Balaji S. Srinivasan
Stanford University
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Publication
Featured researches published by Balaji S. Srinivasan.
Genome Research | 2009
Joseph K. Pickrell; Graham Coop; John Novembre; Sridhar Kudaravalli; Jun Li; Devin Absher; Balaji S. Srinivasan; Gregory S. Barsh; Richard M. Myers; Marcus W. Feldman; Jonathan K. Pritchard
Genome-wide scans for recent positive selection in humans have yielded insight into the mechanisms underlying the extensive phenotypic diversity in our species, but have focused on a limited number of populations. Here, we present an analysis of recent selection in a global sample of 53 populations, using genotype data from the Human Genome Diversity-CEPH Panel. We refine the geographic distributions of known selective sweeps, and find extensive overlap between these distributions for populations in the same continental region but limited overlap between populations outside these groupings. We present several examples of previously unrecognized candidate targets of selection, including signals at a number of genes in the NRG-ERBB4 developmental pathway in non-African populations. Analysis of recently identified genes involved in complex diseases suggests that there has been selection on loci involved in susceptibility to type II diabetes. Finally, we search for local adaptation between geographically close populations, and highlight several examples.
research in computational molecular biology | 2008
Jason Flannick; Antal F. Novak; Chuong B. Do; Balaji S. Srinivasan; Serafim Batzoglou
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 2.0s 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.
Nature Reviews Genetics | 2004
Harley H. McAdams; Balaji S. Srinivasan; Adam P. Arkin
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.
Genetics in Medicine | 2013
Gabriel A. Lazarin; Imran S. Haque; Shivani Nazareth; Kevin Iori; A. Scott Patterson; Jessica Jacobson; John R. Marshall; William K. Seltzer; Pasquale Patrizio; Eric A. Evans; Balaji S. Srinivasan
Purpose:Recent developments in genomics have led to expanded carrier screening panels capable of assessing hundreds of causal mutations for genetic disease. This new technology enables simultaneous measurement of carrier frequencies for many diseases. As the resultant rank-ordering of carrier frequencies impacts the design and prioritization of screening programs, the accuracy of this ranking is a public health concern.Methods:A total of 23,453 individuals from many obstetric, genetics, and infertility clinics were referred for routine recessive disease carrier screening. Multiplex carrier screening was performed and results were aggregated for this study.Results:Twenty-four percent of individuals were identified as carriers for at least one of 108 disorders, and 5.2% were carriers for multiple disorders. We report tabulations of carrier frequency by self-identified ethnicity and disease.Conclusion:To our knowledge, this study of a large, ethnically diverse clinical sample provides the most accurate measurements to date of carrier frequencies for hundreds of recessive alleles. The study also yields information on the clinical considerations associated with routine use of expanded panels and provides support for a pan-ethnic screening paradigm that minimizes the use of “racial” categories by the physician, as recommended by recent guidelines.Genet Med 2013:15(3):178–186
Molecular Cell | 2010
Kasia G. Gora; Christos G. Tsokos; Y. Erin Chen; Balaji S. Srinivasan; Barrett S. Perchuk; Michael T. Laub
Progression through the Caulobacter cell cycle is driven by the master regulator CtrA, an essential two-component signaling protein that regulates the expression of nearly 100 genes. CtrA is abundant throughout the cell cycle except immediately prior to DNA replication. However, the expression of CtrA-activated genes is generally restricted to S phase. We identify the conserved protein SciP (small CtrA inhibitory protein) and show that it accumulates during G1, where it inhibits CtrA from activating target genes. The depletion of SciP from G1 cells leads to the inappropriate induction of CtrA-activated genes and, consequently, a disruption of the cell cycle. Conversely, the ectopic synthesis of SciP is sufficient to inhibit CtrA-dependent transcription, also disrupting the cell cycle. SciP binds directly to CtrA without affecting stability or phosphorylation; instead, SciP likely prevents CtrA from recruiting RNA polymerase. CtrA is thus tightly regulated by a protein-protein interaction which is critical to cell-cycle progression.
