Peter J. Shepard

1.9k total citations
19 papers, 1.4k citations indexed

About

Peter J. Shepard is a scholar working on Molecular Biology, Cell Biology and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Peter J. Shepard has authored 19 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 2 papers in Cell Biology and 1 paper in Pediatrics, Perinatology and Child Health. Recurrent topics in Peter J. Shepard's work include RNA Research and Splicing (8 papers), RNA modifications and cancer (7 papers) and RNA and protein synthesis mechanisms (5 papers). Peter J. Shepard is often cited by papers focused on RNA Research and Splicing (8 papers), RNA modifications and cancer (7 papers) and RNA and protein synthesis mechanisms (5 papers). Peter J. Shepard collaborates with scholars based in United States, Netherlands and Austria. Peter J. Shepard's co-authors include Klemens J. Hertel, Eun‐A Choi, Jente Lu, Lisa A. Flanagan, Yongsheng Shi, Bruce Seligmann, Joanne M. Yeakley, Harper VanSteenhouse, Kelly A. Frazer and Kristen Jepsen and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Molecular and Cellular Biology.

In The Last Decade

Peter J. Shepard

19 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter J. Shepard United States 12 1.2k 172 76 64 57 19 1.4k
Bruno Morolli Netherlands 13 521 0.4× 206 1.2× 54 0.7× 16 0.3× 18 0.3× 21 762
Yuxun Wang United States 15 605 0.5× 149 0.9× 57 0.8× 36 0.6× 7 0.1× 29 878
Stefano Amente Italy 21 1.2k 1.0× 275 1.6× 24 0.3× 37 0.6× 9 0.2× 42 1.4k
Anja Füllgrabe United Kingdom 7 591 0.5× 119 0.7× 17 0.2× 21 0.3× 22 0.4× 7 807
Geng Tian China 18 635 0.5× 232 1.3× 28 0.4× 18 0.3× 9 0.2× 52 924
Caitlin Hall United Kingdom 8 580 0.5× 74 0.4× 13 0.2× 125 2.0× 30 0.5× 13 961
Consuelo Pitolli Italy 8 493 0.4× 163 0.9× 26 0.3× 23 0.4× 9 0.2× 15 794
Jana Čmejlová Czechia 12 648 0.5× 53 0.3× 23 0.3× 76 1.2× 7 0.1× 26 909
Richard I. Near United States 20 656 0.6× 136 0.8× 126 1.7× 30 0.5× 11 0.2× 40 1.2k
Lee Sam United States 10 787 0.7× 311 1.8× 6 0.1× 33 0.5× 61 1.1× 13 1.1k

Countries citing papers authored by Peter J. Shepard

Since Specialization
Citations

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

Fields of papers citing papers by Peter J. Shepard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peter J. Shepard. 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 Peter J. Shepard. The network helps show where Peter J. Shepard may publish in the future.

