March 5th, Julia Salzman: Circular RNA is expressed across 1 billion years of evolution

About Julia Julia Salzman-250

Julia Salzman is an Assistant Professor of Biochemistry at the Stanford University School of Medicine and Associate Member of the Stanford Cancer Institute. She received an A. B. in Mathematics magna cum laude from Princeton University and her Ph. D. in Statistics from Stanford University. Dr. Salzman spent one year on the Faculty in the Department of Statistics at Columbia University before returning to Stanford as a Postdoctoral research fellow in the laboratory of Dr. Patrick O. Brown and subsequently joining the faculty at Stanford. She has published broadly in fields including quantum information theory, statistical methodology,
computational biology and genetics. Her most significant contributions have been to show that circular RNA is a previously overlooked but ubiquitous component of eukaryotic gene expression programs. Dr. Salzman’s work has been funded by grants from the Division of Mathematical Sciences at the NSF and a K99/R00 award from the NCI. She is a 2014 Alfred P. Sloan Fellow.

The Salzman lab combines biochemical, genetic, algorithmic and statistical approaches to study RNA expression. Our goal is to use high throughput experimental and statistical tools to construct a high dimensional picture of gene regulation, including cis and trans control of the full repertoire of RNAs expressed by cells. Currently, we are focusing on the function and biogenesis of circular RNA, which we recently discovered to be a ubiquitous and uncharacterized component of eukaryotic gene expression. A second major focus is gene expression variation in human cancer. Here, we combine mining massive public datasets, and experimental study of primary tumors and cell lines with bioinformatic and statistical methods. We use the cancer genome as window into functional roles played by RNA, and are attempting to characterize potential biomarkers.

Talk: Circular RNA is expressed across 1 billion years of evolution

Until recently, circular RNA isoforms expressed from protein coding loci have largely gone unnoticed. Yet, these topologically circular molecules are expressed from a large fraction of human, mouse and fly genes. Since our initial report of widespread RNA circles in humans and mouse, constituting the dominant isoform in hundreds of genes, abundant circular RNAs have been reported in zebrafish, C. elegans and fruit flies; and other groups have confirmed our findings in human and mouse cells. Recently, we have discovered that circular RNAs are expressed in diverse species whose most recent common ancestor existed more than one billion years ago including fungi, a plant and protists. Some of these species have very short introns (~100 nucleotides or shorter) and few documented examples of exon skipping, yet they still produce circular RNAs, making it unlikely that all circular RNAs are by-products of alternative splicing or “piggyback” on signals used in alternative RNA processing. Furthermore, these results indicate that circular RNA may be an ancient, conserved feature of eukaryotic gene expression programs.

Wednesday March 5th, 2014
1:00 PM Lunch
1:15 PM Seminar
Location: Clark Center S360


February 26th, Eilon Sharon: Unraveling gene promoter and 3’ end effects on expression strength and noise using many designed sequences

Eilon Sharon is a postdoc in the labs of Jonathan Pritchard and  Hunter Fraser.

Eilon Sharon is a postdoc in the labs of Jonathan Pritchard and Hunter Fraser.

About Eilon

“I completed a PhD followed by a one year postdoctoral position in computational biology at the Weizmann Institute of Science located in Rehovot, Israel, working under the supervision of Prof. Eran Segal in the Departments of Computer Science and Applied Mathematics and Molecular Cell Biology. My PhD studies have focused mainly on developing computational methods and devising experimental methods, which use synthetic biology to decipher how transcription regulation is encoded in the yeast genome. I hold a double major BSc in biology (summa cum laude) and Computer Science (magna cum laude), and also spent two and half years working for Rosetta Genomics as an algorithms developer, where my team found over 100 novel miRNA in human.

During my PhD I developed a technology that accurately measures the induced transcription of thousands of fully designed promoters in a single experiment (Nature Biotechnology 2012). By combining several technologies (Oligo synthetic libraries, fluorescent reporter assay, fluorescence-activated cell sorting and deep sequencing) my method provides a ~1000-fold increase in the scale with which the effect of a fully designed sequence on expression can be studied. The results analysis produced several insights into the principles of transcriptional regulation .Due to its adaptable nature is currently applied to study a broad range of mappings between genotype and diverse biological phenotypes in Eran Segal lab.

In two additional projects I showed how yeast ribosomal protein use transcriptional regulation to compensate for differences in their gene copy number by accurately measuring their promoters derived expression and modeling their regulatory mechanism (Genome Research 2011); and developed a novel probabilistic method (based on Markov networks) to infer and model TF binding specificities from experimental results, while capturing inter-dependencies between binding positions (PLoS Computational Biology 2008).

On Feb. 1st 2014 I have started a postdoc at the labs of Prof. Jonathan Pritchard and Prof. Hunter Fraser.”

Talk: Unraveling gene promoter and 3’ end effects on expression strength and noise using many designed sequences

Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited. We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed regulatory sequences. We first applied it to measure the effect on expression level of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences in promoters. The results analysis revealed a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts. We then applied our method to study promoter effect on noise in gene expression and found that noise levels of promoters with similar mean expression levels can vary over two orders of magnitude. Our results suggests that the effect of promoters on noise is partly mediated by the combination of nonspecific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Lastly we adopted our method for studying the effect of gene 3’ end sequence on expression and found that the main mechanism by which 3’ end sequences affect expression is mRNA 3’ end processing efficiency and that it is encoded by a single element in yeast gene 3’ end sequences. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control and is now being adopted to other organisms.

