Tomás Gomes

6.7k total citations · 1 hit paper
18 papers, 2.3k citations indexed

About

Tomás Gomes is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Tomás Gomes has authored 18 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 11 papers in Immunology and 2 papers in Genetics. Recurrent topics in Tomás Gomes's work include T-cell and B-cell Immunology (8 papers), Immune Cell Function and Interaction (8 papers) and Single-cell and spatial transcriptomics (7 papers). Tomás Gomes is often cited by papers focused on T-cell and B-cell Immunology (8 papers), Immune Cell Function and Interaction (8 papers) and Single-cell and spatial transcriptomics (7 papers). Tomás Gomes collaborates with scholars based in United Kingdom, Portugal and Finland. Tomás Gomes's co-authors include Maria Carmo‐Fonseca, Takayuki Nojima, Nicholas Proudfoot, Samantha J. Riesenfeld, Aaron T. L. Lun, Tallulah Andrews, John C. Marioni, Sarah A. Teichmann, Ana Rita Grosso and Hiroshi Kimurâ and has published in prestigious journals such as Science, Cell and Nature Communications.

In The Last Decade

Tomás Gomes

18 papers receiving 2.3k citations

Hit Papers

EmptyDrops: distinguishing cells from empty droplets in d... 2019 2026 2021 2023 2019 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomás Gomes United Kingdom 16 1.5k 752 363 254 146 18 2.3k
Hannah W. Miller United States 10 1.1k 0.7× 673 0.9× 304 0.8× 231 0.9× 140 1.0× 11 1.9k
Chloe K. Slichter United States 10 1.1k 0.7× 735 1.0× 307 0.8× 222 0.9× 118 0.8× 10 2.0k
Diya Das United States 6 1.1k 0.8× 499 0.7× 257 0.7× 228 0.9× 80 0.5× 7 1.8k
Quin F. Wills United Kingdom 11 1.4k 0.9× 501 0.7× 470 1.3× 293 1.2× 278 1.9× 18 2.1k
Marcin P. Mycko Poland 24 908 0.6× 801 1.1× 521 1.4× 204 0.8× 84 0.6× 52 1.9k
Christopher S. McGinnis United States 11 1.7k 1.1× 809 1.1× 420 1.2× 403 1.6× 179 1.2× 12 2.7k
Alireza Khodadadi‐Jamayran United States 25 975 0.6× 490 0.7× 267 0.7× 325 1.3× 97 0.7× 56 1.7k
Tilman Borggrefe Germany 31 2.5k 1.7× 464 0.6× 388 1.1× 347 1.4× 255 1.7× 63 3.3k
Chia‐Lin Wei Singapore 15 2.6k 1.7× 652 0.9× 629 1.7× 151 0.6× 310 2.1× 18 3.2k
José Javier García‐Ramírez Spain 19 1.1k 0.8× 293 0.4× 520 1.4× 308 1.2× 171 1.2× 33 1.7k

Countries citing papers authored by Tomás Gomes

Since Specialization
Citations

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

Fields of papers citing papers by Tomás Gomes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomás Gomes

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

All Works

18 of 18 papers shown
1.
Friedman, Sivan, Aleksandra A. Kolodziejczyk, Kyung‐Mee Moon, et al.. (2025). Single-cell and Spatial Transcriptomics Illuminate Bat Immunity and Barrier Tissue Evolution. Molecular Biology and Evolution. 42(2). 5 indexed citations
2.
Brazovskaja, Agnieska, Tomás Gomes, René Holtackers, et al.. (2024). Cell atlas of the regenerating human liver after portal vein embolization. Nature Communications. 15(1). 5827–5827. 5 indexed citations
3.
Lust, Katharina, Ashley Maynard, Tomás Gomes, et al.. (2022). Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration. Science. 377(6610). eabp9262–eabp9262. 47 indexed citations
4.
Kumar, Saumya, Válter R. Fonseca, Filipa Ribeiro, et al.. (2021). Developmental bifurcation of human T follicular regulatory cells. Science Immunology. 6(59). 28 indexed citations
5.
Elmentaite, Rasa, Alexander Ross, Kenny Roberts, et al.. (2020). Single-Cell Sequencing of Developing Human Gut Reveals Transcriptional Links to Childhood Crohn’s Disease. Developmental Cell. 55(6). 771–783.e5. 165 indexed citations
6.
Ricco, Martina Lubrano di, Émilie Ronin, Sylvie Grégoire, et al.. (2020). Tumor necrosis factor receptor family costimulation increases regulatory T‐cell activation and function via NF‐κB. European Journal of Immunology. 50(7). 972–985. 64 indexed citations
7.
Chen, Yi‐Ling, Tomás Gomes, Clare S. Hardman, et al.. (2019). Re-evaluation of human BDCA-2+ DC during acute sterile skin inflammation. The Journal of Experimental Medicine. 217(3). 34 indexed citations
8.
Gomes, Tomás, Sarah A. Teichmann, & Carlos Talavera‐López. (2019). Immunology Driven by Large-Scale Single-Cell Sequencing. Trends in Immunology. 40(11). 1011–1021. 54 indexed citations
9.
Lun, Aaron T. L., et al.. (2019). EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome biology. 20(1). 63–63. 501 indexed citations breakdown →
10.
Henriksson, Johan, Xi Chen, Tomás Gomes, et al.. (2019). Genome-wide CRISPR Screens in T Helper Cells Reveal Pervasive Crosstalk between Activation and Differentiation. Cell. 176(4). 882–896.e18. 125 indexed citations
11.
Miragaia, Ricardo J., Tomás Gomes, Agnieszka Chomka, et al.. (2019). Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation. Immunity. 50(2). 493–504.e7. 322 indexed citations
12.
Miragaia, Ricardo J., Xiuwei Zhang, Tomás Gomes, et al.. (2018). Single-cell RNA-sequencing resolves self-antigen expression during mTEC development. Scientific Reports. 8(1). 685–685. 30 indexed citations
13.
Kunz, Daniel J., Tomás Gomes, & Kylie R. James. (2018). Immune Cell Dynamics Unfolded by Single-Cell Technologies. Frontiers in Immunology. 9. 1435–1435. 31 indexed citations
14.
Pramanik, Jhuma, Xi Chen, Gozde Kar, et al.. (2018). Genome-wide analyses reveal the IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation. Genome Medicine. 10(1). 76–76. 60 indexed citations
15.
Nojima, Takayuki, et al.. (2018). RNA Polymerase II Phosphorylated on CTD Serine 5 Interacts with the Spliceosome during Co-transcriptional Splicing. Molecular Cell. 72(2). 369–379.e4. 104 indexed citations
16.
Schlackow, Margarita, Takayuki Nojima, Tomás Gomes, et al.. (2016). Distinctive Patterns of Transcription and RNA Processing for Human lincRNAs. Molecular Cell. 65(1). 25–38. 208 indexed citations
17.
Nojima, Takayuki, Tomás Gomes, Maria Carmo‐Fonseca, & Nicholas Proudfoot. (2016). Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide. Nature Protocols. 11(3). 413–428. 79 indexed citations
18.
Nojima, Takayuki, Tomás Gomes, Ana Rita Grosso, et al.. (2015). Mammalian NET-Seq Reveals Genome-wide Nascent Transcription Coupled to RNA Processing. Cell. 161(3). 526–540. 400 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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