Stanford University
318 Campus Drive
Clark Center S251
Stanford, CA 94305
Tel: (650) 380-0695
balajis_at_stanford.edu
Counsyl
If you're thinking of having children, go check out Counsyl. We've built a free genetic test (more precisely, free with insurance) which allows you to prevent genetic disease before pregnancy.
Our work has become international news and made the front page of the NYT Business section:
Classes (2007-2008):
Statistics
110 (Bulletin)
Statistical Methods in Engineering and the Physical Sciences.
Introduction to statistics for engineers and physical
scientists. Topics: descriptive statistics, probability, interval
estimation, tests of hypotheses, nonparametric methods, linear
regression, analysis of variance, elementary experimental design.
Prerequisite: one year of calculus.
GER:DB-Math
4-5 units, Aut (Srinivasan, B)- Data mining is used to discover
patterns and relationships in data. Emphasis is on large complex data
sets such as those in very large databases or through web
mining. Topics: decision trees, neural networks,and data
visualization.
3 units, Aut (Srinivasan, B)
Classes (2006-2007):
Statistics 110 (Bulletin)
Statistical Methods in Engineering and the Physical Sciences.
Introduction to statistics for
engineers and physical scientists. Topics: descriptive statistics,
probability, interval estimation, tests of hypotheses, nonparametric
methods, linear regression, analysis of variance, elementary
experimental design. Prerequisite: one year of calculus.
GER:DB-Math
4-5 units, Aut (Srinivasan, B)- (Graduate students register for STATS
366; same as BIOMEDIN 366.) Emphasis is on analysis of genomic scale
data sets with multivariate methods. Comparative genomics: whole
genome phylogeny, genome alignment, identification of constrained
elements, genomic motif finding, ENCODE project. Functional genomics:
interaction networks, random networks, data integration, tests for
functional enrichment, network alignment, deterministic and stochastic
genetic circuits, data-driven circuit reconstruction. Population
genomics: principles of molecular evolution, tests for selection,
HapMap, structural variation. Recommended: familiarity with R, Perl,
Unix, basic bioinformatics (sequence alignment and protein structure)
at the level of BIOC 218,
BIOMEDIN 214/CS274, or
CS 262.
2-3 units, Win (Srinivasan, B)
Research Interests:
- Protein Interaction Networks
- Functional & Comparative Genomics
- Population Genetics
- Genetic Circuits/Systems Biology
My research focuses on the application of computational and
statistical methods to problems in biology. More detail can be found
in the papers below:
Protein Interaction Networks
- J Flannick, A Novak, CB Do, BS Srinivasan, S Batzoglou.
"Automatic Parameter Learning for Network Alignment."
RECOMB 2008 Proceedings, to appear. (PDF)
We developed Graemlin 2.0, a new multiple network aligner with (1) a
novel scoring function that can use arbitrary features of a multiple
network alignment, such as protein deletions, protein duplications,
protein mutations, and interaction losses; (2) a parameter learning
algorithm that uses a training set of known network alignments to
learn parameters for our scoring function and thereby adapt it to any
set of networks; and (3) an algorithm that uses our scoring function
to find approximate multiple network alignments in linear time. We
tested Graemlin 2.0's accuracy on protein interaction networks from
IntAct, DIP, and the Stanford Network Database. We show that, on each
of these datasets, Graemlin 2.0 has higher sensitivity and specificity
than existing network aligners. Graemlin 2.0 is available under the
GNU public license
at http://graemlin.stanford.edu.
-
BS Srinivasan, NH Shah, JA Flannick, E Abeliuk, AF Novak, S Batzoglou
"Current Progress in Network Research: Toward Reference Networks for
Key Model Organisms", Briefings in Bioinformatics
(PubMed), (Briefings in Bioinformatics), (Google Scholar), (PDF)
The collection of multiple genome-scale datasets is now routine, and
the frontier of research in systems biology has shifted
accordingly. Rather than clustering a single dataset to produce a
static map of functional modules, the focus today is on data
integration, network alignment, interactive visualization and
ontological markup. Because of the intrinsic noisiness of
high-throughput measurements, statistical methods have been central to
this effort. In this review, we briefly survey available datasets in
functional genomics, review methods for data integration and network
alignment, and describe recent work on using network models to guide
experimental validation. We explain how the integration and validation
steps spring from a Bayesian description of network uncertainty, and
conclude by describing an important near-term milestone for systems
biology: the construction of a set of rich reference networks for key
model organisms.
