T. Brian

5.4k total citations · 1 hit paper
8 papers, 937 citations indexed

About

T. Brian is a scholar working on Molecular Biology, Cancer Research and Surgery. According to data from OpenAlex, T. Brian has authored 8 papers receiving a total of 937 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Cancer Research and 1 paper in Surgery. Recurrent topics in T. Brian's work include RNA Research and Splicing (4 papers), RNA modifications and cancer (3 papers) and RNA and protein synthesis mechanisms (3 papers). T. Brian is often cited by papers focused on RNA Research and Splicing (4 papers), RNA modifications and cancer (3 papers) and RNA and protein synthesis mechanisms (3 papers). T. Brian collaborates with scholars based in United States, United Kingdom and Sweden. T. Brian's co-authors include Howard Y. Chang, Paul A. Khavari, Ryan A. Flynn, Matthew G. Vander Heiden, Brian Zarnegar, Caroline A. Lewis, Ying Shen, Nicholas J. Matheson, Craig J. Thomas and Adi Naamati and has published in prestigious journals such as Genes & Development, Molecular Cell and Nature Cell Biology.

In The Last Decade

T. Brian

8 papers receiving 929 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.

Increased demand for NAD+ relative to ATP drives aerobic glycolysis

2020 351 citations Alba Luengo, Zhaoqi Li et al. Molecular Cell profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
T. Brian United States	7	726	317	64	54	51	8	937
Gianmarco Rinaldi Belgium	14	661 0.9×	374 1.2×	51 0.8×	91 1.7×	53 1.0×	19	964
Carolyn Sangokoya United States	6	419 0.6×	280 0.9×	56 0.9×	33 0.6×	40 0.8×	8	618
Xiphias Ge Zhu United States	7	516 0.7×	307 1.0×	77 1.2×	90 1.7×	60 1.2×	7	719
Guo Dai China	17	575 0.8×	336 1.1×	36 0.6×	124 2.3×	69 1.4×	34	932
Yang W. Zhang United States	11	981 1.4×	190 0.6×	76 1.2×	129 2.4×	62 1.2×	13	1.2k
Katrin Eckhardt Switzerland	9	505 0.7×	149 0.5×	63 1.0×	82 1.5×	52 1.0×	15	875
Vikash Reebye United Kingdom	16	549 0.8×	184 0.6×	48 0.8×	69 1.3×	86 1.7×	36	805
Rikki A. M. Brown Australia	10	530 0.7×	484 1.5×	23 0.4×	83 1.5×	93 1.8×	11	783
Ping Tang China	14	591 0.8×	218 0.7×	46 0.7×	159 2.9×	62 1.2×	25	862

Countries citing papers authored by T. Brian

Since Specialization

Citations

This map shows the geographic impact of T. Brian'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 T. Brian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Brian more than expected).

Fields of papers citing papers by T. Brian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of T. Brian

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

All Works

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8 of 8 papers shown

1.

Frazier, Jean A., T. Brian, Kun Wu, et al.. (2025). Histone chaperones coupled to DNA replication and transcription control divergent chromatin elements to maintain cell fate. Genes & Development. 39(9-10). 652–675. 1 indexed citations

2.

Diehl, Frances F., Teemu P. Miettinen, Christopher S. Nabel, et al.. (2022). Nucleotide imbalance decouples cell growth from cell proliferation. Nature Cell Biology. 24(8). 1252–1264. 91 indexed citations

3.

Schwartz, Andrew J., Joshua Goyert, Sumeet Solanki, et al.. (2021). Hepcidin sequesters iron to sustain nucleotide metabolism and mitochondrial function in colorectal cancer epithelial cells. Nature Metabolism. 3(7). 969–982. 79 indexed citations

4.

Luengo, Alba, Zhaoqi Li, Dan Y. Gui, et al.. (2020). Increased demand for NAD+ relative to ATP drives aerobic glycolysis. Molecular Cell. 81(4). 691–707.e6. 351 indexed citations breakdown →

5.

Zarnegar, Brian, Ryan A. Flynn, Ying Shen, et al.. (2016). irCLIP platform for efficient characterization of protein–RNA interactions. Nature Methods. 13(6). 489–492. 190 indexed citations

6.

Flynn, Ryan A., T. Brian, Adam J. Rubin, et al.. (2016). 7SK-BAF axis controls pervasive transcription at enhancers. Nature Structural & Molecular Biology. 23(3). 231–238. 86 indexed citations

7.

Flynn, Ryan A., Lance Martin, Robert C. Spitale, et al.. (2014). Dissecting noncoding and pathogen RNA–protein interactomes. RNA. 21(1). 135–143. 61 indexed citations

8.

Zheng, Grace X. Y., T. Brian, Dan E. Webster, Paul A. Khavari, & Howard Y. Chang. (2014). Dicer-microRNA-Myc circuit promotes transcription of hundreds of long noncoding RNAs. Nature Structural & Molecular Biology. 21(7). 585–590. 78 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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