10/1       Lee, Tu
10/8       H. Li, Lange
10/15     Eskin, Liao
10/22     Zhou, Horvath
10/29     Yeates, Parker
11/5       Eisenberg, Suchard
11/12     Xiao, Lake
11/19     J. Distefano, Novembre
12/3       Pellegrini

Massively parallel sequencing, or so called ‘next gen’ sequencing is providing unprecedented insights into the genome permitting 1) whole genome and whole exome sequencing, economical large scale targeted sequencing for variant discovery, 2) CHIP_SEQ for full access to genomic binding targets, 3) RNA_SEQ for more complete surveys of transcription on a genome scale, 4) Methyl_SEQ providing genomic assessment of methylation status at base resolution.  Huge improvements in the technology over the past two years has resulted in the routine ability to generate about 20-30 billion bases of sequence information from each machine each week. A single run of a machine can generate up to 500 million reads of up to 100 bases in length and further improvements are coming. However, the scale of the data and expense of equipment set up and operation are impeding application to biological problems on the UCLA campus. We are creating the UCLA Center for High Throughput Biology (CHTB) that leverages substantial on campus expertise in next generation sequencing to enable UCLA biomedical researchers to harness the power of new genomics technologies. CHTB will provide: 1) adequate capacity and staff who run the sequencing machines and train users on library preparation; 2) adequate computational power and staff to process and store the raw data: and 3) provide a structure for establishing collaborations between informaticians and experimentalists on campus.  This talk will describe the Center, current technologies, the plan for implementation of the Center including access to the Center through partnerships with individual faculty and campus institutes. The CHTB will foster collaborations between experimentalists and computational biologists in each of four research areas: Expression / RNA Seq analysis; Regulation / ChIP Seq analysis; Resequencing / variation analysis; New genomes and metagenomics.

http://genomics.ctrl.ucla.edu/pmwiki/

Abstract:

Comparing the genomes of present-day species allows us to computationally reconstruct what most of the DNA bases in the genome of the common ancestor of placental mammals must have looked like approximately 100 million years ago. We can then deduce the genetic changes on the evolutionary path from that ancient species to humans. In so doing, we discover how Darwinian evolution has shaped us at the molecular level. About five percent of the human genome consists of conserved elements that have remained surprisingly unchanged across millions of years of evolution, suggesting important function. Only one third of these code for protein; the rest are likely to be gene regulatory elements and non-coding RNAs. Among these we occasionally see short segments that have undergone unusually rapid change in one species, such as a gene linked to brain development that has changed dramatically only in humans since we split from our common ancestor with chimpanzees. It will be many years before the biology of such examples is fully understood, but right now we relish the opportunity to get a first peek at the molecular tinkering that transformed our animal ancestors into humans.




Bio:

David Haussler’s research lies at the interface of mathematics, computer science, and molecular biology. He develops new statistical and algorithmic methods to explore the molecular evolution of the human genome, integrating cross-species comparative and high-throughput genomics data to study gene structure, function, and regulation. His recent research sheds light on the possible functionality of what was once considered to be “junk” DNA, and his lab has identified and explored the function of genomic elements that have remained conserved for millions of years and then undergone rapid evolution in newer species. He has also begun to computationally reconstruct the genome of the ancestor common to placental mammals.

As a collaborator on the international Human Genome Project, his team posted the first publicly available computational assembly of the human genome sequence on the internet—the precursor to the UCSC Genome Browser (http://genome.ucsc.edu), a web-based tool that is used extensively in biomedical research.

Haussler received his PhD in computer science from the University of Colorado at Boulder. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences and a fellow of AAAS and AAAI. He has won a number of awards, including the 2008 Senior Scientist Accomplishment Award from the International Society for Computational Biology (ISCB), the 2006 Dickson Prize for Science from Carnegie Mellon University, and the 2003 ACM/AAAI Allen Newell Award.

When: Mon., January 26, 2009 4PM
Where: CNSI Auditorium
Where: CNSI Auditorium

Abstract

Efforts to identify the mechanisms of cellular responses to perturbations
increasingly depend on high-throughput data including mRNA profiling and
genetic library screens. Yet the functional roles of the genes identified
by these assays often remain enigmatic. By comparing the results of these
two assays across various cellular responses, we found that they are
consistently distinct. Moreover, genetic screens tend to identify
response regulators, while mRNA profiling frequently detects metabolic
responses. We developed an integrative approach that bridges the gap
between these data using known molecular interactions, highlighting major
response pathways. We harnessed this approach to reveal cellular pathways
related to alpha-synuclein, a small lipid-binding protein implicated in
several neurodegenerative disorders including Parkinson disease.

Location: IPAM, UCLA.

When: Feb. 19-20, 2009.

Description: This symposium will focus on recent advances in high-throughput biology, and highlight the interface between experimental and computational biologists in this field.

The talks will be presented by a mix of experimentalists, computational biologists (and people who do both), demonstrating the exciting biological discoveries that are possible with new waves of high throughput data. Areas we will cover include image analysis (e.g. making microscopy a high-throughput analysis technology), mass spectrometry, and new types of “human-genome-in-a-day” ultra high throughput sequencing now coming online.

The symposium will take place over two days, with one day of talks (February 19), and a second day with separate roundtable meetings in each research area represented in the symposium. This second day will focus on identifying new directions and opportunities in this field, and strategic discussions about how UCLA can best move forward in the field.

The symposium speakers, and their affiliations are listed below.

Tobias Meyer, Professor of Chemical and Systems Biology at Stanford University.

Nevan Krogan, Assistant Professor, Cellular & Molecular Pharmacol, University of Caifornia, San Francisco.

Bing Ren, Associate Professor, Cellular & Molecular Medicine
Cancer Biology Program, University of California, San Diego

Edward Marcotte, Professor, Department of Chemistry and Biochemistry, University of Texas, Austin

Eric Schadt, Executive Scientific Director, Genetics, Merck Inc.

Rob Williams, Professor, Department of Anatomy and Neurobiology, University of Tennessee Health Science Center

Mike Synder, Yale University

Start Date: 2009-02-19
Start Time: 09:00
End Date: 2009-02-20
End Time: 17:00

When: Mon., October 20, 2008, 4 pm

Where: CNSI Auditorium

Abstract

The information overload in molecular biology is a mere example of the status common to all fields of the current science and culture: An ever-strengthening avalanche of novel data and ideas overwhelms specialists and non-specialists alike, unavoidably fragments knowledge, and makes enormous chunks of knowledge invisible/inaccessible to those who desperately need it.

The help of relieving the information overload may come from the text-miners who can automatically extract and catalogue facts described in books and journals.

My talk will touch the following six questions: What is text-mining? In what ways is text-mining useful? What can large-scale analyses of scientific literature tell us about both active and forgotten knowledge? What can such analyses tells us about the scientific community itself? How do mathematical models help us to differentiate true and false statements in literature? How will text-mining help us to find cures for human and non-human maladies?