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Research Topics

Broad advances in sequencing have radically transformed the scale and nature of genetic studies making it possible to analyze genomic changes across species, individuals, cell-types, and as mutations accrue and are subject to natural selection. Diverse phenotypic datasets have also grown rapidly, not only for sequencing-based assays such as gene expression and protein-nucleic acid interactions, but also other types including clinical and drug-screening investigations. My lab is interested in computational and mathematical approaches to understand how genomes function and evolve and to make these findings clinically relevant. We develop and use techniques from a variety of disciplines, including data science, evolutionary modeling, and biophysics. We are currently focused in two major areas: 1) Computational Approaches for Cancer Genomics, and 2) Gene Regulation. These projects involve collaborations with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and a number of outside groups.

Computational Approaches for Cancer Genomics

Our lab focuses on understanding cancer using patient-derived xenografts, a model system in which human tumors are engrafted and studied in NSG mice. JAX has developed >400 such models from cancer types including breast, lung, bladder, and others, and these are a community wide resource. Our lab is involved in a number of studies using these models to understand the genetic drivers of cancer and drug resistance, with a particular focus on tumor heterogeneity and evolution. Within JAX we work closely with groups studying xenografts, including the Bult (JAX-MG), Liu (JAX-GM), and Lee (JAX-MG) labs, and we also work with a number of other groups in projects on cancer genomics. These projects include studies to identify drivers of drug susceptibility in triple negative breast cancers (Menghi et al 2016) and cancer evolution in response to chemotherapy.


As of September 2017, our lab runs the NCI PDXNet Data Commons and Coordination Center together with our colleagues at Seven Bridges Genomics. In this and other projects (Bais et al 2017), our lab has been one of the leading adopters of cloud computing approaches for large scale data and workflow sharing for enhancing cancer genomics data analysis.

The lab also broadly studies evolutionary and ecological processes in a variety of cancer systems. Recent projects have included the inference of intratumoral evolution through large scale data mining across thousands of cancer datasets (Noorbakhsh et al 2017) and investigations into immune and stromal introgression across cancer types (Chae et al 2018).

Gene Regulation

Our lab also studies gene regulation at both the RNA and DNA levels. For RNA, our projects have included regulation of translation, protein-RNA binding, and splicing. For example, in collaboration with Prof. Susan Ackerman (JAX-MG) we have identified and characterized a mutation in a tRNA as a driver for neurodegeneration and shown that this phenotype is mediated by specific translational pausing at the codons complementary to the tRNA anticodon (Ishimura et al 2014; Ishimura et al 2016). This was the first tRNA mutation found to have a phenotypic consequence in a mammal. Another current interest is how proteins interact with RNAs to achieve specific binding. In this area, we have developed approaches to identify functional elements in RNA based on functional genomic, structural, algorithmic, and high-throughput sequencing approaches (Dotu et al 2018; Zarringhalam et al 2012). The lab has been studying the functions and neutral evolutionary behavior of synonymous sites in coding sequences for more than a decade (Chuang and Li 2004; Chin et al 2005). We have shown for example that coding sequences are replete with binding sites for microRNAs, as well as other types of functional sequences such as exonic splicing enhancers. Such sites exhibit a strong selective pressure on the synonymous sites of coding regions (Kural et al 2009; Ding et al 2012; Ritter et al 2012).

Our lab also studies gene regulation at the DNA level through collaborations with Prof. Yijun Ruan (JAX-GM) by analyzing 3D interactions across the genome, including studies to elucidate the relationship between genome evolution and 3D structure (Grzeda et al 2014).

Other Interests

Much of the lab's research has grown out of early studies in molecular evolution and statistical physics, and we have explored a variety of problems in these areas. For example, we have characterized the relative importance of cis- and trans- regulatory evolution on the functional behavior of enhancers (Ritter et al 2010) and also developed tools to organize the functions of CNEs (, Persampieri et al 2008) .
A related interest has been the evolution of mutational processes across species. One puzzle is why mutation rates are uniform in some species, such as the sensu stricto yeasts, while rates vary by location in other species, such as mouse and human. We have found that all mammalian species have regional mutation biases, typically on a scale of several megabases. In contrast, all yeasts have uniform mutation rates, with the exception of the Candida clade (Fox et al 2008; Chuang and Li 2004; Chuang and Li 2007; Chin, Chuang, and Li 2005). In species where the mutation rate is non-uniform, we have studied what structural or sequence features affect mutation rates, and whether gene locations have evolved to make use of mutational heterogeneity. Other prior interests have been studies into the dynamics of translocation of polymers through a nanopore (Chuang et al, Phys Rev E 2001) and the thermodynamic stability of protein folds (Chuang et al, Phys Rev Lett 2001).

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