-
BS Srinivasan, AF Novak, JA Flannick, S Batzoglou, HH McAdams,
"Integrated Interaction Networks for 11 Microbes", RECOMB 2006
Proceedings (Won Best Poster at CSHL Genome Informatics 2005) (Springer
Website), (PDF and associated poster)
We have combined four different types of functional genomic data to
create high coverage protein interaction networks for 11 microbes. Our
integration algorithm naturally handles statistically dependentpredictors and automatically corrects for differing noise
levels and data corruption in different evidence sources. We find that
many of the predictions in each integrated network hinge on moderate but
consistent evidence from multiple sources rather than strong evidence
from a single source, yielding novel biology which would be missed if
a single data source such as coexpression or coinheritance was used in
isolation. In addition to statistical analysis, we demonstrate via
case study that these subtle interactions can discover new aspects of
even well studied functional modules. Our work represents the largest
collection of probabilistic protein interaction networks compiled to
date, and our methods can be applied to any sequenced organism and any
kind of experimental or computational technique which produces
pairwise measures of protein interaction.
-
BS Srinivasan, CB Do, S Batzoglou, "RECOMB 2006: Evidence for
Intelligent (Algorithm) Design", Genome
Biology. 7(7):322 (2006).
(PubMed),
(Genome Biology),
(PDF)
More than 700 computational biologists convened in beautiful Venice in
early April for RECOMB 2006, the 10th annual Conference on Research in
Computational Molecular Biology. After 40 talks, 6 keynote lectures,
180 posters, and at least two cameos by the Riemann zeta function, several emerging trends in computational biology are apparent. We have selected a few of the talks that
particularly caught our eye out of the many excellent ones given at the conference.
Functional & Comparative Genomics
-
JA Flannick, AF Novak, BS Srinivasan, HH McAdams, S Batzoglou,
"Graemlin: General and Robust Alignment of Multiple Large Interaction Networks", Genome
Research 16(9):1169-81 (2006).
(Graemlin Website),(PubMed)
(Genome Research),
(PDF, Supplementary Information)
The recent proliferation of protein interaction networks has motivated
research into network alignment: the cross-species comparison of conserved functional modules. Previous studies have laid the foundations for such comparisons and demonstrated their power on a select set of sparse interaction networks. Recently, however, new computational techniques have produced hundreds of predicted interaction networks with interconnection densities that push existing
alignment algorithms to their limits. To find conserved functional modules in these new networks, we have developed Graemlin, the first algorithm capable of scalable multiple network alignment. Graemlin's explicit model of functional evolution allows both the generalization of existing alignment scoring schemes and the location of conserved network topologies other than protein complexes and metabolic pathways. To assess Graemlin's performance, we have developed the first quantitative benchmarks for network alignment, which allow comparisons of algorithms in terms of their ability to recapitulate the KEGG database of conserved functional modules. We find that Graemlin achieves substantial scalability gains over previous methods while improving sensitivity.
-
BS Srinivasan et al., "Functional Genome Annotation through Phylogenomic Mapping", Nature Biotechnology. 23, 691-698 (2005).
(Website/Mirror),(PubMed)
(Nature Biotechnology)
(Google Scholar),
(PDF, Supplementary Information)
Accurate determination of functional interactions among proteins at the genome level remains a challenge for genomic research. Here we introduce a genome-scale
approach to functional protein annotation -- phylogenomic mapping -- that requires only sequence data, can be applied equally well to both finished and unfinished
genomes, and can be extended beyond single genomes to annotate multiple genomes simultaneously. We have developed and applied it to more than 200 sequenced bacterial
genomes. Proteins with similar evolutionary histories were grouped together, placed on a three dimensional map, and visualized as a topographical landscape. The resulting
phylogenomic map displays thousands of proteins clustered in mountains on the basis of coinheritance, a strong indicator of shared function. In addition to systematic
computational validation, we have experimentally confirmed the ability of phylogenomic maps to predict both mutant phenotype and gene function in the delta proteobacterium
Myxococcus xanthus .
Genetic Circuits/Systems Biology
-
HH McAdams, BS Srinivasan, AP Arkin, "The Evolution of Genetic Regulatory Systems in Bacteria.", Nature Reviews Genetics. 5(3):169-178 (2004)
(PubMed),
(Nature Reviews Genetics),
(Google Scholar),
(PDF)
The genomes of bacterial species show enormous plasticity in the function of individual genes, in genome organization and in regulatory organization. Over millions of years, both bacterial genes and their genomes have been extensively reorganized and adapted so that bacteria occupy virtually every environmental niche on the earth. In addition, changes have occurred in the regulatory circuitry that controls cell operations, cell-cycle progression and responses to environmental signals. The mechanisms that underlie the adaptation of the bacterial regulatory circuitry are crucial for understanding the bacterial biosphere and have important roles in the emergence of antibiotic resistance.
