Parantu K Shah

University of Texas MD Anderson Cancer Center, Houston, Texas, United States

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Publications (30)241.77 Total impact

  • Xiaoxi Ling · Yu Liu · Zhen Huang · Parantu K. Shah · Cheng Li
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    ABSTRACT: With the advances of all research fields, the volume of scientific literature has grown exponentially over the past decades, and the management and exploration of scientific literature is becoming an increasingly complicated task. It calls for a tool that combines scientific impacts and social focuses to visualize relevant papers from a specific research area and time period, and to find important and interesting papers. Therefore, we propose a graphical article-level metric (gALM), which captures the impact and popularity of papers from scientific and social aspects. These two dimensions are combined and visualized graphically as a circular map. The map is divided into sectors of papers belonging to a publication year, and each block represents a paper’s journal citations by block size and readerships in Mendeley by block color. In this graphical way, gALM provides a more intuitive comparison of large-scale literatures. In addition, we also design an online Web server, Science Navigation Map (SNM), which not only visualizes the gALM but provides it with interactive features. Through an interactive visualization map of article-level metrics on scientific impact and social popularity in Mendeley, users can intuitively make a comparison of papers as well as explore and filter important and relevant papers by these metrics. We take the journal PLoS Biology as an example and visualize all the papers published in PLoS Biology during 2003 and 2014 by SNM. From this map, one can easily and intuitively find basic statistics of papers, such as the most cited papers and the most popular papers in Mendeley during a time period. SNM on the journal PLoS Biology is publicly available at http:// www. linkscholar. org/ plosbiology/ .
    No preview · Article · Nov 2015 · Scientometrics

