Noelle Cockett

3.5k total citations
37 papers, 2.1k citations indexed

About

Noelle Cockett is a scholar working on Molecular Biology, Genetics and Genetics. According to data from OpenAlex, Noelle Cockett has authored 37 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 25 papers in Genetics and 5 papers in Genetics. Recurrent topics in Noelle Cockett's work include Genetic Syndromes and Imprinting (13 papers), Muscle Physiology and Disorders (9 papers) and Genomics and Chromatin Dynamics (6 papers). Noelle Cockett is often cited by papers focused on Genetic Syndromes and Imprinting (13 papers), Muscle Physiology and Disorders (9 papers) and Genomics and Chromatin Dynamics (6 papers). Noelle Cockett collaborates with scholars based in United States, Belgium and Australia. Noelle Cockett's co-authors include Michel Georges, Carole Charlier, T. L. Shay, Karin Segers, Erica E. Davis, Florian Caiment, Gàbor Gyapay, Stéphane Berghmans, Xavier Tordoir and Christopher A. Bidwell and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Noelle Cockett

37 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Noelle Cockett United States 22 1.4k 1.3k 460 252 171 37 2.1k
Pablo J. Ross United States 36 1.3k 0.9× 2.4k 1.8× 287 0.6× 327 1.3× 80 0.5× 150 3.6k
B. A. Freking United States 30 1.5k 1.1× 983 0.7× 204 0.4× 173 0.7× 434 2.5× 91 2.5k
Alex Van Zeveren Belgium 22 712 0.5× 810 0.6× 174 0.4× 72 0.3× 242 1.4× 92 1.8k
Bernard Grisart Belgium 20 2.8k 2.0× 683 0.5× 507 1.1× 88 0.3× 211 1.2× 32 3.3k
Ramiro Alberio United Kingdom 29 845 0.6× 1.8k 1.4× 74 0.2× 193 0.8× 45 0.3× 73 2.4k
M. Świtoński Poland 26 2.0k 1.5× 1.1k 0.8× 204 0.4× 87 0.3× 373 2.2× 227 2.9k
A. Iritani Japan 33 876 0.6× 1.2k 0.9× 49 0.1× 212 0.8× 81 0.5× 149 3.2k
S M Kappes United States 23 2.3k 1.6× 637 0.5× 256 0.6× 55 0.2× 385 2.3× 59 2.8k
Rodney D. Geisert United States 37 1.3k 1.0× 780 0.6× 205 0.4× 165 0.7× 266 1.6× 92 3.6k
Izabela Szczerbal Poland 20 815 0.6× 787 0.6× 96 0.2× 59 0.2× 62 0.4× 108 1.3k

Countries citing papers authored by Noelle Cockett

Since Specialization
Citations

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

Fields of papers citing papers by Noelle Cockett

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noelle Cockett. 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 Noelle Cockett. The network helps show where Noelle Cockett may publish in the future.

