Ji Zhang1, Yanwen Xiong2, Lynn Rogers1, Glen P Carter3, Nigel French1. 1. mEpiLab, New Zealand Food Safety Science & Research Centre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand. 2. State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, China. 3. Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne, Melbourne, Australia.
Abstract
Motivation: Large-scale whole-genome sequencing dataset-based studies are becoming increasingly common in pathogen surveillance and outbreak investigations. A highly discriminative and time-efficient bioinformatics tool is needed to transform large amounts of sequencing data into usable biological information. To replace the intuitive, yet inefficient, way of gene-by-gene allele calling algorithm, a new algorithm using genome-by-genome approach was developed. Results: Tests showed that the program equipped with the new algorithm achieved significant improvements in allele calling efficiency compared to a conventional gene-by-gene approach. The new program, Fast-GeP, rendered a fast and easy way to infer high-resolution genealogical relationships between bacterial isolates using whole-genome sequencing data. Availability and implementation: FAST-GeP is freely available from: https://github.com/jizhang-nz/fast-GeP. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Large-scale whole-genome sequencing dataset-based studies are becoming increasingly common in pathogen surveillance and outbreak investigations. A highly discriminative and time-efficient bioinformatics tool is needed to transform large amounts of sequencing data into usable biological information. To replace the intuitive, yet inefficient, way of gene-by-gene allele calling algorithm, a new algorithm using genome-by-genome approach was developed. Results: Tests showed that the program equipped with the new algorithm achieved significant improvements in allele calling efficiency compared to a conventional gene-by-gene approach. The new program, Fast-GeP, rendered a fast and easy way to infer high-resolution genealogical relationships between bacterial isolates using whole-genome sequencing data. Availability and implementation: FAST-GeP is freely available from: https://github.com/jizhang-nz/fast-GeP. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Nigel P French; Ji Zhang; Glen P Carter; Anne C Midwinter; Patrick J Biggs; Kristin Dyet; Brent J Gilpin; Danielle J Ingle; Kerry Mulqueen; Lynn E Rogers; David A Wilkinson; Sabrina S Greening; Petra Muellner; Ahmed Fayaz; Deborah A Williamson Journal: Emerg Infect Dis Date: 2019-12 Impact factor: 6.883
Authors: Leah J Toombs-Ruane; Jackie Benschop; Nigel P French; Patrick J Biggs; Anne C Midwinter; Jonathan C Marshall; Maggie Chan; Dragana Drinković; Ahmed Fayaz; Michael G Baker; Jeroen Douwes; Mick G Roberts; Sara A Burgess Journal: Appl Environ Microbiol Date: 2020-11-24 Impact factor: 4.792
Authors: Pippa Scott; Ji Zhang; Trevor Anderson; Patricia C Priest; Stephen Chambers; Helen Smith; David R Murdoch; Nigel French; Patrick J Biggs Journal: Sci Rep Date: 2021-10-13 Impact factor: 4.379
Authors: Sandy Slow; Trevor Anderson; David R Murdoch; Samuel Bloomfield; David Winter; Patrick J Biggs Journal: Sci Rep Date: 2022-04-06 Impact factor: 4.379
Authors: Xiangning Bai; Elisa Ylinen; Ji Zhang; Saara Salmenlinna; Jani Halkilahti; Harri Saxen; Aswathy Narayanan; Timo Jahnukainen; Andreas Matussek Journal: Microbiol Spectr Date: 2022-06-22