ViCTree: an automated framework for taxonomic classification from protein sequences

Bioinformatics of virus taxonomy: foundations and tools for developing sequence-based hierarchical classification

The genome sequence is the only characteristic readily obtainable for all known viruses, underlying the growing role of comparative genomics in organizing knowledge about viruses in a systematic evolution-aware way, known as virus taxonomy. Overseen by the International Committee on Taxonomy of Viruses (ICTV), development of virus taxonomy involves taxa demarcation at 15 ranks of a hierarchical classification, often in host-specific manner. Outside the ICTV remit, researchers assess fitting numerous unclassified viruses into the established taxa. They employ different metrics of virus clustering, basing on conserved domain(s), separation of viruses in rooted phylogenetic trees and pair-wise distance space. Computational approaches differ further in respect to methodology, number of ranks considered, sensitivity to uneven virus sampling, and visualization of results. Advancing and using computational tools will be critical for improving taxa demarcation across the virosphere and resolving rank origins in research that may also inform experimental virology.

Detecting and correcting misclassified sequences in the large-scale public databases

Motivation

As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity.

Results

We found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases.

Availability and implementation

Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Automated, phylogeny-based genotype delimitation of the Hepatitis Viruses HBV and HCV

Background

The classification of hepatitis viruses still predominantly relies on ad hoc criteria, i.e., phenotypic traits and arbitrary genetic distance thresholds. Given the subjectivity of such practices coupled with the constant sequencing of samples and discovery of new strains, this manual approach to virus classification becomes cumbersome and impossible to generalize.

Methods

Using two well-studied hepatitis virus datasets, HBV and HCV, we assess if computational methods for molecular species delimitation that are typically applied to barcoding biodiversity studies can also be successfully deployed for hepatitis virus classification. For comparison, we also used ABGD, a tool that in contrast to other distance methods attempts to automatically identify the barcoding gap using pairwise genetic distances for a set of aligned input sequences.

Results—Discussion

We found that the mPTP species delimitation tool identified even without adapting its default parameters taxonomic clusters that either correspond to the currently acknowledged genotypes or to known subdivision of genotypes (subtypes or subgenotypes). In the cases where the delimited cluster corresponded to subtype or subgenotype, there were previous concerns that their status may be underestimated. The clusters obtained from the ABGD analysis differed depending on the parameters used. However, under certain values the results were very similar to the taxonomy and mPTP which indicates the usefulness of distance based methods in virus taxonomy under appropriate parameter settings. The overlap of predicted clusters with taxonomically acknowledged genotypes implies that virus classification can be successfully automated.

Metaviromics Reveals Unknown Viral Diversity in the Biting Midge Culicoides impunctatus

Biting midges (Culicoides species) are vectors of arboviruses and were responsible for the emergence and spread of Schmallenberg virus (SBV) in Europe in 2011 and are likely to be involved in the emergence of other arboviruses in Europe. Improved surveillance and better understanding of risks require a better understanding of the circulating viral diversity in these biting insects. In this study, we expand the sequence space of RNA viruses by identifying a number of novel RNA viruses from Culicoides impunctatus (biting midge) using a meta-transcriptomic approach. A novel metaviromic pipeline called MetaViC was developed specifically to identify novel virus sequence signatures from high throughput sequencing (HTS) datasets in the absence of a known host genome. MetaViC is a protein centric pipeline that looks for specific protein signatures in the reads and contigs generated as part of the pipeline. Several novel viruses, including an alphanodavirus with both segments, a novel relative of the Hubei sobemo-like virus 49, two rhabdo-like viruses and a chuvirus, were identified in the Scottish midge samples. The newly identified viruses were found to be phylogenetically distinct to those previous known. These findings expand our current knowledge of viral diversity in arthropods and especially in these understudied disease vectors.