Co-authorship network of co-authors of Noelle Cockett

This figure shows the co-authorship network connecting the top 25 collaborators of Noelle Cockett. A scholar is included among the top collaborators of Noelle Cockett 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 Noelle Cockett. Noelle Cockett 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.
Rosen, Benjamin D., Holly L. Neibergs, Kimberly M Davenport, et al.. (2024). Telomere-to-telomere assemblies of cattle and sheep Y-chromosomes uncover divergent structure and gene content. Nature Communications. 15(1). 8277–8277. 13 indexed citations
2.
Yu, Hui, et al.. (2018). Identification of genes directly responding to DLK1 signaling in Callipyge sheep. BMC Genomics. 19(1). 283–283. 18 indexed citations
3.
Goddard, Michael E., Michael A. Black, Rüdiger Bräuning, et al.. (2016). Copy number variants in the sheep genome detected using multiple approaches. BMC Genomics. 17(1). 441–441. 29 indexed citations
4.
Li, Juan, P. L. Greenwood, Noelle Cockett, et al.. (2014). Impacts of the Callipyge Mutation on Ovine Plasma Metabolites and Muscle Fibre Type. PLoS ONE. 9(6). e99726–e99726. 7 indexed citations
5.
Cheng, Huijun, Xuewen Xu, Noelle Cockett, et al.. (2014). Experimental evaluation does not reveal a direct effect of microRNA from the callipyge locus on DLK1 expression. BMC Genomics. 15(1). 944–944. 3 indexed citations
6.
Caiment, Florian, et al.. (2010). Assessing the effect of the CLPG mutation on the microRNA catalog of skeletal muscle using high-throughput sequencing. Genome Research. 20(12). 1651–1662. 37 indexed citations
7.
Jann, O., S.I. Anderson, Kirsty Jensen, et al.. (2009). Comparative genomics of Toll-like receptor signalling in five species. BMC Genomics. 10(1). 216–216. 35 indexed citations
8.
Olbricht, Gayla R., Tasia M. Taxis, Jason D. White, et al.. (2009). Effect of DLK1 and RTL1 but Not MEG3 or MEG8 on Muscle Gene Expression in Callipyge Lambs. PLoS ONE. 4(10). e7399–e7399. 52 indexed citations
9.
Kijas, James, Brian P. Dalrymple, Michael P. Heaton, et al.. (2009). A Genome Wide Survey of SNP Variation Reveals the Genetic Structure of Sheep Breeds. PLoS ONE. 4(3). e4668–e4668. 269 indexed citations
10.
Cockett, Noelle & Chittaranjan Kole. (2008). Genome Mapping and Genomics in Domestic Animals. DIAL (Catholic University of Leuven). 27 indexed citations
11.
Dalrymple, Brian P., Ewen F. Kirkness, Mikhail Nefedov, et al.. (2007). Using comparative genomics to reorder the human genome sequence into a virtual sheep genome. Genome biology. 8(7). R152–R152. 69 indexed citations
12.
Takeda, Haruko, Florian Caiment, Maria A. Smit, et al.. (2006). The callipyge mutation enhances bidirectional long-range DLK1-GTL2 intergenic transcription in cis. Proceedings of the National Academy of Sciences. 103(21). 8119–8124. 40 indexed citations
13.
Vuocolo, Tony, Keren Byrne, Jason D. White, et al.. (2006). Identification of a gene network contributing to hypertrophy in callipyge skeletal muscle. Physiological Genomics. 28(3). 253–272. 65 indexed citations
14.
Davis, Erica E., Florian Caiment, Xavier Tordoir, et al.. (2005). RNAi-Mediated Allelic trans-Interaction at the Imprinted Rtl1/Peg11 Locus. Current Biology. 15(8). 743–749. 261 indexed citations
15.
Smit, Maria A., Xavier Tordoir, Gàbor Gyapay, et al.. (2005). BEGAIN: A novel imprinted gene that generates paternally expressed transcripts in a tissue- and promoter-specific manner in sheep. Mammalian Genome. 16(10). 801–814. 14 indexed citations
16.
Notter, D. R. & Noelle Cockett. (2005). Opportunities for detection and use of QTL influencing seasonal reproduction in sheep: a review. Genetics Selection Evolution. 37(S1). S39–53. 55 indexed citations
17.
Davis, Erica E., Charlotte Harken Jensen, Henrik Daa Schrøder, et al.. (2004). Ectopic Expression of DLK1 Protein in Skeletal Muscle of Padumnal Heterozygotes Causes the Callipyge Phenotype. Current Biology. 14(20). 1858–1862. 104 indexed citations
18.
Cockett, Noelle, Maria A. Smit, Christopher A. Bidwell, et al.. (2004). The callipyge mutation and other genes that affect muscle hypertrophy in sheep. Genetics Selection Evolution. 37(S1). S65–S81. 45 indexed citations
19.
Georges, Michel, Carole Charlier, & Noelle Cockett. (2003). The callipyge locus: evidence for the trans interaction of reciprocally imprinted genes. Trends in Genetics. 19(5). 248–252. 117 indexed citations
20.

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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