Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses

Águas, Ricardo; Ferguson, Neil M.

doi:10.1371/journal.pcbi.1003254

Cited by 22 publications

(12 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 8 used relative dinucleotide frequencies and discriminant analysis to infer the hosts of novel picorna-like viruses. Aguas and Ferguson 9 developed a feature selection method and used random forests (RF) based on the diverged nucleotide or amino acid bases among a set of aligned molecular sequences to predict the host species of pathogens. Tang et al .…”

Section: Introductionmentioning

confidence: 99%

Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences

Sun

2018

Sci Rep

View full text Add to dashboard Cite

Predicting the hosts of newly discovered viruses is important for pandemic surveillance of infectious diseases. We investigated the use of alignment-based and alignment-free methods and support vector machine using mononucleotide frequency and dinucleotide bias to predict the hosts of viruses, and applied these approaches to three datasets: rabies virus, coronavirus, and influenza A virus. For coronavirus, we used the spike gene sequences, while for rabies and influenza A viruses, we used the more conserved nucleoprotein gene sequences. We compared the three methods under different scenarios and showed that their performances are highly correlated with the variability of sequences and sample size. For conserved genes like the nucleoprotein gene, longer k-mers than mono- and dinucleotides are needed to better distinguish the sequences. We also showed that both alignment-based and alignment-free methods can accurately predict the hosts of viruses. When alignment is difficult to achieve or highly time-consuming, alignment-free methods can be a promising substitute to predict the hosts of new viruses.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences

Sun

2018

Sci Rep

View full text Add to dashboard Cite

show abstract

“…These exaptive host range mutations must exist in the viral population prior to contact with the novel host, as part of the virus' standing genetic diversity (5,6). The exact mutations and mechanisms of host shifting are intensively studied in emerging zoonotic viruses such as influenza, SARS-CoV, and Ebola virus (7,8).…”

Section: Introductionmentioning

confidence: 99%

Existing host range mutations constrain further emergence of RNA viruses

Zhao

Seth-Pasricha

Stemate

et al. 2018

Preprint

View full text Add to dashboard Cite

RNA viruses are capable of rapid host shifting, typically due to a point mutation that confers expanded host range. As additional point mutations are necessary for further expansions, epistasis among host range mutations can potentially affect the mutational neighborhood and frequency of niche expansion. We mapped the mutational neighborhood of host range expansion using three genotypes of the dsRNA bacteriophage phi6 (wildtype and two isogenic host range mutants) on the novel host Pseudomonas syringae pv. atrofaciens (PA). Sanger sequencing of fifty PA mutant clones for each genotype and population Illumina sequencing both revealed the same high frequency mutations allowing infection of PA. Wildtype phi6 had at least nine different ways of mutating to enter the novel host, eight of which are in p3 (host attachment protein gene), and 13/50 clones had unchanged p3 genes. However, the two isogenic mutants had dramatically restricted neighborhoods: only one or two mutations, all in p3. Deep sequencing revealed that wildtype clones without mutations in p3 likely had changes in p12 (morphogenic protein), a region that was not polymorphic for the two isogenic host range mutants. Sanger sequencing confirmed that 10/13 of the wildtype phi6 clones had nonsynonymous mutations in p12 and two others had point mutations in p9 and p5 -none of these genes had previously been associated with host range expansion in phi6. We demonstrate, for the first time, epistatic constraint in an RNA virus due to host range mutations themselves, which has implications for models of serial host range expansion. ImportanceRNA viruses mutate rapidly and frequently expand their host ranges to infect novel hosts, leading to serial host shifts. Using an RNA bacteriophage model system (Pseudomonas phage phi6), we studied the impact of pre-existing host range mutations on another host range expansion. Results from both clonal Sanger and Illumina sequencing show extant host range mutations dramatically narrow the neighborhood of potential host range mutations compared to wildtype phi6. This research suggests that serial host shifting viruses may follow a small number of molecular paths to enter additional novel hosts. We also identified . CC-BY-NC 4.0

show abstract

“…Previous studies ( Aguas & Ferguson, 2013 ; Meroz et al , 2011 ) have partially solved the above-discussed problem by supervised machine learning techniques. Within this related setting, genetic determinants are identified conditional on a known classification of the sequences.…”

Section: Introductionmentioning

confidence: 99%

K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

et al. 2015

View full text Add to dashboard Cite

The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.

show abstract

Feature Selection Methods for Identifying Genetic Determinants of Host Species in RNA Viruses

Cited by 22 publications

References 58 publications

Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences

Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences

Existing host range mutations constrain further emergence of RNA viruses

K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

Contact Info

Product

Resources

About