Co-authorship network of co-authors of Peter J. Shepard

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

All Works

19 of 19 papers shown
1.
Trejo, Christy L., Miloš Babić, Elliot Imler, et al.. (2019). Extraction-free whole transcriptome gene expression analysis of FFPE sections and histology-directed subareas of tissue. PLoS ONE. 14(2). e0212031–e0212031. 25 indexed citations
2.
Mav, Deepak, Dhiral Phadke, Logan J. Everett, et al.. (2019). Development of a Zebrafish S1500+ Sentinel Gene Set for High-Throughput Transcriptomics. Zebrafish. 16(4). 331–347. 9 indexed citations
3.
Trejo, Christy L., Elliot Imler, Miloš Babić, et al.. (2019). Abstract 5241: Whole transcriptome TempO-Seq profiling of focal areas of H&E stained FFPE: Differentiation of normal colon and cancer phenotypes between donors. Cancer Research. 79(13_Supplement). 5241–5241. 1 indexed citations
4.
Limonciel, Alice, Gamze Ates, Giada Carta, et al.. (2018). Comparison of base-line and chemical-induced transcriptomic responses in HepaRG and RPTEC/TERT1 cells using TempO-Seq. Archives of Toxicology. 92(8). 2517–2531. 38 indexed citations
5.
Panopoulos, Athanasia D., Erin N. Smith, Angelo D. Arias, et al.. (2017). Aberrant DNA Methylation in Human iPSCs Associates with MYC-Binding Motifs in a Clone-Specific Manner Independent of Genetics. Cell stem cell. 20(4). 505–517.e6. 25 indexed citations
6.
Yeakley, Joanne M., et al.. (2017). A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling. PLoS ONE. 12(5). e0178302–e0178302. 158 indexed citations
7.
VanSteenhouse, Harper, Peter J. Shepard, Joanne M. Yeakley, & Bruce Seligmann. (2017). Targeted whole transcriptome gene expression profiling for mechanistic toxicology. Toxicology Letters. 280. S295–S295. 2 indexed citations
8.
Grimm, Fabian A., Yasuhiro Iwata, Oksana Sirenko, et al.. (2016). A chemical–biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives. Green Chemistry. 18(16). 4407–4419. 63 indexed citations
9.
DeBoever, Christopher, Emanuela M. Ghia, Peter J. Shepard, et al.. (2015). Transcriptome Sequencing Reveals Potential Mechanism of Cryptic 3’ Splice Site Selection in SF3B1-mutated Cancers. PLoS Computational Biology. 11(3). e1004105–e1004105. 159 indexed citations
10.
Shepard, Peter J., Bruce A. Barshop, Matthias R. Baumgartner, et al.. (2014). Consanguinity and rare mutations outside of MCCC genes underlie nonspecific phenotypes of MCCD. Genetics in Medicine. 17(8). 660–667. 8 indexed citations
11.
Smith, Erin N., Kristen Jepsen, Angelo D. Arias, et al.. (2014). Genetic ancestry of participants in the National Children’s Study. Genome biology. 15(2). R22–R22. 7 indexed citations
12.
Shepard, Peter J., Eun‐A Choi, Anke Busch, & Klemens J. Hertel. (2011). Efficient internal exon recognition depends on near equal contributions from the 3′ and 5′ splice sites. Nucleic Acids Research. 39(20). 8928–8937. 32 indexed citations
13.
Shepard, Peter J., Eun‐A Choi, Jente Lu, et al.. (2011). Complex and dynamic landscape of RNA polyadenylation revealed by PAS-Seq. RNA. 17(4). 761–772. 333 indexed citations
14.
Shepard, Peter J. & Klemens J. Hertel. (2010). Embracing the complexity of pre-mRNA splicing. Cell Research. 20(8). 866–868. 5 indexed citations
15.
Hicks, Martin J., William F. Mueller, Peter J. Shepard, & Klemens J. Hertel. (2010). Competing Upstream 5′ Splice Sites Enhance the Rate of Proximal Splicing. Molecular and Cellular Biology. 30(8). 1878–1886. 33 indexed citations
16.
Shepard, Peter J. & Klemens J. Hertel. (2009). The SR protein family. Genome Biology. 10(10). 242–242. 334 indexed citations
17.
Shepard, Peter J. & Klemens J. Hertel. (2008). Conserved RNA secondary structures promote alternative splicing. RNA. 14(8). 1463–1469. 116 indexed citations
18.
Koller, Erich, Stephanie Propp, Hong Zhang, et al.. (2006). Use of a Chemically Modified Antisense Oligonucleotide Library to Identify and Validate Eg5 (Kinesin-Like 1) as a Target for Antineoplastic Drug Development. Cancer Research. 66(4). 2059–2066. 32 indexed citations
19.
Propp, Stephanie, Erich Koller, Robert I. Glazer, et al.. (2005). Inhibition of Eg5 (kinesin-like 1) with Antisense Oligonucleotides leads to cell-cycle arrest in G2/M and antitumor activity against xenograft tumors. Cancer Research. 65. 1047–1047. 1 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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