Gregory B. Gloor

21.3k total citations · 7 hit papers
157 papers, 14.6k citations indexed

About

Gregory B. Gloor is a scholar working on Molecular Biology, Epidemiology and Microbiology. According to data from OpenAlex, Gregory B. Gloor has authored 157 papers receiving a total of 14.6k indexed citations (citations by other indexed papers that have themselves been cited), including 125 papers in Molecular Biology, 18 papers in Epidemiology and 17 papers in Microbiology. Recurrent topics in Gregory B. Gloor's work include Gut microbiota and health (42 papers), CRISPR and Genetic Engineering (19 papers) and Genomics and Phylogenetic Studies (19 papers). Gregory B. Gloor is often cited by papers focused on Gut microbiota and health (42 papers), CRISPR and Genetic Engineering (19 papers) and Genomics and Phylogenetic Studies (19 papers). Gregory B. Gloor collaborates with scholars based in Canada, United States and Netherlands. Gregory B. Gloor's co-authors include Jean M. Macklaim, Gregor Reid, Vera Pawlowsky‐Glahn, Juan José Egozcue, Andrew D. Fernandes, Lindi M. Wahl, Stanley D. Dunn, William R. Engels, Camilla Urbaniak and David R. Edgell and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Gregory B. Gloor

154 papers receiving 14.4k citations

Hit Papers

Microbiome Datasets Are Compositional: And This Is Not Op... 2013 2026 2017 2021 2017 2019 2014 2013 2013 500 1000 1.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Microbiome Datasets Are Compositional: And This Is Not Optional
    2017 1.7k citations Gregory B. Gloor, Jean M. Macklaim et al. profile →
  2. A new genomic blueprint of the human gut microbiota
    2019 840 citations Gregory B. Gloor et al. profile →
  3. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis
    2014 838 citations Andrew D. Fernandes, Jennifer Reid et al. Microbiome profile →
  4. ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq
    2013 589 citations Andrew D. Fernandes, Jean M. Macklaim et al. PLoS ONE profile →
  5. Stool substitute transplant therapy for the eradication of Clostridium difficile infection: ‘RePOOPulating’ the gut
    2013 522 citations Elaine O. Petrof, Gregory B. Gloor et al. Microbiome profile →
  6. The Microbiota of Breast Tissue and Its Association with Breast Cancer
    2016 461 citations Gregory B. Gloor, Gregor Reid et al. profile →
  7. Microbiota of Human Breast Tissue
    2014 390 citations Jean M. Macklaim, Gregory B. Gloor et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Gregory B. Gloor Canada 53 9.7k 1.8k 1.7k 1.5k 1.4k 157 14.6k
Erica Sodergren United States 46 6.4k 0.7× 1.7k 1.0× 1.7k 1.0× 849 0.6× 1.5k 1.1× 98 12.7k
Bryan A. White United States 63 8.3k 0.8× 1.3k 0.7× 877 0.5× 1.1k 0.8× 1.9k 1.4× 174 14.4k
Levi Waldron United States 37 13.3k 1.4× 2.6k 1.4× 1.7k 1.0× 1.7k 1.1× 2.7k 2.0× 90 21.7k
Jacques Izard United States 29 11.7k 1.2× 2.8k 1.6× 1.7k 1.0× 1.7k 1.1× 3.3k 2.4× 61 20.7k
Julia K. Goodrich United States 25 9.0k 0.9× 1.9k 1.0× 1.1k 0.7× 1.0k 0.7× 2.6k 1.9× 31 14.3k
Elizabeth K. Costello United States 30 8.6k 0.9× 2.2k 1.3× 1.7k 1.0× 450 0.3× 1.8k 1.3× 43 13.9k
Lars Hestbjerg Hansen Denmark 64 9.1k 0.9× 3.8k 2.1× 870 0.5× 1.6k 1.1× 1.6k 1.2× 331 16.9k
Guoping Zhao China 58 10.2k 1.0× 1.1k 0.6× 1.7k 1.0× 1.5k 1.0× 1.1k 0.8× 495 16.6k
Sarah K. Highlander United States 31 5.7k 0.6× 3.0k 1.7× 819 0.5× 1.2k 0.8× 916 0.7× 71 11.7k
Hera Vlamakis United States 50 8.2k 0.8× 1.9k 1.1× 894 0.5× 1.1k 0.8× 943 0.7× 90 11.8k

Countries citing papers authored by Gregory B. Gloor

Since Specialization
Citations

This map shows the geographic impact of Gregory B. Gloor's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gregory B. Gloor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregory B. Gloor more than expected).

Fields of papers citing papers by Gregory B. Gloor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gregory B. Gloor. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gregory B. Gloor. The network helps show where Gregory B. Gloor may publish in the future.

