Bill C Chang, Sen-Lin Tang, Saman K Halgamuge

BMC Bioinformatics | BioMed Central | Published : 2019

DOI: 10.1186/s12859-018-2398-5

Download PDF

Abstract

Background: Estimating the parameters that describe the ecology of viruses,particularly those that are novel, can be made possible using metagenomic approaches. However, the best-performing existing methods require databases to first estimate an average genome length of a viral community before being able to estimate other parameters, such as viral richness. Although this approach has been widely used, it can adversely skew results since the majority of viruses are yet to be catalogued in databases. Results: In this paper, we present ENVirT, a method for estimating the richness of novel viral mixtures, and for the first time we also show that it is possible to simultaneously estimate the av..

View full abstract

University of Melbourne Researchers

Bill Chang Author

Saman Halgamuge Author

Related Projects (3)

Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses

Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats t..

Near-unsupervised computational methods for exploring 'omic' data

The project aims to investigate the recently proposed promising machine learning paradigm "Near Unsupervised Learning" by critically analysi..

Discovering Patterns using Near Unsupervised Leaning to Support the Quick Detection of New Animal Disease Outbreaks Caused by Viruses

Without the capability to identify and study the vast majority of extant viruses using traditional laboratory techniques, emerging threats t..

Grants

Awarded by Australia Research Council

Funding Acknowledgements

This work was supported partially by Australia Research Council [grant numbers LP140100670 and DP150103512] and the Biodiversity Research Center, Academia Sinica, Taiwan. DJ, DH, DS and YS were funded by the MIFRS and MIRS scholarships of The University of Melbourne. Publication costs were funded by The Australian National University.