~ Sorin Istrail, Julie Nguyen Brown Professor of Computational and Mathematical and Professor of Computer Science
Algorithmic Methods in Genomics, Regulatory Genomics,
Genetic Basis of Disease, Medical Bioinformatics
My laboratory's main research projects are:
(1) Genomic Regulatory Networks focusing on sea urchin developmental gene regulatory
networks, building a high-resolution transcriptome map of the embryo, and inferring
logic functions of genomic cis-regulatory code and the principles of information
processing of genomic regulation;
(2) Computational Models of SNPs and Haplotypes dealing with HapMap analysis tools
design for SNP selection, haplotype phasing, and genome-wide disease associations;
(3) Medical Bioinformatics with focus on comparative immunopeptidomics of humans
and their pathogens, genetic determinants of sudden cardiac death and human-rabbit
comparative genomics, computational support for pathology diagnosis of cancer.
I am also interested in building a programming language for genomics, designing
protein folding algorithms, and continuing John von Neumann's research program towards
a new information and computation theory for biological complex systems.
~ Charles Lawrence, Professor of Applied Mathematics
Bayesian Inferences of DNA and RNA Structure and Function
Statistical inference of structure and function for nucleotide polymers is my laboratory’s
area of expertise. The lab’s research focuses on nucleic acid structure and
function, including studies of the cis acting features of transcription regulation
and RNA secondary structure and its relationship to function. We develop and apply
Bayesian inference technologies, including exact Bayesian and MCMC sampling algorithms
to draw inferences on these systems using genome sequence data. Applications include
whole genome inferences of cis-regulatory elements and regulons for single cell
species, cis-regulatory modules and regulons in multi-cellular species, cis regulatory
features of RNAi mechanisms, and RNA secondary structure landscapes.
~ Benjamin Raphael, Assistant Professor of Computer Science
Computational Cancer Genomics, Biological Sequence Analysis
-Computational Cancer Genomics. Cancer is driven by mutations in DNA including both
single letter changes and more drastic, larger scale rearrangements of the genome;
e.g. chromosomal inversions, translocations, deletions, and duplications. In some
types of cancer, these large-scale rearrangements produce changes in gene structure
and regulation that are directly implicated in cancer progression and are targets
for cancer therapeutics. In 2003, I began developing algorithms for analyzing genome
rearrangements in tumors through a technique called End Sequence Profiling. This
work is an ongoing collaboration with Colin Collins, Joe Gray, and Stas Volik at
the University of California, San Francisco Cancer Center. In addition to the obvious
medical importance, the study of cancer presents numerous challenges that impact
nearly every area of bioinformatics.
-Biological Sequence Analysis. The second focus of my research is the development
of algorithms for biological sequence analysis, specifically methods for analyzing
and annotating DNA and protein sequences including problems in multiple sequence
alignment, motif finding, comparative genomics, and structural variation in the
~ Daniel Weinreich, Assistant Professor of Biology
Evolutionary Genetics and Genomics
I employ population genetics, computational, molecular, microbial and protein biology
to explicate the Darwinian paradigm in its most fundamental, mechanistic terms.
In particular, I am interested in the consequences on evolution by natural selection
arising from functional interactions between mutations within loci or between loci
within genomes. Such interactions mean that the phenotypic effect of a mutation
vary as a function of what other mutations are already present, a phenomenon called
epistasis. My work follows parallel theoretical and experimental lines.
Theoretically I am interested in understanding what forms of epistasis for fitness
influence the ability of natural selection to follow mutational and recombinational
trajectories to higher fitness genotypes. More abstractly, I am working on methods
to characterize the space of all possible patterns of epistasis, with with particular
emphasis on consequences for evolution by natural selection.
The absence of mature theory has also left many basic empirical questions unasked
and I am exploring some of these in the laboratory, chiefly by measuring the fitness
effects of carefully selected sets of mutations singly and in combination. Additionally,
in order to understand the mechanistic basis of such functional interactions, I
seek to dissect the fitness consequences of individual mutations into a causal chain
of effects at the molecular, biochemical, biophysical, cellular and physiological
levels. Because evolution by natural selection is an important component of human
infectious disease biology, much of my work employs is in human pathogen models,
which also offer many technical advantages. Additionally I work in bacteriophage
and marine bacteria.
I am actively recruiting students at all levels who possess a background or strong
interest in one or more of evolution, population genetics, molecular or protein
biology, computational biology or theory.
~ Will Fairbrother, Assistant Professor of Biology
Using Computational Methods to Identify Sequence Elements
that are Involved in Gene Expression.
