Ryan M. Layer

6.5k total citations · 1 hit paper
33 papers, 2.8k citations indexed

About

Ryan M. Layer is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Ryan M. Layer has authored 33 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 18 papers in Genetics and 7 papers in Cancer Research. Recurrent topics in Ryan M. Layer's work include Genomics and Phylogenetic Studies (15 papers), Genomics and Rare Diseases (12 papers) and Genomic variations and chromosomal abnormalities (7 papers). Ryan M. Layer is often cited by papers focused on Genomics and Phylogenetic Studies (15 papers), Genomics and Rare Diseases (12 papers) and Genomic variations and chromosomal abnormalities (7 papers). Ryan M. Layer collaborates with scholars based in United States, United Kingdom and Norway. Ryan M. Layer's co-authors include Aaron R. Quinlan, Ira M. Hall, Colby Chiang, Anindya Dutta, Brent S. Pedersen, Jeffrey Gagan, Bijan K. Dey, Gábor Marth, Michael Lindberg and Gregory G. Faust and has published in prestigious journals such as Nature, Science and Nucleic Acids Research.

In The Last Decade

Ryan M. Layer

31 papers receiving 2.8k citations

Hit Papers

LUMPY: a probabilistic framework for structural variant d... 2014 2026 2018 2022 2014 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ryan M. Layer United States 20 1.8k 1.2k 836 464 138 33 2.8k
Thomas Zichner Germany 12 1.4k 0.7× 760 0.6× 402 0.5× 396 0.9× 110 0.8× 19 2.2k
Hugo Y. K. Lam United States 23 1.7k 0.9× 927 0.8× 654 0.8× 499 1.1× 136 1.0× 32 2.4k
Adrian M. Stütz Germany 23 1.9k 1.0× 1.1k 0.9× 678 0.8× 659 1.4× 158 1.1× 44 3.3k
Andreas Schlattl Austria 10 1.2k 0.7× 757 0.6× 393 0.5× 359 0.8× 141 1.0× 16 1.8k
Michael Lawrence United States 10 2.9k 1.6× 812 0.7× 750 0.9× 654 1.4× 119 0.9× 24 4.3k
Tatiana Borodina Germany 14 2.5k 1.4× 539 0.4× 687 0.8× 431 0.9× 80 0.6× 25 3.3k
Colby Chiang United States 13 1.3k 0.7× 1.3k 1.0× 379 0.5× 489 1.1× 88 0.6× 16 2.2k
Semyon Kruglyak United States 13 2.0k 1.1× 1.1k 0.9× 452 0.5× 437 0.9× 208 1.5× 20 3.1k
Donna Karolchik United States 17 4.3k 2.4× 1.3k 1.0× 945 1.1× 693 1.5× 96 0.7× 21 5.3k
Fereydoun Hormozdiari United States 23 2.2k 1.2× 1.4k 1.2× 468 0.6× 712 1.5× 111 0.8× 42 3.1k

Countries citing papers authored by Ryan M. Layer

Since Specialization
Citations

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

Fields of papers citing papers by Ryan M. Layer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan M. Layer. 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 Ryan M. Layer. The network helps show where Ryan M. Layer may publish in the future.

