Daniel A. Skelly

2.8k total citations · 1 hit paper
34 papers, 1.7k citations indexed

About

Daniel A. Skelly is a scholar working on Molecular Biology, Genetics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Daniel A. Skelly has authored 34 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 11 papers in Genetics and 4 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Daniel A. Skelly's work include Genetic Mapping and Diversity in Plants and Animals (10 papers), Fungal and yeast genetics research (7 papers) and Epigenetics and DNA Methylation (5 papers). Daniel A. Skelly is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (10 papers), Fungal and yeast genetics research (7 papers) and Epigenetics and DNA Methylation (5 papers). Daniel A. Skelly collaborates with scholars based in United States, United Kingdom and Australia. Daniel A. Skelly's co-authors include Nadia Rosenthal, Micheal A. McLellan, Alexander R. Pinto, Galen T Squiers, Joshua M. Akey, Paul Robson, Mohan Bolisetty, Paul M. Magwene, Eric A. Stone and Fred S. Dietrich and has published in prestigious journals such as Nucleic Acids Research, Circulation and The EMBO Journal.

In The Last Decade

Daniel A. Skelly

33 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart

2018 373 citations Daniel A. Skelly, Galen T Squiers et al. Cell Reports profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daniel A. Skelly United States	16	1.2k	388	248	219	191	34	1.7k
Juliane Ramser Germany	20	934 0.8×	86 0.2×	376 1.5×	94 0.4×	240 1.3×	54	1.8k
Phillip A. Richmond Canada	11	1.4k 1.2×	58 0.1×	387 1.6×	58 0.3×	318 1.7×	25	2.0k
Nicolás Bellora Argentina	20	1.4k 1.2×	48 0.1×	213 0.9×	177 0.8×	361 1.9×	42	1.9k
Chee Peng Ng Singapore	13	1.2k 1.0×	67 0.2×	353 1.4×	67 0.3×	63 0.3×	14	2.0k
Francisco J. Sáez Spain	21	653 0.6×	28 0.1×	148 0.6×	285 1.3×	312 1.6×	78	1.6k
Ian J. Donaldson United Kingdom	26	1.6k 1.4×	24 0.1×	355 1.4×	83 0.4×	155 0.8×	51	2.1k
Takehiko Ogura Japan	25	1.2k 1.0×	392 1.0×	171 0.7×	11 0.1×	452 2.4×	53	1.9k
Françoise Lamy Belgium	26	989 0.9×	39 0.1×	443 1.8×	41 0.2×	414 2.2×	69	2.2k
Shinji Honda Japan	26	1.3k 1.2×	115 0.3×	110 0.4×	12 0.1×	449 2.4×	51	2.0k
Wendie S. Cohick United States	24	643 0.6×	148 0.4×	450 1.8×	13 0.1×	33 0.2×	55	1.9k

Countries citing papers authored by Daniel A. Skelly

Since Specialization

Citations

This map shows the geographic impact of Daniel A. Skelly'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 Daniel A. Skelly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel A. Skelly more than expected).

Fields of papers citing papers by Daniel A. Skelly

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel A. Skelly. 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 Daniel A. Skelly. The network helps show where Daniel A. Skelly may publish in the future.

Co-authorship network of co-authors of Daniel A. Skelly

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Skelly. A scholar is included among the top collaborators of Daniel A. Skelly 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 Daniel A. Skelly. Daniel A. Skelly is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Skelly, Daniel A., Mingshan Cheng, Mayuko Furuta, et al.. (2025). Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response. Cell Reports. 44(5). 115698–115698. 2 indexed citations

Skelly, Daniel A., J. Matthew Mahoney, Ted Choi, et al.. (2025). Systems genetics reveals the influence of expression QTLs in mouse embryonic stem cells on transcriptional variation later in differentiated neural progenitor cells. G3 Genes Genomes Genetics. 15(7).