The pursuit of my research goals: exploring mechanisms of signal evolution and understanding
the effects of mutation on gene expression will require reliable definitions of
gene expression signals. We developed a computational method, RESCUE-ESE, for identifying
exonic splicing enhancers in human genes. Although ESE sequence motifs were poorly
defined prior to RESCUE-ESE, the literature had provided some insight into how ESEs
function. For example, ESEs had been shown to function by compensating for weak
(non-consensus) splice sites and this enhancement was lost when the ESE was moved
to an intronic location. These observations suggested that ESEs would possess two
attributes: They would be A) enriched in exons (relative to introns) and B) enriched
in exons flanked by weak (non-consensus) splice sites relative to strong (consensus)
splice sites. The RESCUE-ESE method counted word frequencies in the vicinity around
splice sites and found 238 hexamers to be significantly enriched in both of these
attributes (see A and B above). These 238 hexamers were clustered on the basis of
sequence into 10 unique sets.
~ Franco Preparata, An Wang Professor of Computer Science
Following early research in switching and coding, culminating in the discovery of
the nonlinear Preparata codes, for the past three decades the focus of Franco Preparata's
research has been the design and analysis of algorithms in their most general connotation.
With the remarkable evolution of computer technology, his research interests have
been correspondingly evolving. He has been deeply interested in fundamental algorithms
and data structures, VLSI computation and layout, and parallel algorithms.
Perhaps the most enduring interest has been computational geometry, a spin-off of
algorithmic research aimed at the systematic investigation of methods for the most
efficient solution of geometric problems. Geometric problems are ubiquitous in human
activities. Sporadic, and frequently inefficient, computer solutions had been proposed
before, but in the mid-seventies computational geometry emerged as a self-standing
discipline targeted at this important area. The goal of computational geometry is
to analyze the combinatorial structure of specific problems as the underpinning
of efficient algorithms for their solution. The field burgeoned, and in the mid-eighties
Prof. Preparata wrote a textbook on the subject that helped establish it in the
instructional arena. Today an enormous body of geometric algorithms is known and
this knowledge is increasingly indispensable in several applied areas such as geographic
information systems, computer graphics, and computer-aided design and manufacturing.
Within the last area, Prof. Preparata has also contributed to computational metrology—the
assessment of the geometric quality of manufactured parts.
As another example of computer science interacting with other fields, today his
main research focus is computational biology (also called 'bioalgorithmics'), an
emerging discipline that entails the development and use of mathematical and computer
science techniques to solve problems in molecular biology. Since the discovery of
the structure of DNA about 50 years ago and the digital underpinning of molecular
biology, huge amounts of data have been generated in this field, making it necessary
to resort to sophisticated computer science techniques for their analysis.
~ David M. Rand, Professor of Biology
Molecular evolution and ecology; nuclear-mitochondrial
coevolution; comparative genomics
We are interested in molecular evolution, comparative genomics and association mapping
of continuous traits. Most of our work is empirical, but we employ computational
methods using existing software packages (PAML, MEGA, DNAsp), or for specific questions,
custom software written in C++ or Perl. A Major interest is how selection acts on
mitochondrial genes. We have developed neutrality tests of nucleotide and protein
sequence data to infer selection on standing variation. A focus of current research
is the coevolution of nuclear and mitochondrial genomes, with particular interest
in nuclear genes that function in the mitochondrion and alter fitness effects of
mtDNA mutations. We employ laboratory selection experiments to uncover genomic regions
that carry allelic variation governing the selected traits. Ongoing work with thermal
selection has identified a region of the Drosophila X chromosome that alters fitness
at different temperatures; we are dissecting this region with gene mapping methods
and analyses of latitudinal clines of haplotype variation.
~ J. William Suggs, Associate Professor of Chemistry and Biochemistry
We are interested in ligand design and synthesis. In particular, we are interested
in how DNA molecules can bind in specific ways to organic molecules, especially
drugs used in cancer chemotherapy. We also seek to understand how drug molecules
can induce specific rearrangements in mammalian chromosomes, since abberant chromosomes
are one of the hallmarks of cancer cells. Finally, we have a program underway to
make chemically modified DNA polymers to test the limits of molecular information
storage. Recent work has resulted in our synthesizing DNA binding drugs whose location
in cellular DNA can be determined by electron microscopy.
~ Zhjin Wu, Assistant Professor of Medical Science
Zhijin's primary interest is in developing statistical methods for analyizing data
from microarray experiments. She and colleagues proposed the model-based preprocessing
method, GCRMA, that adjusts for non-specific background and normalizes raw measurements
from microarrays and produces more accurate and precise estimates of gene expression
levels. In addition to their original implementation of the method provided through
the Bioconductor project, GCRMA has also been incorporated into commercial software
for microarray data analysis, such as GeneSpring, and has become a popular alternative
to the manufacturer-provided procedure.
Besides gene expression experiments, she continues to work on methods for new applications
of the microarray technology, such as detecting single nucleotide polymorphisms
(SNPs) and DNA methylation. She is also interested in high-throughput screening
(HTS) on cell based assays.
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