A graph-based approach for the visualisation and analysis of bacterial pangenomes
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04898-2 |
| _version_ | 1828194717376970752 |
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| author | Joshua D. Harling-Lee Jamie Gorzynski Gonzalo Yebra Tim Angus J. Ross Fitzgerald Tom C. Freeman |
| author_facet | Joshua D. Harling-Lee Jamie Gorzynski Gonzalo Yebra Tim Angus J. Ross Fitzgerald Tom C. Freeman |
| author_sort | Joshua D. Harling-Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T09:30:48Z |
| format | Article |
| id | doaj.art-f272dff8060b454c91c5bc0d4a7a85e6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T09:30:48Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f272dff8060b454c91c5bc0d4a7a85e62022-12-22T03:38:23ZengBMCBMC Bioinformatics1471-21052022-10-0123111510.1186/s12859-022-04898-2A graph-based approach for the visualisation and analysis of bacterial pangenomesJoshua D. Harling-Lee0Jamie Gorzynski1Gonzalo Yebra2Tim Angus3J. Ross Fitzgerald4Tom C. Freeman5The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of EdinburghThe Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of EdinburghThe Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of EdinburghRoslin Innovation CentreThe Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of EdinburghThe Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of EdinburghAbstract 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.https://doi.org/10.1186/s12859-022-04898-2BacteriaPangenomeAccessory genesNetwork graphsData visualisation |
| spellingShingle | Joshua D. Harling-Lee Jamie Gorzynski Gonzalo Yebra Tim Angus J. Ross Fitzgerald Tom C. Freeman A graph-based approach for the visualisation and analysis of bacterial pangenomes BMC Bioinformatics Bacteria Pangenome Accessory genes Network graphs Data visualisation |
| title | A graph-based approach for the visualisation and analysis of bacterial pangenomes |
| title_full | A graph-based approach for the visualisation and analysis of bacterial pangenomes |
| title_fullStr | A graph-based approach for the visualisation and analysis of bacterial pangenomes |
| title_full_unstemmed | A graph-based approach for the visualisation and analysis of bacterial pangenomes |
| title_short | A graph-based approach for the visualisation and analysis of bacterial pangenomes |
| title_sort | graph based approach for the visualisation and analysis of bacterial pangenomes |
| topic | Bacteria Pangenome Accessory genes Network graphs Data visualisation |
| url | https://doi.org/10.1186/s12859-022-04898-2 |
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