Doctor of Philosophy, Columbia University (2011)
Master of Philosophy, Columbia University (2009)
Master of Science, Columbia University (2006)
Bachelor of Technology, Indian Institute of Technology, Madras (2005)
Jonathan Pritchard, Postdoctoral Faculty Sponsor
msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.
2015; 10 (9)
Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.
View details for DOI 10.1371/journal.pone.0138030
View details for PubMedID 26406244
- fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets GENETICS 2014; 197 (2): 573-U207
Identification of Genetic Variants That Affect Histone Modifications in Human Cells
2013; 342 (6159): 747-749
Histone modifications are important markers of function and chromatin state, yet the DNA sequence elements that direct them to specific genomic locations are poorly understood. Here, we identify hundreds of quantitative trait loci, genome-wide, that affect histone modification or RNA polymerase II (Pol II) occupancy in Yoruba lymphoblastoid cell lines (LCLs). In many cases, the same variant is associated with quantitative changes in multiple histone marks and Pol II, as well as in deoxyribonuclease I sensitivity and nucleosome positioning. Transcription factor binding site polymorphisms are correlated overall with differences in local histone modification, and we identify specific transcription factors whose binding leads to histone modification in LCLs. Furthermore, variants that affect chromatin at distal regulatory sites frequently also direct changes in chromatin and gene expression at associated promoters.
View details for DOI 10.1126/science.1242429
View details for Web of Science ID 000326647600046
View details for PubMedID 24136359
Identifying Hosts of Families of Viruses: A Machine Learning Approach
2011; 6 (12)
Identifying emerging viral pathogens and characterizing their transmission is essential to developing effective public health measures in response to an epidemic. Phylogenetics, though currently the most popular tool used to characterize the likely host of a virus, can be ambiguous when studying species very distant to known species and when there is very little reliable sequence information available in the early stages of the outbreak of disease. Motivated by an existing framework for representing biological sequence information, we learn sparse, tree-structured models, built from decision rules based on subsequences, to predict viral hosts from protein sequence data using popular discriminative machine learning tools. Furthermore, the predictive motifs robustly selected by the learning algorithm are found to show strong host-specificity and occur in highly conserved regions of the viral proteome.
View details for DOI 10.1371/journal.pone.0027631
View details for Web of Science ID 000298365700006
View details for PubMedID 22174744
An Information-Theoretic Derivation of Min-Cut-Based Clustering
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2010; 32 (6): 988-995
Min-cut clustering, based on minimizing one of two heuristic cost functions proposed by Shi and Malik nearly a decade ago, has spawned tremendous research, both analytic and algorithmic, in the graph partitioning and image segmentation communities over the last decade. It is, however, unclear if these heuristics can be derived from a more general principle, facilitating generalization to new problem settings. Motivated by an existing graph partitioning framework, we derive relationships between optimizing relevance information, as defined in the Information Bottleneck method, and the regularized cut in a K-partitioned graph. For fast-mixing graphs, we show that the cost functions introduced by Shi and Malik can be well approximated as the rate of loss of predictive information about the location of random walkers on the graph. For graphs drawn from a generative model designed to describe community structure, the optimal information-theoretic partition and the optimal min-cut partition are shown to be the same with high probability.
View details for DOI 10.1109/TPAMI.2009.124
View details for Web of Science ID 000276671900003
View details for PubMedID 20431126
- Closed-form solutions for the free longitudinal vibration of inhomogeneous rods JOURNAL OF SOUND AND VIBRATION 2005; 283 (3-5): 1015-1030