Steven M. Blue

7.3k total citations · 1 hit paper
17 papers, 1.7k citations indexed

About

Steven M. Blue is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cancer Research. According to data from OpenAlex, Steven M. Blue has authored 17 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 2 papers in Cellular and Molecular Neuroscience and 2 papers in Cancer Research. Recurrent topics in Steven M. Blue's work include RNA Research and Splicing (16 papers), RNA modifications and cancer (13 papers) and RNA and protein synthesis mechanisms (12 papers). Steven M. Blue is often cited by papers focused on RNA Research and Splicing (16 papers), RNA modifications and cancer (13 papers) and RNA and protein synthesis mechanisms (12 papers). Steven M. Blue collaborates with scholars based in United States, Singapore and Canada. Steven M. Blue's co-authors include G Yeo, Eric L. Van Nostrand, Gabriel A. Pratt, Chelsea Gelboin-Burkhart, Thai B. Nguyen, Balaji Sundararaman, Rebecca Stanton, Keri Elkins, Christine Surka and Mitchell Guttman and has published in prestigious journals such as Cell, Nucleic Acids Research and Nature Communications.

In The Last Decade

Steven M. Blue

17 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.

Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP)

2016 863 citations Eric L. Van Nostrand, Gabriel A. Pratt et al. Nature Methods profile →

Peers

Steven M. Blue

CMN

GENET

Dinghai Zheng United States

Timothy Sterne-Weiler United States

Yoichiro Sugimoto United Kingdom

Marina M. Scotti United States

Federico Pelisch Argentina

Amy Heidersbach United States

Martyna Olga Urbanek-Trzeciak Poland

Marta M. Fay United States

Konstantina Skourti-Stathaki United Kingdom

Paulina Galka-Marciniak Poland

Dinghai Zheng United States

Steven M. Blue

2.0k ×1.2

336 ×0.9

41 ×0.5

CMN

56 ×0.7

GENET

72 ×1.3

GENET

Citations per year, relative to Steven M. Blue Steven M. Blue (= 1×) peers Dinghai Zheng

Countries citing papers authored by Steven M. Blue

Since Specialization

Citations

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

Fields of papers citing papers by Steven M. Blue

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Steven M. Blue

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

All Works

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17 of 17 papers shown

Kofman, Eric, Evan A. Boyle, Tao Yu, et al.. (2024). Expanded palette of RNA base editors for comprehensive RBP-RNA interactome studies. Nature Communications. 15(1). 875–875. 11 indexed citations

Lin, Lin, Jingrong Zhao, Naoto Kubota, et al.. (2024). Epistatic interactions between NMD and TRP53 control progenitor cell maintenance and brain size. Neuron. 112(13). 2157–2176.e12. 6 indexed citations

Kathman, Stefan G., Seong Joo Koo, Hsuan-Lin Her, et al.. (2023). Remodeling oncogenic transcriptomes by small molecules targeting NONO. Nature Chemical Biology. 19(7). 825–836. 49 indexed citations

Arif, Waqar, Ullas V. Chembazhi, Qinyu Hao, et al.. (2023). Splicing factor SRSF1 deficiency in the liver triggers NASH-like pathology and cell death. Nature Communications. 14(1). 551–551. 32 indexed citations

Bouvrette, Louis Philip Benoit, Xiaofeng Wang, Jonathan Boulais, et al.. (2022). RBP Image Database: A resource for the systematic characterization of the subcellular distribution properties of human RNA binding proteins. Nucleic Acids Research. 51(D1). D1549–D1557. 2 indexed citations

Blue, Steven M., Brian A. Yee, Gabriel A. Pratt, et al.. (2022). Transcriptome-wide identification of RNA-binding protein binding sites using seCLIP-seq. Nature Protocols. 17(5). 1223–1265. 39 indexed citations

Jourdain, Alexis A., Bridget E. Begg, Eran Mick, et al.. (2021). Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS. Molecular Cell. 81(9). 1905–1919.e12. 56 indexed citations

Corley, Meredith, Ryan A. Flynn, Steven M. Blue, et al.. (2021). fSHAPE, fSHAPE-eCLIP, and SHAPE-eCLIP probe transcript regions that interact with specific proteins. STAR Protocols. 2(3). 100762–100762. 1 indexed citations

Batra, Ranjan, David A. Nelles, Daniela Roth, et al.. (2020). The sustained expression of Cas9 targeting toxic RNAs reverses disease phenotypes in mouse models of myotonic dystrophy type 1. Nature Biomedical Engineering. 5(2). 157–168. 43 indexed citations

10.

Bajaj, Jeevisha, Michael Hamilton, Yutaka Shima, et al.. (2020). An in vivo genome-wide CRISPR screen identifies the RNA-binding protein Staufen2 as a key regulator of myeloid leukemia. Nature Cancer. 1(4). 410–422. 37 indexed citations

11.

Nostrand, Eric L. Van, Gabriel A. Pratt, Brian A. Yee, et al.. (2020). Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins. Genome biology. 21(1). 90–90. 129 indexed citations

12.

Corley, Meredith, Ryan A. Flynn, Byron Lee, et al.. (2020). Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces. Molecular Cell. 80(5). 903–914.e8. 24 indexed citations

13.

Batra, Ranjan, David A. Nelles, Elaine Pirie, et al.. (2017). Elimination of Toxic Microsatellite Repeat Expansion RNA by RNA-Targeting Cas9. Cell. 170(5). 899–912.e10. 191 indexed citations

14.

Nostrand, Eric L. Van, Thai B. Nguyen, Chelsea Gelboin-Burkhart, et al.. (2017). Robust, Cost-Effective Profiling of RNA Binding Protein Targets with Single-end Enhanced Crosslinking and Immunoprecipitation (seCLIP). Methods in molecular biology. 1648. 177–200. 68 indexed citations

15.

Nostrand, Eric L. Van, Chelsea Gelboin-Burkhart, Ruth Wang, et al.. (2016). CRISPR/Cas9-mediated integration enables TAG-eCLIP of endogenously tagged RNA binding proteins. Methods. 118-119. 50–59. 27 indexed citations

16.

Sundararaman, Balaji, Lijun Zhan, Steven M. Blue, et al.. (2016). Resources for the Comprehensive Discovery of Functional RNA Elements. Molecular Cell. 61(6). 903–913. 93 indexed citations

17.

Nostrand, Eric L. Van, Gabriel A. Pratt, A.A. Shishkin, et al.. (2016). Robust transcriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP). Nature Methods. 13(6). 508–514. 863 indexed citations breakdown →

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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