A photo of Nathan Salomonis.

Nathan Salomonis, PhD

  • Associate Professor, UC Department of Pediatrics



I am a trained computational and experimental biologist who develops approaches to examine the interplay between diverse modes of gene regulation, including transcription, alternative splicing, genetics and epigenetics that underlie disease interaction networks.

I have co-authored more than 80 publications related to computational genomics and have more than 20 years’ experience directing the development of highly-used software applications for computational experts and biologists alike. My research group actively applies these and many other computational genomics techniques to diverse biological problems, including understanding the role of alternative splicing and neoantigen creation in cancer while defining lineage fate choices in differentiation and defining novel molecular mechanisms in human cardiovascular diseases.

We continue to develop new approaches for the integrative analysis of diverse molecular omics technologies including those for single-cell transcriptomics/epigenomics/ proteomics. We hope to ultimately apply insights from these computational approaches to develop new therapeutic strategies for difficult to treat pediatric cancers.

I have been fortunate to receive support from various internal awards, including:

  • Cincinnati Children’s Hospital Medical Center Endowed Scholar recognition (2019)
  • Academic and Research Committee (ARC) Award (2018)
  • Trustee Award and Procter Scholar (TAPS) Program Award (2017)

Our work has been published in numerous high impact journals, including Nature, Nature Genetics, Cancer Discovery, Nature Methods, Cell Stem Cell and Nucleic Acids Research.

BS: University of California, Los Angeles, CA, 1998.

PhD: University of California, San Francisco, CA, 2008.

Postdoctoral Fellow: Gladstone Institutes, San Francisco, CA, 2012.


Bioinformatics; genomics; cancer genomics; single-cell RNA-Seq analysis; alternative splicing; pathway analysis; pathway visualization; pathway curation; SIDS; stem cell biology; cardiac specification; renal graft dysfunction

Research Areas

Biomedical Informatics, Fibrosis



Decision level integration of unimodal and multimodal single cell data with scTriangulate. Li, G; Song, B; Singh, H; Surya Prasath, VB; Leighton Grimes, H; Salomonis, N. Nature Communications. 2023; 14:406.


Retinoid X receptor promotes hematopoietic stem cell fitness and quiescence and preserves hematopoietic homeostasis. Menéndez-Gutiérrez, MP; Porcuna, J; Nayak, R; Paredes, A; Niu, H; Núñez, V; Paranjpe, A; Gómez, MJ; Bhattacharjee, A; Schnell, DJ; et al. Blood. 2023; 141:592-608.


Erythroblastic islands foster granulopoiesis in parallel to terminal erythropoiesis. Romano, L; Seu, KG; Papoin, J; Muench, DE; Konstantinidis, D; Olsson, A; Schlum, K; Chetal, K; Chasis, JA; Mohandas, N; et al. Blood. 2022; 140:1621-1634.


CellDrift: inferring perturbation responses in temporally sampled single-cell data. Jin, K; Schnell, D; Li, G; Salomonis, N; Prasath, VB S; Szczesniak, R; Aronow, BJ. Briefings in Bioinformatics. 2022; 23:bbac324.


Bromodomain inhibition overcomes treatment resistance in distinct molecular subtypes of melanoma. Dar, AA; Bezrookove, V; Nosrati, M; Ice, R; Patino, JM; Vaquero, EM; Parrett, B; Leong, SP; Kim, KB; Debs, RJ; et al. Proceedings of the National Academy of Sciences of USA. 2022; 119:e2206824119.


A census of the lung: CellCards from LungMAP. Sun, X; Perl, AK; Li, R; Bell, SM; Sajti, E; Kalinichenko, VV; Kalin, TV; Misra, RS; Deshmukh, H; Clair, G; et al. Developmental Cell. 2022; 57:112-145.e2.


LungMAP Portal Ecosystem: Systems-Level Exploration of the Lung. Gaddis, N; Fortriede, J; Guo, M; Bardes, EE; Kouril, M; Tabar, S; Burns, K; Ardini-Poleske, ME; Loos, S; Schnell, D; et al. 2021; Preprint:2021.12.05.471312.


Gain-of-function cardiomyopathic mutations in RBM20 rewire splicing regulation and re-distribute ribonucleoprotein granules within processing bodies. Fenix, AM; Miyaoka, Y; Bertero, A; Blue, SM; Spindler, MJ; Tan, KK B; Perez-Bermejo, JA; Chan, AH; Mayerl, SJ; Nguyen, TD; et al. Nature Communications. 2021; 12:6324.


DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Li, G; Iyer, B; Prasath, VB S; Ni, Y; Salomonis, N. Briefings in Bioinformatics. 2021; 22:bbab160.