A long-time focus of my lab has been discovery and interpretation of large-scale genomic and transcriptomic alterations in tumor cells. Our algorithmic methods for genomic structural variation discovery, including VariationHunter, CommonLAW, DeStruct and NovelSeq, were the first with the ability to handle novel insertions, deletions, inversions and duplications in repetitive regions of the human genome. I am currently interested in applying our algorithmic techniques to exact genotyping of highly repetitive, structurally variant genes. These include genes involved in drug metabolism – for which my group has developed Cypiripi and Aldy methods, and the immune system genes  for which my group has developed Immunotyper and Geny methods. My lab has also contributed to the identification and quantification of transcriptomic aberrations, in particular gene fusions, as well as genic inversions, duplications and deletions in cancer samples. Leading computational methods we have developed include DeFuse, NFuse, Comrad MiStrVar and SVICT (which handles circulating cell-free tumor DNA data).

Our current focus area is tumor heterogeneity and progression inference, especially by the use of single-cell sequencing and spatial/time series sequencing (for which we have developed CITUP, CTP-Single, Remix-T, BSCITE, PhISCS, CONETT, PhISCS-BnB, DeepT, HUNTRESS, Sgootr, Detopt and others). We have also worked on network-aided, integrative analysis of genomic and transcriptomic sequence data from tumor samples (Hit’nDrive and cdCAP). We have several additional interests within "algorithmic biology" including (i) read alignment and variant calling (e.g. for reads from repetitive regions of the genome – mrFAST, mrsFAST, drFAST and lordFAST, or reads extracted from cell free tumor DNA - SINVICT),  (ii) genomic data compression (SCALCE, DeeZ and AssemblTrie), (iii) secure/privacy preserving genomic sequence analysis (PrivStrat, SkSES, SMac, TX-Phase) and (iv) metagenomic binning (CAMMiQ).