Assistant Professor, UC Department of Biomedical Informatics
Dr. Salomonis and his group are on the cutting edge of developing new software and algorithms to identify complex functional relationships from whole transcriptome data. They have developed several open source analysis tools including AltAnalyze, LineageProfiler, GO-Elite, and NetPerspective. The advent of single-cell genomic profiles has created many new opportunities for understanding stochastic decisions mediating stem cell differentiation to distinct cell fates and the regulation of distinct gene expression and splicing programs. They are capitalizing on this new technology to explore these decision-making processes at a resolution never previously possible.
Last year, they worked collaboratively with a dozen investigative research teams within Cincinnati Children's to develop new methods for evaluating whole genome transcriptome datasets. These methods include: 1) the detection of distinct gene and splicing populations from bulk and single cell genome profiles, 2) predicting implicated cell types present in complex fetal maternal biological samples and 3) identifying new disease regulatory networks related to pediatric and adult cancers, cardiovascular disease and spinal cord injury.
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
Biomedical Informatics, Fibrosis
Nathan Salomonis, PhD11/5/2019
Nathan Salomonis, PhD9/17/2019
Bruce J. Aronow, PhD, Peter S. White, PhD, Nathan Salomonis, PhD7/3/2019
BS: University of California, Los Angeles, CA, 1998.
PhD: University of California, San Francisco, CA, 2008.
Postdoctoral Fellow: Gladstone Institutes, San Francisco, CA, 2012.
Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity.
Harley, JB; Chen, X; Pujato, M; Miller, D; Maddox, A; Forney, C; Magnusen, AF; Lynch, A; Chetal, K; Yukawa, M; et al.
Cross-platform single cell analysis of kidney development shows stromal cells express Gdnf.
Magella, B; Adam, M; Potter, AS; Venkatasubramanian, M; Chetal, K; Hay, SB; Salomonis, N; Potter, SS.
Molecular Characterization of Pediatric Restrictive Cardiomyopathy from Integrative Genomics.
Rindler, TN; Hinton, RB; Salomonis, N; Ware, SM.
Transcriptomic and epigenomic differences in human induced pluripotent stem cells generated from six reprogramming methods.
Churko, JM; Lee, J; Ameen, M; Gu, M; Venkatasubramanian, M; Diecke, S; Sallam, K; Im, H; Wang, G; Gold, JD; et al.
Nature Biomedical Engineering.
Single-cell analysis of mixed-lineage states leading to a binary cell fate choice.
Olsson, A; Venkatasubramanian, M; Chaudhri, VK; Aronow, BJ; Salomonis, N; Singh, H; Grimes, HL.
Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium.
Salomonis, N; Dexheimer, PJ; Omberg, L; Schroll, R; Bush, S; Huo, J; Schriml, L; Sui, SH; Keddache, M; Mayhew, C; et al.
Stem Cell Reports.
Systems biology evaluation of cell-free amniotic fluid transcriptome of term and preterm infants to detect fetal maturity.
Kamath-Rayne, BD; Du, Y; Hughes, M; Wagner, EA; Muglia, LJ; DeFranco, EA; Whitsett, JA; Salomonis, N; Xu, Y.
BMC Medical Genomics.
The kSORT Assay to Detect Renal Transplant Patients at High Risk for Acute Rejection: Results of the Multicenter AART Study.
Roedder, S; Sigdel, T; Salomonis, N; Hsieh, S; Dai, H; Bestard, O; Metes, D; Zeevi, A; Gritsch, A; Cheeseman, J; et al.
Systems-level perspective of sudden infant death syndrome.
Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation.
Salomonis, N; Schlieve, CR; Pereira, L; Wahlquist, C; Colas, A; Zambon, AC; Vranizan, K; Spindler, MJ; Pico, AR; Cline, MS; et al.
Proceedings of the National Academy of Sciences of USA.
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