Co-authorship network of co-authors of Gregory B. Gloor

This figure shows the co-authorship network connecting the top 25 collaborators of Gregory B. Gloor. A scholar is included among the top collaborators of Gregory B. Gloor based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gregory B. Gloor. Gregory B. Gloor is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Gloor, Gregory B., et al.. (2025). Uropygial gland microbiota of nearctic−neotropical migrants vary with season and migration distance. Animal Microbiome. 7(1). 11–11.
2.
Santos, Scott J Dos, et al.. (2024). Vaginal metatranscriptome meta-analysis reveals functional BV subgroups and novel colonisation strategies. Microbiome. 12(1). 271–271. 6 indexed citations
3.
Gloor, Gregory B.. (2023). amIcompositional: Simple Tests for Compositional Behaviour of High Throughput Data with Common Transformations. SHILAP Revista de lepidopterología. 52(4). 180–197.
4.
Al, Kait F., John D. Denstedt, Brendan A. Daisley, et al.. (2020). Ureteral Stent Microbiota Is Associated with Patient Comorbidities but Not Antibiotic Exposure. Cell Reports Medicine. 1(6). 100094–100094. 16 indexed citations
5.
Quinn, Thomas P., Ionas Erb, Gregory B. Gloor, et al.. (2019). A field guide for the compositional analysis of any-omics data. GigaScience. 8(9). 195 indexed citations
6.
Bogiatzi, Chrysi, Gregory B. Gloor, Emma Allen‐Vercoe, et al.. (2018). Metabolic products of the intestinal microbiome and extremes of atherosclerosis. Atherosclerosis. 273. 91–97. 110 indexed citations
7.
Berg, Matthew D., et al.. (2018). Acceptor Stem Differences Contribute to Species-Specific Use of Yeast and Human tRNASer. Genes. 9(12). 612–612. 8 indexed citations
8.
Freitas, Aline C., Bonnie Chaban, Alan Bocking, et al.. (2017). The vaginal microbiome of pregnant women is less rich and diverse, with lower prevalence of Mollicutes, compared to non-pregnant women. Scientific Reports. 7(1). 9212–9212. 143 indexed citations
9.
Al, Kait F., Ousseynou Sarr, Gregory B. Gloor, et al.. (2017). Impact of birth weight and postnatal diet on the gut microbiota of young adult guinea pigs. PeerJ. 5. e2840–e2840. 15 indexed citations
10.
Bisanz, Jordan E., W. Murray Thomson, Trudy J. Milne, et al.. (2016). The oral microbiome of patients with axial spondyloarthritis compared to healthy individuals. PeerJ. 4. e2095–e2095. 15 indexed citations
11.
McMillan, Amy, Stephen Rulisa, Mark W. Sumarah, et al.. (2015). A multi-platform metabolomics approach identifies highly specific biomarkers of bacterial diversity in the vagina of pregnant and non-pregnant women. Scientific Reports. 5(1). 14174–14174. 91 indexed citations
12.
Fernandes, Andrew D., Jennifer Reid, Jean M. Macklaim, et al.. (2014). Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2(1). 15–15. 838 indexed citations breakdown →
13.
Gloor, Gregory B., et al.. (2014). Microbial composition analysis ofClostridium difficileinfections in an ulcerative colitis patient treated with multiple fecal microbiota transplantations. Journal of Crohn s and Colitis. 8(9). 1133–1137. 20 indexed citations
14.
McMurrough, Thomas A, et al.. (2014). Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues. Proceedings of the National Academy of Sciences. 111(23). E2376–83. 27 indexed citations
15.
Gan, Xiaohong Tracey, Cathy Huang, Jeremy P. Burton, et al.. (2014). Probiotic Administration Attenuates Myocardial Hypertrophy and Heart Failure After Myocardial Infarction in the Rat. Circulation Heart Failure. 7(3). 491–499. 254 indexed citations
16.
Anukam, Kingsley C., Jean M. Macklaim, Gregory B. Gloor, et al.. (2013). Genome Sequence of Lactobacillus pentosus KCA1: Vaginal Isolate from a Healthy Premenopausal Woman. PLoS ONE. 8(3). e59239–e59239. 46 indexed citations
17.
18.
Dunn, Stanley D., Lindi M. Wahl, & Gregory B. Gloor. (2007). Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics. 24(3). 333–340. 339 indexed citations
19.
Gloor, Gregory B., et al.. (2005). Using information theory to search for co-evolving residues in proteins. Bioinformatics. 21(22). 4116–4124. 195 indexed citations
20.
Lankenau, Dirk‐Henner & Gregory B. Gloor. (1998). In vivo gap repair in Drosophila: a one-way street with many destinations. BioEssays. 20(4). 317–327. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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