Seminar details

Wednesday Feb 26, 2014
1:00 PM Lunch (please sign up here)
1:15 PM Seminar
Location: Clark Center S360
Host: Jonathan Pritchard

February 19th, Olga Sazonova: Functional genomics of vascular smooth muscle cell differentiation

Olga Sazonova

Olga Sazonova

Olga is a post doc in the labs of Stephen Montgomery and Tom Quertermous.

Functional genomics of vascular smooth muscle cell differentiation

Coronary heart disease (CHD) and other complex human pathologies are products of genetic and environmental factors whose interactions are poorly understood. Genome-wide association studies (GWAS) demonstrate that most disease-associated genetic variants modulate the expression profile of a given gene, not the structure of its protein product. Thus, precise identification of regulatory SNPs and the environment-specific mechanisms of their function is critical for developing novel therapeutics in the post-genomic era. To this end, we have developed a novel computational method to detect gene-environment (GxE) interactions from RNA-Seq data by mapping differential allele-specific expression (dASE) in response to an environmental stimulus. We applied this method to detect dASE in vascular smooth muscle cells (VSMCs) exposed to a healthy or disease-like environment and discovered 72 genes (5% FDR) exhibiting dASE as a function of serum stimulation. Only 28 of these 72 genes were shown to exhibit differential expression (dE), illustrating the power of rASE to reveal environment-responsive transcriptional regulation not captured by conventional differential expression analysis. Further, we found enrichment of genes associated with coronary heart disease by GWAS among dASE genes but not dE genes, and this result further suggests that dASE mapping can reveal novel mechanistic insights about the identify and function of causal variants implicated in disease risk. Our pipeline can be applied to any paired case-control RNA-Seq data set to discover the presence of environment-sensitive regulatory variants and offers a novel and powerful avenue to study GxE interactions in complex human disease.

Seminar details

Wednesday Feb 19, 2014
1:00 PM Lunch
1:15 PM Seminar
Location: Clark Center S360
Host: Stephen Montgomery

Feb 12th, Dennis Wall: Decoding autisms using machine intelligence and systems medicine

About Dennis


Professor Dennis P. Wall

Dennis Wall is an Associate Professor of Pediatrics at the Stanford University School of Medicine.

The Wall Lab uses machine learning and systems biology to develop clinical solutions for the detection and treatment of autism and other complex human diseases. The lab’s research falls into three categories three general categories: (1) Translating the thinking of systems biology to the field of autism genetics with the intent to develop effective early-stage diagnostics and targets for therapeutic intervention. The work involves the generation and analysis of genomic and phenotypic databases using computational tools of systems biology, machine learning and network inference.(2) Efforts to understand and characterize the clinical significance and utility of human genetic variation. This work involves clinical-grade annotation of human genetic variation, estimating the rates of both true and false positives in present day genetic testing and their likely impacts on the practice of personalized care, the construction of an authoritative knowledgebase for clinical decision support, and efforts in educating present and future doctors on the potentials of genomics in individualized healthcare.(3) Redefining human diseases through computational and comparative network analysis. The work involves the integration and analysis of transcriptomic, genomic and bibliomic data to network all known human diseases. Deliverables include revealing disease connections, properly reshaping blurred boundaries of classification, and opportunities for drug treatment repositioning.

Dr. Wall received his doctorate in Integrative Biology from the University of California, Berkeley, where he pioneered the use of fast evolving gene sequences to trace population-scale diversification across islands. Then, with a postdoctoral fellowship award from the National Science Foundation, he went on to Stanford University to address broader questions in systems biology and computational genomics, work that resulted in comprehensive functional models for both protein mutation and protein interaction.


The incidence of autism has increased dramatically over recent years, making this mental disorder one of the greatest public health challenges of our time. It has a strong genetic component, but molecular pathology remains unclear despite deep sequencing efforts. Thus, the dominant methods for diagnosis rely on behavioral characteristics, however these take hours to administer and often do not reach children until they have aged past key windows of development. In this talk, I will describe recent efforts in my lab to discover both genetic and behavioral markers that enable rapid, early and accurate detection of autism. For the former, I will describe how we have compared the network of autism gene candidates to the complete genetic systems of behaviorally related disorders including ADHD to target novel gene candidates and improve our understanding of the genetic system of autism, and how this work has identified a potentially important role for the immune system. For the latter, I will describe how we have used machine-learning techniques to study over 5,000 autism cases and some of the most commonly used behavioral instruments for autism detection to quicken and mobilize the detection of the core features of autism. Deploying an alternative decision tree-learning algorithm, we identified a procedure that could reduce the total complexity by 93% without loss of accuracy and that can be administered out of the clinic and via mobile media. Such an abbreviated diagnostic instrument could have significant impact on the timeframe of diagnosis, making it possible for more children to receive diagnosis and care early in their development.

Seminar details

Wednesday Feb 12, 2014
12:45 PM Lunch
1:15 PM Seminar
Location: Clark Center S360
Host: Dmitri Petrov