A graph-based approach for the visualisation and analysis of bacterial pangenomes

Josh Harling-Lee; Jamie Gorzynski; Gonzalo Yebra; Tim Angus; Ross Fitzgerald; Thomas C Freeman

doi:10.1186/s12859-022-04898-2

A graph-based approach for the visualisation and analysis of bacterial pangenomes

Josh Harling-Lee^*, Jamie Gorzynski, Gonzalo Yebra, Tim Angus, Ross Fitzgerald, Thomas C Freeman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Background
The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species.
Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation.
Results
Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences.
Conclusions
We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Original language	English
Pages (from-to)	1-15
Number of pages	15
Journal	BMC Bioinformatics
Volume	23
Issue number	416
Early online date	8 Oct 2022
DOIs	https://doi.org/10.1186/s12859-022-04898-2
Publication status	Published - Dec 2022

Keywords

Bacteria
Pangenome
Accessory genes
Network graphs
Data visualisation

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10.1186/s12859-022-04898-2Licence: Creative Commons: Attribution (CC-BY)

s12859-022-04898-2Final published version, 3.5 MBLicence: Creative Commons: Attribution (CC-BY)

Cite this

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title = "A graph-based approach for the visualisation and analysis of bacterial pangenomes",

abstract = "BackgroundThe advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. ResultsStarting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. ConclusionsWe anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.",

keywords = "Bacteria, Pangenome, Accessory genes, Network graphs, Data visualisation",

author = "Josh Harling-Lee and Jamie Gorzynski and Gonzalo Yebra and Tim Angus and Ross Fitzgerald and Freeman, {Thomas C}",

note = "Funding Information: We would like to acknowledge Sion Bayliss for many helpful discussions on the use of PIRATE for this analysis, and for specific advice on the use of PIRATE adapter scripts. We also thank Bryan Wee and Barbara Shih for their helpful input and advice. Funding Information: This work was supported by the Biotechnology and Biological Sciences Research Council ( https://bsrc.ukri.org/ ) (institute strategic grants ISP2 BB/PO13740/1 to JRF and BB/P013732/1 to TCF) and the Wellcome Trust ( https://wellcome.org/ ) (grant reference 201531/Z/16/Z awarded to JRF). JDHL is supported by a Principal{\textquoteright}s Career Development Scholarship (University of Edinburgh), and JG is supported by an MRC Precision Medicine Studentship (grant reference MR/N013166/1). The funders had no role in study design, data collection and analysis, interpretation of the data, or preparation of the manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

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doi = "10.1186/s12859-022-04898-2",

language = "English",

volume = "23",

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journal = "BMC Bioinformatics",

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T1 - A graph-based approach for the visualisation and analysis of bacterial pangenomes

AU - Harling-Lee, Josh

AU - Gorzynski, Jamie

AU - Yebra, Gonzalo

AU - Angus, Tim

AU - Fitzgerald, Ross

AU - Freeman, Thomas C

N1 - Funding Information: We would like to acknowledge Sion Bayliss for many helpful discussions on the use of PIRATE for this analysis, and for specific advice on the use of PIRATE adapter scripts. We also thank Bryan Wee and Barbara Shih for their helpful input and advice. Funding Information: This work was supported by the Biotechnology and Biological Sciences Research Council ( https://bsrc.ukri.org/ ) (institute strategic grants ISP2 BB/PO13740/1 to JRF and BB/P013732/1 to TCF) and the Wellcome Trust ( https://wellcome.org/ ) (grant reference 201531/Z/16/Z awarded to JRF). JDHL is supported by a Principal’s Career Development Scholarship (University of Edinburgh), and JG is supported by an MRC Precision Medicine Studentship (grant reference MR/N013166/1). The funders had no role in study design, data collection and analysis, interpretation of the data, or preparation of the manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: © 2022, The Author(s).

PY - 2022/12

Y1 - 2022/12

N2 - BackgroundThe advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. ResultsStarting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. ConclusionsWe anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

AB - BackgroundThe advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. ResultsStarting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. ConclusionsWe anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

KW - Bacteria

KW - Pangenome

KW - Accessory genes

KW - Network graphs

KW - Data visualisation

U2 - 10.1186/s12859-022-04898-2

DO - 10.1186/s12859-022-04898-2

M3 - Article

C2 - 36209064

VL - 23

SP - 1

EP - 15

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - 416

ER -

A graph-based approach for the visualisation and analysis of bacterial pangenomes

Abstract

Keywords

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