  • No preview · Article · Aug 2015 · Cancer Research
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    ABSTRACT: The SWI/SNF multisubunit complex modulates chromatin structure through the activity of two mutually exclusive catalytic subunits, SMARCA2 and SMARCA4, which both contain a bromodomain and an ATPase domain. Using RNAi, cancer-specific vulnerabilities have been identified in SWI/SNF-mutant tumors, including SMARCA4-deficient lung cancer; however, the contribution of conserved, druggable protein domains to this anticancer phenotype is unknown. Here, we functionally deconstruct the SMARCA2/4 paralog dependence of cancer cells using bioinformatics, genetic, and pharmacologic tools. We evaluate a selective SMARCA2/4 bromodomain inhibitor (PFI-3) and characterize its activity in chromatin-binding and cell-functional assays focusing on cells with altered SWI/SNF complex (e.g., lung, synovial sarcoma, leukemia, and rhabdoid tumors). We demonstrate that PFI-3 is a potent, cell-permeable probe capable of displacing ectopically expressed, GFP-tagged SMARCA2-bromodomain from chromatin, yet contrary to target knockdown, the inhibitor fails to display an antiproliferative phenotype. Mechanistically, the lack of pharmacologic efficacy is reconciled by the failure of bromodomain inhibition to displace endogenous, full-length SMARCA2 from chromatin as determined by in situ cell extraction, chromatin immunoprecipitation, and target gene expression studies. Furthermore, using inducible RNAi and cDNA complementation (bromodomain- and ATPase-dead constructs), we unequivocally identify the ATPase domain, and not the bromodomain of SMARCA2, as the relevant therapeutic target with the catalytic activity suppressing defined transcriptional programs. Taken together, our complementary genetic and pharmacologic studies exemplify a general strategy for multidomain protein drug-target validation and in case of SMARCA2/4 highlight the potential for drugging the more challenging helicase/ATPase domain to deliver on the promise of synthetic-lethality therapy. Cancer Res; 75(18); 1-14. ©2015 AACR.
    Full-text · Article · Jul 2015 · Cancer Research
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    ABSTRACT: Our current understanding of cancer genetics is grounded on the principle that cancer arises from a clone that has accumulated the requisite somatically acquired genetic aberrations, leading to the malignant transformation. It also results in aberrent of gene and protein expression. Next generation sequencing (NGS) or deep sequencing platforms are being used to create large catalogues of changes in copy numbers, mutations, structural variations, gene fusions, gene expression, and other types of information for cancer patients. However, inferring different types of biological changes from raw reads generated using the sequencing experiments is algorithmically and computationally challenging. In this article, we outline common steps for the quality control and processing of NGS data. We highlight the importance of accurate and application-specific alignment of these reads and the methodological steps and challenges in obtaining different types of information. We comment on the importance of integrating these data and building infrastructure to analyse it. We also provide exhaustive lists of available software to obtain information and point the readers to articles comparing software for deeper insight in specialised areas. We hope that the article will guide readers in choosing the right tools for analysing oncogenomic datasets.
    Full-text · Article · Jan 2015 · Contemporary Oncology / Wspólczesna Onkologia
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    ABSTRACT: Androgen deprivation is the standard treatment for advanced prostate cancer (PCa), but most patients ultimately develop resistance and tumor recurrence. We found that MYB is transcriptionally activated by androgen deprivation therapy or genetic silencing of the androgen receptor (AR). MYB silencing inhibited PCa growth in culture and xenografts in mice. Microarray data revealed that c-Myb and AR shared a subset of target genes that encode DNA damage response (DDR) proteins, suggesting that c-Myb may supplant AR as the dominant regulator of their common DDR target genes in AR inhibition-resistant or AR-negative PCa. Gene signatures including AR, MYB, and their common DDR-associated target genes positively correlated with metastasis, castration resistance, tumor recurrence, and decreased survival in PCa patients. In culture and in xenograft-bearing mice, a combination strategy involving the knockdown of MYB, BRCA1, or TOPBP1 or the abrogation of cell cycle checkpoint arrest with AZD7762, an inhibitor of the checkpoint kinase Chk1, increased the cytotoxicity of the poly[adenosine 5'-diphosphate (ADP)-ribose] polymerase (PARP) inhibitor olaparib in PCa cells. Our results reveal new mechanism-based therapeutic approaches for PCa by targeting PARP and the DDR pathway involving c-Myb, TopBP1, ataxia telangiectasia mutated- and Rad3-related (ATR), and Chk1.
    No preview · Article · May 2014 · Science Signaling
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    ABSTRACT: With advent of several treatment options in multiple myeloma, a selection of effective regimen has become an important issue. Use of GEP is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated ability of GEP to predict complete response in MM. GEP from pre-treatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional datasets from three different studies (n=511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four datasets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56% to 78% in test datasets and no significant difference with regard to GEP platforms, treatment regimens or in newly-diagnosed or relapsed patients. Importantly, permuted P-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.Leukemia accepted article peview online, 15 April 2014. doi:10.1038/leu.2014.140.
    Full-text · Article · Apr 2014 · Leukemia: official journal of the Leukemia Society of America, Leukemia Research Fund, U.K
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    ABSTRACT: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.
    Preview · Article · Oct 2013 · BMC Genomics
