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Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes

Overview of attention for article published in BMC Microbiology, January 2017
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Title
Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes
Published in
BMC Microbiology, January 2017
DOI 10.1186/s12866-016-0920-3
Pubmed ID
Authors

Louis J. Taylor, Klaus Strebel

Abstract

Gene knockouts are a common tool used to study gene function in various organisms. However, designing gene knockouts is complicated in viruses, which frequently contain sequences that code for multiple overlapping genes. Designing mutants that can be traced by the creation of new or elimination of existing restriction sites further compounds the difficulty in experimental design of knockouts of overlapping genes. While software is available to rapidly identify restriction sites in a given nucleotide sequence, no existing software addresses experimental design of mutations involving multiple overlapping amino acid sequences in generating gene knockouts. Pyviko performed well on a test set of over 240,000 gene pairs collected from viral genomes deposited in the National Center for Biotechnology Information Nucleotide database, identifying a point mutation which added a premature stop codon within the first 20 codons of the target gene in 93.2% of all tested gene-overprinted gene pairs. This shows that Pyviko can be used successfully in a wide variety of contexts to facilitate the molecular cloning and study of viral overprinted genes. Pyviko is an extensible and intuitive Python tool for designing knockouts of overlapping genes. Freely available as both a Python package and a web-based interface ( http://louiejtaylor.github.io/pyViKO/ ), Pyviko simplifies the experimental design of gene knockouts in complex viruses with overlapping genes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 25%
Student > Ph. D. Student 2 17%
Student > Doctoral Student 1 8%
Professor 1 8%
Student > Master 1 8%
Other 1 8%
Unknown 3 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 33%
Agricultural and Biological Sciences 2 17%
Computer Science 1 8%
Immunology and Microbiology 1 8%
Medicine and Dentistry 1 8%
Other 0 0%
Unknown 3 25%