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      VIRULIGN: fast codon-correct alignment and annotation of viral genomes

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          Abstract

          Summary

          Virus sequence data are an essential resource for reconstructing spatiotemporal dynamics of viral spread as well as to inform treatment and prevention strategies. However, the potential benefit of these applications critically depends on accurate and correctly annotated alignments of genetically heterogeneous data. VIRULIGN was built for fast codon-correct alignments of large datasets, with standardized and formalized genome annotation and various alignment export formats.

          Availability and implementation

          VIRULIGN is freely available at https://github.com/rega-cev/virulign as an open source software project.

          Supplementary information

          Supplementary data is available at Bioinformatics online.

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          Most cited references13

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          TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations

          Federico Abascal, Rafael Zardoya, Maximilian J Telford (2010)
          We present TranslatorX, a web server designed to align protein-coding nucleotide sequences based on their corresponding amino acid translations. Many comparisons between biological sequences (nucleic acids and proteins) involve the construction of multiple alignments. Alignments represent a statement regarding the homology between individual nucleotides or amino acids within homologous genes. As protein-coding DNA sequences evolve as triplets of nucleotides (codons) and it is known that sequence similarity degrades more rapidly at the DNA than at the amino acid level, alignments are generally more accurate when based on amino acids than on their corresponding nucleotides. TranslatorX novelties include: (i) use of all documented genetic codes and the possibility of assigning different genetic codes for each sequence; (ii) a battery of different multiple alignment programs; (iii) translation of ambiguous codons when possible; (iv) an innovative criterion to clean nucleotide alignments with GBlocks based on protein information; and (v) a rich output, including Jalview-powered graphical visualization of the alignments, codon-based alignments coloured according to the corresponding amino acids, measures of compositional bias and first, second and third codon position specific alignments. The TranslatorX server is freely available at http://translatorx.co.uk.
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            Reproducible research in computational science.

            Roger Peng (2011)
            Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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              Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

              Beth Shapiro, Andrew Rambaut, Alexei J. Drummond (2006)
              Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.
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                Author and article information

                Contributors
                John Hancock: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 May 2019
                Publication date (Electronic): 08 October 2018
                Publication date PMC-release: 08 October 2018
                Volume: 35
                Issue: 10
                Pages: 1763-1765
                Affiliations
                [1 ]KU Leuven, Rega Institute for Medical, Laboratorium of Clinical and Evolutionary Virology, Leuven, Belgium
                [2 ]Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
                [3 ]Emweb, Herent, Belgium
                [4 ]Center for Global Health and Tropical Medicine, Institute for Hygiene and Tropical Medicine, Lisboa, Portugal
                Author notes
                To whom correspondence should be addressed. Email: pieter.libin@ 123456gmail.com
                Author information
                http://orcid.org/0000-0003-3906-758X
                http://orcid.org/0000-0003-3898-9494
                Article
                Publisher ID: bty851
                DOI: 10.1093/bioinformatics/bty851
                PMC ID: 6513156
                PubMed ID: 30295730
                SO-VID: 133756c9-932b-48d3-a177-ea1d07534d3b
                Copyright © © The Author(s) 2018. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Date received : 03 May 2018
                Date revision received : 24 September 2018
                Date accepted : 05 October 2018
                Page count
                Pages: 3
                Funding
                Funded by: Research Foundation - Flanders
                Funded by: FWO 10.13039/501100003130
                Funded by: Fundação para a Ciência e Tecnologia
                Funded by: FCT 10.13039/100006129
                Award ID: GHTM-UID/Multi/04413/2013
                Funded by: MigrantHIV project
                Award ID: PTDC/DTP-EPI/7066/2014
                Categories
                Subject: Applications Notes
                Subject: Genome Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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