Tampa, Florida, United States
Masterfully adept at leading design, development and implementation of scientific studies, data acquisition, data analysis and reporting of scientific results. Highly-knowledgeable of scientific developments. Holds a reputable record of contributions to ground-breaking scientific innovations. Effective leader who performs well in collaborative environment. Proven results in the design of novel experiments, particularly in area of genomics research. Articulate, analytical and dedicated.
Directed analysis of gene-expression profiling data for the The Environmental Determinants of Diabetes in the Young (TEDDY) study, the largest study to date to screen and enroll the highest genetically at-risk infants from families in the general population and from families with a first degree relative as well as for the Rare Diseases Clinical Research Network which included image analysis, quality assessment, normalization and different statistical analysis. Designed study protocols and analysis pipelines for the large-scale genomics data for the TEDDY study.
Spearheaded development of a methodology (svclassify) to calculate annotations from one or more aligned bam files from any high-throughput sequencing technology including Illumina, Pacific Biosciences (PacBio) and Moleculo. Built a machine learning technique (one-class classification method) using these annotations to classify candidate structural variants (SVs) as likely true or false positives. Developed 2676 high-confidence deletions and 68 high-confidence insertions calls across a whole genome that could be used for benchmarking SV callers. Created integration of analytical approaches for detection of single nucleotide polymorphisms (SNPs) as well as insertions and deletions (indels) for the Genome in a Bottle Consortium (GIAB). Executed whole-genome/whole-exome sequencing data analysis including quality assessment, alignment, variant identification, variant annotation and visualization. Developed a highly confident variant call sets across a whole genome that could be used as “truth” for understanding accuracy for next generation sequencing methods and algorithms.
Structured an analytical approach for genome-wide association study (GWAS) of pancreatic cancer in 3,851 affected individuals and 3,934 unaffected controls to identify 3 genomic regions in chromosomes 1q32.1, 5p15.33 and 13q22.1 that are associated with risk of pancreatic cancer in populations of European ancestry. Led development analysis of genotype imputation using the 1000 Genomes Project data as well as DNA sequencing data to fine-map common pancreatic cancer susceptibility regions. This led to improved accuracy of alignments and variant calling for DNA sequencing data by integrating various bioinformatics tools. Created RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq) and methylated DNA immunoprecipitation (MeDIP) data analysis pipelines to dissect pancreatic-cancer-related pathways and biological processes that included cell adhesion, growth factor, receptor activity, signaling, transcription and differentiation from human cell lines as well as normal and tumor-derived human pancreatic tissues. Designed and implemented association analysis of tag SNPs for KLK3 locus on chromosome 19q13.33 in 3,522 prostate cancer cases and 3,338 controls with prostate cancer susceptibility and prostate-specific antigen (PSA) levels. This led to identification of variant (rs17632542) that introduced non-synonymous amino acid change in KLK3 protein with predicted benign or neutral functional impact. Managed several large-scale genomic data sets. Developed scripts for data manipulation and analysis in Python, Perl, C, Java, R, Stata, MatLab, SAS and MySQL. Proficient in using several bioinformatics tools and databases. Created, organized and directed cross-functional teams and several multi-disciplinary scientific projects. Consistently completed initiatives in timely manner and within allocated resources.
Led analysis of several microarray data sets from different platforms including image analysis, quality assessment, normalization and different statistical tests. Identified TXNIP as novel regulator of glucose uptake in periphery of human body by acting as glucose and insulin-sensitive switch. Determined key role of TXNIP in defective glucose homeostasis preceding overt type 2 diabetes mellitus (T2DM). Discovered pathophysiological role of HNF1B and THADA in T2DM etiology by initiating innovative approach by compiling several GWAS data sets with gene expression profiling data from human tissues relevant to T2DM. Investigated molecular mechanisms associated with maximal oxygen uptake and type 1 fibers in human skeletal muscle by integrating different microarray data to classify high expression of genes involved in oxidative phosphorylation (OXPHOS) pathway associated with good muscle fitness. Explicitly determined that expression of NDUFB5 and ATP5C1 in skeletal muscle decreased with aging and increased with exercise training.