Journal of Computational Physics | 2007
Georg May; Balaji S. Srinivasan; Antony Jameson
During the past decade gas-kinetic methods based on the BGK simplification of the Boltzmann equation have been employed to compute fluid flow in a finite-difference or finite-volume context. Among the most successful formulations is the finite-volume scheme proposed by Xu [K. Xu, A gas-kinetic BGK scheme for the Navier-Stokes equations and its connection with artificial dissipation and Godunov method, J. Comput. Phys. 171 (48) (2001) 289-335]. In this paper we build on this theoretical framework mainly with the aim to improve the efficiency and convergence of the scheme, and extend the range of application to three-dimensional complex geometries using general unstructured meshes. To that end we propose a modified BGK finite-volume scheme, which significantly reduces the computational cost, and improves the behavior on stretched unstructured meshes. Furthermore, a modified data reconstruction procedure is presented to remove the known problem that the Chapman-Enskog expansion of the BGK equation fixes the Prandtl number at unity. The new Prandtl number correction operates at the level of the partial differential equations and is also significantly cheaper for general formulations than previously published methods. We address the issue of convergence acceleration by applying multigrid techniques to the kinetic discretization. The proposed modifications and convergence acceleration help make large-scale computations feasible at a cost competitive with conventional discretization techniques, while still exploiting the advantages of the gas-kinetic discretization, such as computing full viscous fluxes for finite volume schemes on a simple two-point stencil.
Journal of Computational Biology | 2009
Jason Flannick; Antal F. Novak; Chuong B. Do; Balaji S. Srinivasan; Serafim Batzoglou
We developed Graemlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) 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; (3) 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 (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Graemlin 2.0s 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 .
research in computational molecular biology | 2006
Balaji S. Srinivasan; Antal F. Novak; Jason Flannick; Serafim Batzoglou; Harley H. McAdams
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 dependent predictors 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.
Human Mutation | 2009
Balaji S. Srinivasan; Jaleh Doostzadeh; Farnaz Absalan; Sharareh Mohandessi; Roxana Jalili; Saharnaz Bigdeli; Justin Wang; Jaydev Mahadevan; Caroline Gl Lee; Ronald W. Davis; J. William Langston; Mostafa Ronaghi
It is quickly becoming apparent that situating human variation in a pathway context is crucial to understanding its phenotypic significance. Toward this end, we have developed a general method for finding pathways associated with traits that control for pathway size. We have applied this method to a new whole genome survey of coding SNP variation in 187 patients afflicted with Parkinson disease (PD) and 187 controls. We show that our dataset provides an independent replication of the axon guidance association recently reported by Lesnick et al. [PLoS Genet 2007;3:e98], and also indicates that variation in the ubiquitin‐mediated proteolysis and T‐cell receptor signaling pathways may predict PD susceptibility. Given this result, it is reasonable to hypothesize that pathway associations are more replicable than individual SNP associations in whole genome association studies. However, this hypothesis is complicated by a detailed comparison of our dataset to the second recent PD association study by Fung et al. [Lancet Neurol 2006;5:911–916]. Surprisingly, we find that the axon guidance pathway does not rank at the very top of the Fung dataset after controlling for pathway size. More generally, in comparing the studies, we find that SNP frequencies replicate well despite technologically different assays, but that both SNP and pathway associations are globally uncorrelated across studies. We thus have a situation in which an association between axon guidance pathway variation and PD has been found in 2 out of 3 studies. We conclude by relating this seeming inconsistency to the molecular heterogeneity of PD, and suggest future analyses that may resolve such discrepancies. Hum Mutat 0,1–11, 2008.
PLOS Genetics | 2008
Cara C. Boutte; Balaji S. Srinivasan; Jason Flannick; Antal F. Novak; Andrew T. Martens; Serafim Batzoglou; Patrick H. Viollier; Sean Crosson
We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the α-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1) confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2) defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3) identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other α-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo-inositol metabolism in select α-proteobacteria. Moreover, this study describes a forward validation of gene-network alignment, and illustrates a strategy for reliably transferring pathway-level annotation across bacterial species.