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Viral quasispecies reconstruction via tensor factorization with successive read removal.

ABSTRACT: Motivation:As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains--a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small. Results:This paper presents TenSQR, an algorithm that utilizes tensor factorization framework to analyze HTS data and reconstruct viral quasispecies characterized by highly uneven frequencies of its components. Fundamentally, TenSQR performs clustering with successive data removal to infer strains in a quasispecies in order from the most to the least abundant one; every time a strain is inferred, sequencing reads generated from that strain are removed from the dataset. The proposed successive strain reconstruction and data removal enables discovery of rare strains in a population and facilitates detection of deletions in such strains. Results on simulated datasets demonstrate that TenSQR can reconstruct full-length strains having widely different abundances, generally outperforming state-of-the-art methods at diversities 1-10% and detecting long deletions even in rare strains. A study on a real HIV-1 dataset demonstrates that TenSQR outperforms competing methods in experimental settings as well. Finally, we apply TenSQR to analyze a Zika virus sample and reconstruct the full-length strains it contains. Availability and implementation:TenSQR is available at https://github.com/SoYeonA/TenSQR. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Ahn S

PROVIDER: S-EPMC6022648 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Viral quasispecies reconstruction via tensor factorization with successive read removal.

Ahn Soyeon S Ke Ziqi Z Vikalo Haris H

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains--a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform ...[more]

PMID: 29949976

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Project description:The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to medical concepts that clinical researchers need or use. Some recent studies have focused on EHR-derived phenotyping, which aims at mapping the EHR data to specific medical concepts; however, most of these approaches require labor intensive supervision from experienced clinical professionals. Furthermore, existing approaches are often disease-centric and specialized to the idiosyncrasies of the information technology and/or business practices of a single healthcare organization. In this paper, we propose Limestone, a nonnegative tensor factorization method to derive phenotype candidates with virtually no human supervision. Limestone represents the data source interactions naturally using tensors (a generalization of matrices). In particular, we investigate the interaction of diagnoses and medications among patients. The resulting tensor factors are reported as phenotype candidates that automatically reveal patient clusters on specific diagnoses and medications. Using the proposed method, multiple phenotypes can be identified simultaneously from data. We demonstrate the capability of Limestone on a cohort of 31,815 patient records from the Geisinger Health System. The dataset spans 7years of longitudinal patient records and was initially constructed for a heart failure onset prediction study. Our experiments demonstrate the robustness, stability, and the conciseness of Limestone-derived phenotypes. Our results show that using only 40 phenotypes, we can outperform the original 640 features (169 diagnosis categories and 471 medication types) to achieve an area under the receiver operator characteristic curve (AUC) of 0.720 (95% CI 0.715 to 0.725). Moreover, in consultation with a medical expert, we confirmed 82% of the top 50 candidates automatically extracted by Limestone are clinically meaningful.

Dataset Information

Viral quasispecies reconstruction via tensor factorization with successive read removal.

Publications

Viral quasispecies reconstruction via tensor factorization with successive read removal.

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