Ph.D., University of California, Berkeley 2005
Computational biology; human genetics; phylogenetics; copy number variation
There is increasing evidence that rare and unique mutations play a significant role in the etiology of many diseases such as autism, congenital heart disease, and cancer. We develop algorithms to identify these mutations from large, high throughput datasets comprising thousands of nuclear families, previously from high resolution CGH arrays and currently from targeted sequence data. We have developed methods for identifying de novo mutations by simultaneously genotyping the entire family and are currently focused on building algorithms to detect copy number variants and multi-scale genomic rearrangements.
Targeted capture protocols enrich coverage of the exome by fifty-fold; however, half of the sequence data collected is outside the targeted region. Even within the target, coverage is uneven on a scale that crosses orders of magnitude. Our copy number algorithms utilize all the data, compensating for coverage bias by normalizing across the family and population. Our copy number methods are based on read density; however, there are classes of mutations that require analysis at the level of the read. We are developing algorithms to identify insertions, deletions, inversions, transpositions, and other complex events.
Driven by the ease and availability of high throughput sequencing, vast datasets and novel protocols regularly challenge and exceed the capacity of “off the shelf” methods and software. These new needs require custom algorithms and models that address specific aims. For example, we are developing methods for determining parental phase from short-read sequence data generated either from single sperm cells or from fractions of the genome. Other projects include analysis of single cell RNA, phylogenetic reconstruction from sparse datasets, and disentangling the superposition of multiple phylogenies using “tree-like” networks.
Levy., D., Ronemus., M,, Yamrom, B., Lee, Y,H,, Leotta, A., Kendall, J., Marks, S., Lakshmi, B., Pai, D., Ye, K., Buja, A., Krieger, A., Yoon, S., Troge, J., Rodgers, L., Iossifov, I., and Wigler M 2011. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 70: 886–897.
Gilman, S.R., Iossifov, I., Levy, D., Ronemus, M., Wigler, M., and Vitkup, D. 2011. Rare de novo variants associated with autism implicate a large functional network of genes involved information and function of synapses. Neuron 70: 898–907.
Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y-H., Narzisi, G., Leotta, A., Kendall, J., Grabowska, E., Ma, B., Marks, S., Rodgers, L., Stepansky, A., Troge, J., Andrews, Bekritsky, M., Pradhan, K., Ghiban, E., Kramer, M., Parla, J., Demeter, R., Fulton, L., Fulton, R.S., Magrini, V.J., Ye, K., Darnell, J.C., Darnell, R.B., Mardis, E.R., Wilson, R.K., Schatz, M.C., McCombie, W.R., Wigler, M. 2012. De novo gene disruptions in children on the autistic spectrum. Neuron 74: 28–299.
Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., Muthuswamy, L., Krasnitz, A., McCombie, W.R., Hicks, J., and Wigler, M. 2011. Tumour evolution inferred by single-cell sequencing. Nature 472: 90–94.
Levy, D. and Pachter, L 2011. The neighbor-net algorithm. Advances in Applied Mathematics. 47: 240–258.