VirusPredictor: XGBoost-based software to predict virus-related sequences in human data

Author:

Liu Guangchen123,Chen Xun1ORCID,Luan Yihui2,Li Dawei145ORCID

Affiliation:

1. Department of Microbiology and Molecular Genetics, University of Vermont , Burlington, Vermont 05405, United States

2. School of Mathematics, Shandong University , Jinan, Shandong 250100, China

3. School of Mathematics and Statistics, Ludong University , Yantai, Shandong 264025, China

4. Department of Immunology and Molecular Microbiology, Texas Tech University Health Sciences Center , Lubbock, Texas 79430, United States

5. ICanCME Research Network, Sainte-Justine University Hospital Research Center , Montreal, Quebec H3T 1C5, Canada

Abstract

Abstract Motivation Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data. Results We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150–350 (Illumina short reads), 850–950 (Sanger sequencing data), and 2000–5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150–350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients’ unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions. Availability and implementation www.dllab.org/software/VirusPredictor.html.

Funder

Start-up Funds

National Institutes of Health

National Institute of Allergy and Infectious Diseases

Solve ME/CFS Initiative Ramsay Research Grant Program, Open Medicine Foundation

Department of Defense Lung Cancer Research Program

Publisher

Oxford University Press (OUP)

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