Natalie Davidson, MSc. UCLA Computer Science

PhD Student

E-Mail
natalie.davidson@get-your-addresses-elsewhere.inf.ethz.ch
Phone
+41 43 254 0224
Address
Biomedical Informatics Group
Schmelzbergstrasse 26
8006 Zürich
Room
SHM 26 B 3

I am interested in extending statistical models to gain deeper understanding of transcriptional dysregulation in cancer.

I am interested in understanding the role transcriptional dysregulation plays in cancer. Through my collaborations during my Ph.D., I have been able to conduct single cancer and pan-cancer analyses using RNA-Seq and Ribosome Profiling data. I typically use generalized linear models and generalized linear mixed models, but I am interested in extending to other models as the numbers of samples in cancer analyses grow. I am currently a part of the International Cancer Genome Consortium, where I integrate multiple transcriptional aberrations such as splicing, fusions, over/under expression, allele specific expression, and others in over 1,000 samples to identify cancer relevant genes and alteration patterns. I received my B.Sc. in computer science and minor in mathematics from University of California, Santa Barbara.  I received my M.Sc. in computer science from University of California, Los Angeles under the advisement of Dr. Jason Ernst. I am currently obtaining my Ph.D from Tri-Institutional Program for Computational Biology and Medicine in New York, while conducting research at ETH Zürich.

Authors Natalie R. Davidson, ; PanCancer Analysis of Whole Genomes 3 (PCAWG-3) for ICGC, Alvis Brazma, Angela N. Brooks, Claudia Calabrese, Nuno A. Fonseca, Jonathan Goke, Yao He, Xueda Hu, Andre Kahles, Kjong-Van Lehmann, Fenglin Liu, Gunnar Rätsch, Siliang Li, Roland F. Schwarz, Mingyu Yang, Zemin Zhang, Fan Zhang and Liangtao Zheng

Submitted Proceedings of the American Association for Cancer Research Annual Meeting 2017

Link DOI

Abstract Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene-trait relationships and their use for future rice improvement.

Authors Kenneth L McNally, Kevin L Childs, Regina Bohnert, Rebecca M Davidson, Keyan Zhao, Victor J Ulat, Georg Zeller, Richard M Clark, Douglas R Hoen, Thomas E Bureau, Renee Stokowski, Dennis G Ballinger, Kelly A Frazer, David R Cox, Badri Padhukasahasram, Carlos D Bustamante, Detlef Weigel, David J Mackill, Richard M Bruskiewich, Gunnar Rätsch, C Robin Buell, Hei Leung, Jan E Leach

Submitted Proceedings of the National Academy of Sciences of the United States of America

Link Pubmed DOI