Ali Mortazavi

Assistant Professor, Developmental & Cell Biology
School of Biological Sciences

Ph.D., California Institute of Technology, 2008, Biology

M.S., California State University, Los Angeles, 2004, Chemistry (Biochemistry)

B.S., California Institute of Technology, 1993, Engineering and Applied Science (Computer Science)

Phone: (949) 824-6762

University of California, Irvine
2218 Biological Sciences III
Mail Code: 2300
Irvine, CA 92697

picture of Ali  Mortazavi

Transcriptional Regulation, Genomics, Long-range chromatin interactions, ChIP-seq, RNA-seq
URL Lab website
2004 CSULA Chemistry and Biochemistry Graduate Student Award
2008 Caltech Lawrence L. and Audrey W. Ferguson Prize for outstanding doctoral thesis in biology
Appointments 2008 – 2011 Gordon and Betty Moore Cell Center Postdoctoral Fellowship, Caltech
2009 – 2011 Beckman Institute Postdoctoral Fellowship, Caltech
Applications of genomics, computation, and sequencing technologies to the analysis of transcriptional regulation in development.

My laboratory explores how gene regulatory networks that underlie development are encoded in the human genome and we seek to understand their “grammar” and dynamics using both laboratory and computational methods. We focus on how tissue-specific transcriptional enhancers work, and whether their expression levels and target genes can be predicted from in vivo protein-DNA interactions (as measured using ChIP-seq), open chromatin (from DNase-seq) and RNA expression (from RNA-seq) of both normal and perturbed differentiation time courses. No reliable computational methods yet exist to link distal enhancers to their target genes (which can be many genes and up to a megabase-pair away in mammals) using sequence, expression or ChIP-data alone. While this identification problem is extremely difficult when considering large and complex genomes linearly, it would be a relatively easy problem if we could capture the three-dimensional interaction of the looping enhancers with their target promoters. We are, therefore, working with techniques that capture long-range chromatin interactions globally, such as ChIA-PET, in developmental time courses in order to integrate these long-range data with ChIP-seq, DNase-seq, and RNA-seq into testable gene regulatory networks. We are also interested in what fraction of these long-range interactions account for phenotypic variation between individuals and whether they are more likely to be conserved in vertebrates.
Publications Zhang JA, Mortazavi A, Williams BA, Wold BJ, Rothenberg EV (2012) Dynamic transformations of epigenetic marking and genome-wide transcriptional regulation establish T cell identity. In press.
  The ENCODE Project Consortium. (2011). A user's guide to the Encyclopedia of DNA Elements (ENCODE). PLoS Biol. 9: e1001046
  Trapnell C, Williams BA, Porta G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter B. (2010) Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotech 28, 511-515.
  Mortazavi A*, Schwarz EM*, Williams B, Schaeffer L, Antoshechkin I, Wold BJ, Sternberg PW. (2010) Scaffolding a de novo nematode genome with RNA-seq. Genome Research. 20, 1740-1747.
  Pepke S, Wold B, Mortazavi A. (2009). Computation for ChIP-seq and RNA-seq studies. Nature Methods. 6: S22-32
  Mortazavi A*, Williams BA*, McCue K, Schaeffer L, Wold B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods. 5: 621-628.
  Berghella L, De Angelis L, De Buysscher T, Mortazavi A, Biressi S, Forcales SV, Sirabella D, Cossu G, Wold BJ. (2008) A highly conserved molecular switch binds MSY-3 to regulate myogenin repression in postnatal muscle. Genes and Development. 22, 2125-2138.
  *Johnson, D. S., *Mortazavi, A., Myers, R. M. and Wold, B. (2007). Genome-wide mapping of in vivo protein DNA interactions. Science. 316: 1497-1502. (*co-first authors).
  Mortazavi A, Leeper Thompson EC, Garcia ST, Myers RM, Wold B. (2006). Comparative genomics modeling of the NRSF/REST repressor network: from single conserved sites to genome-wide repertoire. Genome Research. 16: 1208-1221
  Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE. (2006). Mining gene expression data by interpreting principal components. BMC Bioinformatics. 7, 194.
Other Experience Associate Editor
BMC Bioinformatics 2010

Graduate Programs Cellular and Molecular Biosciences

Mathematical and Computational Biology

Research Centers Center for Complex Biological Systems
Institute for Genomics and Bioinformatics
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Last updated 02/20/2015