Demo Data from EDBT 2026 / reviewers in the wild / expert
Thomas H. Clarke
dblp:58/5847
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2019
0000-0002-1916-1029ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
comparative genomics |
0.7 | 2 | 2019 | Large-scale comparative analysis of microbial pan-genomes using PanOCT · Bioinform. 2019 GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models · Bioinform. 2018 |
Bioinformatics and computational biology › comparative genomics › pangenomics
pan-genome analysis |
0.4 | 1 | 2019 | Large-scale comparative analysis of microbial pan-genomes using PanOCT · Bioinform. 2019 |
Bioinformatics and computational biology › statistical genetics › genomic prediction
genomic selection |
0.3 | 1 | 2018 | GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models · Bioinform. 2018 |
Bioinformatics and computational biology › metagenomics
antimicrobial resistance gene detection |
0.1 | 1 | 2019 | Large-scale comparative analysis of microbial pan-genomes using PanOCT · Bioinform. 2019 |
Bioinformatics and computational biology › genomics › microbial genomics
bacterial genome analysis |
0.1 | 1 | 2018 | GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models · Bioinform. 2018 |
Bioinformatics and computational biology
genomics |
0.1 | 1 | 2018 | GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models · Bioinform. 2018 |
Methods — techniques the papers use, named apart from their topics
sequence alignment · 0.4hierarchical clustering · 0.4unsupervised clustering · 0.3gaussian mixture model · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Large-scale comparative analysis of microbial pan-genomes using PanOCTabstractSUMMARY: The JCVI pan-genome pipeline is a collection of programs to run PanOCT and tools that support and extend the capabilities of PanOCT. PanOCT (pan-genome ortholog clustering tool) is a tool for pan-genome analysis of closely related prokaryotic species or strains. The JCVI Pan-Genome Pipeline wrapper invokes command-line utilities that prepare input genomes, invoke third-party tools such as NCBI Blast+, run PanOCT, generate a consensus pan-genome, annotate features of the pan-genome, detect sets of genes of interest such as antimicrobial resistance (AMR) genes and generate figures, tables and html pages to visualize the results. The pipeline can run in a hierarchical mode, lowering the RAM and compute resources used. AVAILABILITY AND IMPLEMENTATION: Source code, demo data, and detailed documentation are freely available at https://github.com/JCVenterInstitute/PanGenomePipeline. Jason M. Inman, Granger G. Sutton, Erin Beck, Lauren M. Brinkac, Thomas H. Clarke, Derrick E. Fouts |
Bioinform. | 5 |
| 2018 | GGRaSP: a R-package for selecting representative genomes using Gaussian mixture modelsabstractMotivation: The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. Results: The Gaussian Genome Representative Selector with Prioritization (GGRaSP) R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian mixture model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. Availability and implementaion: GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. Supplementary information: Supplementary data are available at Bioinformatics online. Thomas H. Clarke, Lauren M. Brinkac, Granger G. Sutton, Derrick E. Fouts |
Bioinform. | 1 |
| 2018 | PanACEA: a bioinformatics tool for the exploration and visualization of bacterial pan-chromosomesabstractBACKGROUND: Bacterial pan-genomes, comprised of conserved and variable genes across multiple sequenced bacterial genomes, allow for identification of genomic regions that are phylogenetically discriminating or functionally important. Pan-genomes consist of large amounts of data, which can restrict researchers ability to locate and analyze these regions. Multiple software packages are available to visualize pan-genomes, but currently their ability to address these concerns are limited by using only pre-computed data sets, prioritizing core over variable gene clusters, or by not accounting for pan-chromosome positioning in the viewer. RESULTS: We introduce PanACEA (Pan-genome Atlas with Chromosome Explorer and Analyzer), which utilizes locally-computed interactive web-pages to view ordered pan-genome data. It consists of multi-tiered, hierarchical display pages that extend from pan-chromosomes to both core and variable regions to single genes. Regions and genes are functionally annotated to allow for rapid searching and visual identification of regions of interest with the option that user-supplied genomic phylogenies and metadata can be incorporated. PanACEA's memory and time requirements are within the capacities of standard laptops. The capability of PanACEA as a research tool is demonstrated by highlighting a variable region important in differentiating strains of Enterobacter hormaechei. CONCLUSIONS: PanACEA can rapidly translate the results of pan-chromosome programs into an intuitive and interactive visual representation. It will empower researchers to visually explore and identify regions of the pan-chromosome that are most biologically interesting, and to obtain publication quality images of these regions. Thomas H. Clarke, Lauren M. Brinkac, Jason M. Inman, Granger G. Sutton, Derrick E. Fouts |
BMC Bioinform. | 1 |