Poirion, Olivier, Wu‐Lin Zuo, Candice N. Baker, et al.. (2024). Enhlink infers distal and context-specific enhancer–promoter linkages. Genome biology. 25(1). 235–235. 1 indexed citations

Zhang, Tian, Gregory R. Keele, Daniel A. Skelly, et al.. (2023). Genetic dissection of the pluripotent proteome through multi-omics data integration. Cell Genomics. 3(4). 100283–100283. 8 indexed citations

Kozmin, Stanislav G., Pooja K Strope, Daniel A. Skelly, et al.. (2023). RNA viruses, M satellites, chromosomal killer genes, and killer/nonkiller phenotypes in the 100-genomes S. cerevisiae strains. G3 Genes Genomes Genetics. 13(10). 6 indexed citations

Skelly, Daniel A., Anne Czechanski, Steven C. Munger, et al.. (2023). Imputation of 3D genome structure by genetic–epigenetic interaction modeling in mice. eLife. 12. 1 indexed citations

Yang, Hongtian, Kristen D. Onos, Kwangbom Choi, et al.. (2021). Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer’s disease. Cell Reports. 34(6). 108739–108739. 53 indexed citations

Hosur, Vishnu, Daniel A. Skelly, Benjamin E. Low, et al.. (2020). Improved mouse models and advanced genetic and genomic technologies for the study of neutrophils. Drug Discovery Today. 25(6). 1013–1025. 4 indexed citations

Ortmann, Daniel, Stephanie Brown, Anne Czechanski, et al.. (2020). Naive Pluripotent Stem Cells Exhibit Phenotypic Variability that Is Driven by Genetic Variation. Cell stem cell. 27(3). 470–481.e6. 31 indexed citations

10.

Forte, Elvira, Daniel A. Skelly, Mandy Chen, et al.. (2020). Dynamic Interstitial Cell Response during Myocardial Infarction Predicts Resilience to Rupture in Genetically Diverse Mice. Cell Reports. 30(9). 3149–3163.e6. 128 indexed citations

11.

McLellan, Micheal A., Daniel A. Skelly, Malathi S.I. Dona, et al.. (2020). High-Resolution Transcriptomic Profiling of the Heart During Chronic Stress Reveals Cellular Drivers of Cardiac Fibrosis and Hypertrophy. Circulation. 142(15). 1448–1463. 178 indexed citations

12.

Skelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, & Elissa J. Chesler. (2019). Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples. Genetics. 212(3). 919–929. 6 indexed citations

13.

Skelly, Daniel A., Galen T Squiers, Micheal A. McLellan, et al.. (2018). Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart. Cell Reports. 22(3). 600–610. 373 indexed citations breakdown →

14.

Skelly, Daniel A., et al.. (2017). Known mutator alleles do not markedly increase mutation rate in clinical Saccharomyces cerevisiae strains. Proceedings of the Royal Society B Biological Sciences. 284(1852). 20162672–20162672. 9 indexed citations

15.

Bogue, Molly A., Stephen C. Grubb, Vivek M. Philip, et al.. (2017). Mouse Phenome Database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice. Nucleic Acids Research. 46(D1). D843–D850. 43 indexed citations

16.

Skelly, Daniel A., Paul M. Magwene, & Eric A. Stone. (2015). Sporadic, Global Linkage Disequilibrium Between Unlinked Segregating Sites. Genetics. 202(2). 427–437. 14 indexed citations

17.

Strope, Pooja K, Daniel A. Skelly, Stanislav G. Kozmin, et al.. (2015). The 100-genomes strains, an S. cerevisiae resource that illuminates its natural phenotypic and genotypic variation and emergence as an opportunistic pathogen. Genome Research. 25(5). 762–774. 260 indexed citations

18.

Strope, Pooja K, Stanislav G. Kozmin, Daniel A. Skelly, et al.. (2015). 2μ plasmid inSaccharomycesspecies and inSaccharomyces cerevisiae. FEMS Yeast Research. 15(8). fov090–fov090. 17 indexed citations

19.

Connelly, Caitlin, Daniel A. Skelly, Maitreya J. Dunham, & Joshua M. Akey. (2013). Population Genomics and Transcriptional Consequences of Regulatory Motif Variation in Globally Diverse Saccharomyces cerevisiae Strains. Molecular Biology and Evolution. 30(7). 1605–1613. 10 indexed citations

20.

Skelly, Daniel A., Marnie Johansson, Jennifer Madeoy, Jon Wakefield, & Joshua M. Akey. (2011). A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Research. 21(10). 1728–1737. 130 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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