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    ABSTRACT: Multiple myeloma is a hematological cancer of plasma B-cells and remains incurable. Two major subtypes of myeloma, hyperdiploid (HMM) and non-hyperdiploid myeloma (NHMM), have distinct chromosomal alterations and different survival outcomes. Transcription factors (TrFs) have been implicated in myeloma oncogenesis but their dysregulation in myeloma subtypes are less studied. Here we develop a TrF-pathway co-expression analysis to identify altered co-expression between two sample types. We apply the method to the two myeloma subtypes and the cell cycle arrest pathway, which is significantly differentially expressed between the two subtypes. We find that TrFs MYC, NF-κB and HOXA9 have significantly lower co-expression with cell cycle arrest in HMM, co-occurring with their over-activation in HMM. In contrast, TrFs ESR1, SP1 and E2F1 have significantly lower co-expression with cell cycle arrest in NHMM. SP1 ChIP targets are enriched by cell cycle arrest genes. These results motivate a cooperation model of ESR1 and SP1 in regulating cell cycle arrest, and a hypothesis that their over-activation in NHMM disrupts proper regulation of cell cycle arrest. Co-targeting ESR1 and SP1 shows a synergistic effect on inhibiting myeloma proliferation in NHMM cell lines. Therefore, studying TrF-pathway co-expression dysregulation in human cancers facilitates forming novel hypotheses towards clinical utility.Leukemia accepted article preview online, 8 August 2013. doi:10.1038/leu.2013.233.
    Full-text · Article · Aug 2013 · Leukemia: official journal of the Leukemia Society of America, Leukemia Research Fund, U.K
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    ABSTRACT: Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software.
    Full-text · Article · Mar 2013 · PLoS ONE
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    ABSTRACT: Genome-wide profiles of tumors obtained using functional genomics platforms are being deposited to the public repositories at an astronomical scale, as a result of focused efforts by individual laboratories and large projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium. Consequently, there is an urgent need for reliable tools that integrate and interpret these data in light of current knowledge and disseminate results to biomedical researchers in a user-friendly manner. We have built the canEvolve web portal to meet this need.
    Full-text · Article · Feb 2013 · PLoS ONE
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    ABSTRACT: Bortezomib therapy has proven successful for the treatment of relapsed/refractory, relapsed, and newly diagnosed multiple myeloma (MM); however, dose-limiting toxicities and the development of resistance limit its long-term utility. Here, we show that P5091 is an inhibitor of deubiquitylating enzyme USP7, which induces apoptosis in MM cells resistant to conventional and bortezomib therapies. Biochemical and genetic studies show that blockade of HDM2 and p21 abrogates P5091-induced cytotoxicity. In animal tumor model studies, P5091 is well tolerated, inhibits tumor growth, and prolongs survival. Combining P5091 with lenalidomide, HDAC inhibitor SAHA, or dexamethasone triggers synergistic anti-MM activity. Our preclinical study therefore supports clinical evaluation of USP7 inhibitor, alone or in combination, as a potential MM therapy.
    Full-text · Article · Sep 2012 · Cancer cell
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    ABSTRACT: We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF–target gene and miRNA–mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for ∼20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at http://www.canevolve.org/dChip-GemiNi.
    Full-text · Article · May 2012 · Nucleic Acids Research
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    Norman Huang · Parantu K Shah · Cheng Li
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    ABSTRACT: Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.
    Full-text · Article · Sep 2011 · Briefings in Bioinformatics
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    ABSTRACT: Target specific antibodies are pivotal for the design of vaccines, immunodiagnostic tests, studies on proteomics for cancer biomarker discovery, identification of protein-DNA and other interactions, and small and large biochemical assays. Therefore, it is important to understand the properties of protein sequences that are important for antigenicity and to identify small peptide epitopes and large regions in the linear sequence of the proteins whose utilization result in specific antibodies. Our analysis using protein properties suggested that sequence composition combined with evolutionary information and predicted secondary structure, as well as solvent accessibility is sufficient to predict successful peptide epitopes. The antigenicity and the specificity in immune response were also found to depend on the epitope length. We trained the B-Cell Epitope Oracle (BEOracle), a support vector machine (SVM) classifier, for the identification of continuous B-Cell epitopes with these protein properties as learning features. The BEOracle achieved an F1-measure of 81.37% on a large validation set. The BEOracle classifier outperformed the classical methods based on propensity and sophisticated methods like BCPred and Bepipred for B-Cell epitope prediction. The BEOracle classifier also identified peptides for the ChIP-grade antibodies from the modENCODE/ENCODE projects with 96.88% accuracy. High BEOracle score for peptides showed some correlation with the antibody intensity on Immunofluorescence studies done on fly embryos. Finally, a second SVM classifier, the B-Cell Region Oracle (BROracle) was trained with the BEOracle scores as features to predict the performance of antibodies generated with large protein regions with high accuracy. The BROracle classifier achieved accuracies of 75.26-63.88% on a validation set with immunofluorescence, immunohistochemistry, protein arrays and western blot results from Protein Atlas database. Together our results suggest that antigenicity is a local property of the protein sequences and that protein sequence properties of composition, secondary structure, solvent accessibility and evolutionary conservation are the determinants of antigenicity and specificity in immune response. Moreover, specificity in immune response could also be accurately predicted for large protein regions without the knowledge of the protein tertiary structure or the presence of discontinuous epitopes. The dataset prepared in this work and the classifier models are available for download at https://sites.google.com/site/oracleclassifiers/.
