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Nicholas J Dimonaco
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University

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Preprint content
Published: 23 May 2021
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Motivation The biases in Open Reading Frame (ORF) prediction tools, which have been based on historic genomic annotations from model organisms, impact our understanding of novel genomes and metagenomes. This hinders the discovery of new genomic information as it results in predictions being biased towards existing knowledge. To date users have lacked a systematic and replicable approach to identify the strengths and weaknesses of any ORF prediction tool and allow them to choose the right tool for their analysis. Results We present an evaluation framework (ORForise) based on a comprehensive set of 12 primary and 60 secondary metrics that facilitate the assessment of the performance of ORF prediction tools. This makes it possible to identify which performs better for specific use-cases. We use this to assess 15 ab initio and model-based tools representing those most widely used (historically and currently) to generate the knowledge in genomic databases. We find that the performance of any tool is dependent on the genome being analysed, and no individual tool ranked as the most accurate across all genomes or metrics analysed. Even the top-ranked tools produced conflicting gene collections which could not be resolved by aggregation. The ORForise evaluation framework provides users with a replicable, data-led approach to make informed tool choices for novel genome annotations and for refining historical annotations. Availability https://github.com/NickJD/ORForise Contact [email protected] Supplementary information Supplementary data are available at bioRxiv online.

ACS Style

Nicholas J. Dimonaco; Wayne Aubrey; Kim Kenobi; Amanda Clare; Christopher J. Creevey. No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study. 2021, 1 .

AMA Style

Nicholas J. Dimonaco, Wayne Aubrey, Kim Kenobi, Amanda Clare, Christopher J. Creevey. No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study. . 2021; ():1.

Chicago/Turabian Style

Nicholas J. Dimonaco; Wayne Aubrey; Kim Kenobi; Amanda Clare; Christopher J. Creevey. 2021. "No one tool to rule them all: Prokaryotic gene prediction tool performance is highly dependent on the organism of study." , no. : 1.

Journal article
Published: 30 December 2020 in Viruses
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In 2019, a novel coronavirus, SARS-CoV-2/nCoV-19, emerged in Wuhan, China, and has been responsible for the current COVID-19 pandemic. The evolutionary origins of the virus remain elusive and understanding its complex mutational signatures could guide vaccine design and development. As part of the international “CoronaHack” in April 2020, we employed a collection of contemporary methodologies to compare the genomic sequences of coronaviruses isolated from human (SARS-CoV-2; n = 163), bat (bat-CoV; n = 215) and pangolin (pangolin-CoV; n = 7) available in public repositories. We have also noted the pangolin-CoV isolate MP789 to bare stronger resemblance to SARS-CoV-2 than other pangolin-CoV. Following de novo gene annotation prediction, analyses of gene–gene similarity network, codon usage bias and variant discovery were undertaken. Strong host-associated divergences were noted in ORF3a, ORF6, ORF7a, ORF8 and S, and in codon usage bias profiles. Last, we have characterised several high impact variants (in-frame insertion/deletion or stop gain) in bat-CoV and pangolin-CoV populations, some of which are found in the same amino acid position and may be highlighting loci of potential functional relevance.

ACS Style

Nicholas J. Dimonaco; Mazdak Salavati; Barbara B. Shih. Computational Analysis of SARS-CoV-2 and SARS-Like Coronavirus Diversity in Human, Bat and Pangolin Populations. Viruses 2020, 13, 49 .

AMA Style

Nicholas J. Dimonaco, Mazdak Salavati, Barbara B. Shih. Computational Analysis of SARS-CoV-2 and SARS-Like Coronavirus Diversity in Human, Bat and Pangolin Populations. Viruses. 2020; 13 (1):49.

Chicago/Turabian Style

Nicholas J. Dimonaco; Mazdak Salavati; Barbara B. Shih. 2020. "Computational Analysis of SARS-CoV-2 and SARS-Like Coronavirus Diversity in Human, Bat and Pangolin Populations." Viruses 13, no. 1: 49.

Preprint content
Published: 24 November 2020
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In 2019, a novel coronavirus, SARS-CoV-2/nCoV-19, emerged in Wuhan, China, and has been responsible for the current COVID-19 pandemic. The evolutionary origins of the virus remain elusive and understanding its complex mutational signatures could guide vaccine design and development. As part of the international “CoronaHack” in April 2020 (https://www.coronahack.co.uk/), we employed a collection of contemporary methodologies to compare the genomic sequences of coronaviruses isolated from human (SARS-CoV-2;n=163), bat (bat-CoV;n=215) and pangolin (pangolin-CoV;n=7) available in public repositories. Following de novo gene annotation prediction, analysis on gene-gene similarity network, codon usage bias and variant discovery were carried out. Strong host-associated divergences were noted in ORF3a, ORF6, ORF7a, ORF8 and S, and in codon usage bias profiles. Lastly, we have characterised several high impact variants (inframe insertion/deletion or stop gain) in bat-CoV and pangolin-CoV populations, some of which are found in the same amino acid position and may be highlighting loci of potential functional relevance.

ACS Style

Nicholas J. Dimonaco; Mazdak Salavati; Barbara Shih. Hacking the diversity of SARS-CoV-2 and SARS-like coronaviruses in human, bat and pangolin populations. 2020, 1 .

AMA Style

Nicholas J. Dimonaco, Mazdak Salavati, Barbara Shih. Hacking the diversity of SARS-CoV-2 and SARS-like coronaviruses in human, bat and pangolin populations. . 2020; ():1.

Chicago/Turabian Style

Nicholas J. Dimonaco; Mazdak Salavati; Barbara Shih. 2020. "Hacking the diversity of SARS-CoV-2 and SARS-like coronaviruses in human, bat and pangolin populations." , no. : 1.

Preprint content
Published: 25 April 2020 in bioRxiv
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MotivationInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts.ResultsWe developed DeepViral, a deep learning based method that predicts protein–protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.Availabilityhttps://github.com/bio-ontology-research-group/[email protected]

ACS Style

Wang Liu-Wei; Şenay Kafkas; Jun Chen; Nicholas Dimonaco; Jesper Tegnér; Robert Hoehndorf. DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions. bioRxiv 2020, 1 .

AMA Style

Wang Liu-Wei, Şenay Kafkas, Jun Chen, Nicholas Dimonaco, Jesper Tegnér, Robert Hoehndorf. DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions. bioRxiv. 2020; ():1.

Chicago/Turabian Style

Wang Liu-Wei; Şenay Kafkas; Jun Chen; Nicholas Dimonaco; Jesper Tegnér; Robert Hoehndorf. 2020. "DeepViral: infectious disease phenotypes improve prediction of novel virus–host interactions." bioRxiv , no. : 1.