Co-authorship network of co-authors of Ryan M. Layer

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan M. Layer. A scholar is included among the top collaborators of Ryan M. Layer 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 Ryan M. Layer. Ryan M. Layer 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.
Layer, Ryan M., Douglas G. Scofield, Alexander Hayward, et al.. (2024). Calling Structural Variants with Confidence from Short-Read Data in Wild Bird Populations. Genome Biology and Evolution. 16(4). 3 indexed citations
3.
Dupuis, Josée, et al.. (2022). RAREsim: A simulation method for very rare genetic variants. The American Journal of Human Genetics. 109(4). 680–691.
4.
Pedersen, Brent S., et al.. (2022). Searching thousands of genomes to classify somatic and novel structural variants using STIX. Nature Methods. 19(4). 445–448. 6 indexed citations
5.
Belyeu, Jonathan R., et al.. (2021). Samplot: a platform for structural variant visual validation and automated filtering. Genome biology. 22(1). 161–161. 53 indexed citations
6.
Carleton, Julia B., et al.. (2020). Regulatory sharing between estrogen receptor α bound enhancers. Nucleic Acids Research. 48(12). 6597–6610. 6 indexed citations
7.
Abel, Haley, David E. Larson, Allison Regier, et al.. (2020). Mapping and characterization of structural variation in 17,795 human genomes. Nature. 583(7814). 83–89. 148 indexed citations
8.
Larson, David E., Haley Abel, Colby Chiang, et al.. (2019). svtools: population-scale analysis of structural variation. Bioinformatics. 35(22). 4782–4787. 33 indexed citations
9.
Belyeu, Jonathan R., T. Nicholas, Brent S. Pedersen, et al.. (2018). SV-plaudit: A cloud-based framework for manually curating thousands of structural variants. GigaScience. 7(7). 25 indexed citations
10.
Ostrander, Betsy, Russell J. Butterfield, Brent S. Pedersen, et al.. (2018). Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy. npj Genomic Medicine. 3(1). 22–22. 54 indexed citations
11.
Pedersen, Brent S., Ryan M. Layer, & Aaron R. Quinlan. (2016). Vcfanno: fast, flexible annotation of genetic variants. Genome biology. 17(1). 118–118. 92 indexed citations
12.
Layer, Ryan M. & Aaron R. Quinlan. (2015). A Parallel Algorithm for <formula><tex>$N$</tex></formula>-Way Interval Set Intersection. Proceedings of the IEEE. 105(3). 1–10. 4 indexed citations
13.
Chiang, Colby, Ryan M. Layer, Gregory G. Faust, et al.. (2015). SpeedSeq: ultra-fast personal genome analysis and interpretation. Nature Methods. 12(10). 966–968. 333 indexed citations
14.
Layer, Ryan M., et al.. (2015). Efficient genotype compression and analysis of large genetic-variation data sets. Nature Methods. 13(1). 63–65. 42 indexed citations
15.
Mueller, Adam C., Bijan K. Dey, Ryan M. Layer, et al.. (2014). MUNC, a Long Noncoding RNA That Facilitates the Function of MyoD in Skeletal Myogenesis. Molecular and Cellular Biology. 35(3). 498–513. 114 indexed citations
16.
Malhotra, Ankit, Michael Lindberg, Gregory G. Faust, et al.. (2013). Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms. Genome Research. 23(5). 762–776. 124 indexed citations
17.
Sun, Dongxiao, Ryan M. Layer, Adam C. Mueller, et al.. (2013). Regulation of several androgen-induced genes through the repression of the miR-99a/let-7c/miR-125b-2 miRNA cluster in prostate cancer cells. Oncogene. 33(11). 1448–1457. 82 indexed citations
18.
Shibata, Yoshiyuki, Pankaj Kumar, Ryan M. Layer, et al.. (2012). Extrachromosomal MicroDNAs and Chromosomal Microdeletions in Normal Tissues. Science. 336(6077). 82–86. 221 indexed citations
19.
Gagan, Jeffrey, Bijan K. Dey, Ryan M. Layer, Zhen Yan, & Anindya Dutta. (2012). Notch3 and Mef2c Proteins Are Mutually Antagonistic via Mkp1 Protein and miR-1/206 MicroRNAs in Differentiating Myoblasts. Journal of Biological Chemistry. 287(48). 40360–40370. 75 indexed citations
20.
Gagan, Jeffrey, Bijan K. Dey, Ryan M. Layer, Zhen Yan, & Anindya Dutta. (2011). MicroRNA-378 Targets the Myogenic Repressor MyoR during Myoblast Differentiation. Journal of Biological Chemistry. 286(22). 19431–19438. 143 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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