Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.Our laboratory is located in the new Department of Computational Medicine & Bioinformatics and is also affiliated with the Department of Human Genetics.

As Team Lead of the the Bioinformatics and Medical Diagnostics Group in the Molecular Pathology Research Unit, my research was focused on developing analytical methods and tools to facilitate the study of genetics, genomics, and systems biology in concert with biochemical experiments as well as to advance personalized medicine and medical genetic diagnostic testing. Specific aims of this group included (1) analysis of next-generation and whole-genome sequencing data to identify pathogenic mutations and subchromosomal genomic alterations, (2) development of new analytical methods to assemble complete genomes of humans and other model organisms, (3) the development of experiments to predict the effect of copy number variation on specific metabolic pathways, and (4) transition of molecular and array-based technologies into new clinical genetic diagnostic tests in the Center for Advanced Molecular Diagnostics at Brigham and Women’s Hospital Pathology Department.

My postdoctoral research involved identifying genetic variation within and between species. We developed approaches to determine the extent of transposon mobilization between human and chimpanzees, and built models to assess what attributes of particular transposons (Alu) affect their ability to retrotranspose. We also developed a computational pipeline to identify insertion and deletion (INDEL) mutations using split-read alignments from resequencing data and applied it to almost 100 million human capillary trace sequences.