    Full-text · Article · Jun 2011 · BMC Bioinformatics
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    ABSTRACT: Systematic annotation of gene regulatory elements is a major challenge in genome science. Direct mapping of chromatin modification marks and transcriptional factor binding sites genome-wide has successfully identified specific subtypes of regulatory elements. In Drosophila several pioneering studies have provided genome-wide identification of Polycomb response elements, chromatin states, transcription factor binding sites, RNA polymerase II regulation and insulator elements; however, comprehensive annotation of the regulatory genome remains a significant challenge. Here we describe results from the modENCODE cis-regulatory annotation project. We produced a map of the Drosophila melanogaster regulatory genome on the basis of more than 300 chromatin immunoprecipitation data sets for eight chromatin features, five histone deacetylases and thirty-eight site-specific transcription factors at different stages of development. Using these data we inferred more than 20,000 candidate regulatory elements and validated a subset of predictions for promoters, enhancers and insulators in vivo. We identified also nearly 2,000 genomic regions of dense transcription factor binding associated with chromatin activity and accessibility. We discovered hundreds of new transcription factor co-binding relationships and defined a transcription factor network with over 800 potential regulatory relationships.
    Full-text · Article · Mar 2011 · Nature
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    ABSTRACT: Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org.
    Full-text · Article · Mar 2011 · BMC Bioinformatics
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    ABSTRACT: To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.
    Full-text · Article · Dec 2010 · Science
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    ABSTRACT: Author Summary The spatiotemporal specificity of gene expression is controlled by interactions among regulatory proteins, cis-regulatory elements, chromatin modifications, and genes. These interactions can occur over large distances, and the mechanisms by which they are controlled are poorly understood. Insulators are DNA sequences that can both block the interaction between regulatory elements and genes, as well as block the spread of regions of modified chromatin. To date, relatively few insulators have been identified in developing Drosophila embryos. We here present the genome wide identification of over 14,000 binding sites for 6 insulator-associated proteins. We demonstrate the existence of two broad classes of insulators. Insulators of both classes are enriched at the boundaries of a particular chromatin modification. However, only insulators bound by BEAF-32, CP190, and dCTCF are enriched in regions of open chromatin or demarcate gene boundaries, with a particular enrichment between differentially expressed promoters. Furthermore, insulators of this class are enriched at points of chromosomal rearrangement among the 12 species of sequenced Drosophila, suggesting that insulator defined regulatory boundaries are evolutionarily conserved.
    Full-text · Article · Jan 2010 · PLoS Genetics
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    ABSTRACT: The highly coordinated expression of thousands of genes in an organism is regulated by the concerted action of transcription factors, chromatin proteins and epigenetic mechanisms. High-throughput experimental data for genome wide in vivo protein-DNA interactions and epigenetic marks are becoming available from large projects, such as the model organism ENCyclopedia Of DNA Elements (modENCODE) and from individual labs. Dissemination and visualization of these datasets in an explorable form is an important challenge. To support research on Drosophila melanogaster transcription regulation and make the genome wide in vivo protein-DNA interactions data available to the scientific community as a whole, we have developed a system called Flynet. Currently, Flynet contains 101 datasets for 38 transcription factors and chromatin regulator proteins in different experimental conditions. These factors exhibit different types of binding profiles ranging from sharp localized peaks to broad binding regions. The protein-DNA interaction data in Flynet was obtained from the analysis of chromatin immunoprecipitation experiments on one color and two color genomic tiling arrays as well as chromatin immunoprecipitation followed by massively parallel sequencing. A web-based interface, integrated with an AJAX based genome browser, has been built for queries and presenting analysis results. Flynet also makes available the cis-regulatory modules reported in literature, known and de novo identified sequence motifs across the genome, and other resources to study gene regulation. Flynet is available at https://www.cistrack.org/flynet/.
    Full-text · Article · Sep 2009 · Bioinformatics

  • No preview · Article · Jan 2009

Publication Stats

1k Citations
241.77 Total Impact Points

Institutions

  • 2014-2015
    • University of Texas MD Anderson Cancer Center
      • Institute for Applied Cancer Science
      Houston, Texas, United States
  • 2011-2015
    • Dana-Farber Cancer Institute
      • Department of Biostatistics and Computational Biology
      Boston, Massachusetts, United States
  • 2011-2013
    • Harvard University
      Cambridge, Massachusetts, United States
  • 2007-2010
    • University of Chicago
      • Department of Human Genetics
      Chicago, Illinois, United States
  • 2006-2008
    • European Molecular Biology Laboratory
      • Structural and Computational Biology Unit (Heidelberg)
      Heidelburg, Baden-Württemberg, Germany
  • 2003-2005
    • Max-Delbrück-Centrum für Molekulare Medizin
      Berlín, Berlin, Germany
  • 2001
    • Tata Institute of Fundamental Research
      • National Centre for Biological Sciences
      Mumbai, Mahārāshtra, India