My group continued to work on computational methods to study the dynamics of biological networks, impact of genetic and structural My group continued to develop and apply computational methods to study the dynamics of biological networks, impact of genetic and structural variation on gene expression and phenotype with emphasis on studies related to cancer and its heterogeneity. We also continued our research on the role of DNA conformational dynamics for gene regulation, and methods for analysis of HT-SELEX data. In particular, we worked on new computational methods to delineate genetic underpinnings of cancer and interactions between them. We focused on delineating properties on mutational landscape of cancer. In particular, we utilized our recently developed Weighted Sampling Mutual Exclusivity (WeSME) method to estimate statistical significance of Mutual Exclusivity (ME) relation (1) and our novel optimization technique, BeWith, to investigate various aspects of the cancer mutational landscape, leading to uncovering relationships between mutated gene modules, cancer subtypes, and mutational signatures. A paper describing preliminary results of this work has been invited for an oral presentation at RECOMB 2017 a premier conference in Computational Biology. We also continued developing our comprehensive software package, AptaTools, for analysis of HT-SELEX derived Aptamers - synthetic. We are preparing the next release of the software for late 2017. In addition, our expertise in this area has led to the collaborative studies with experimental groups as for example the work reported in (2). We also developed a method to construct RNA-drug conjugates. Aptamer-drug conjugates (ApDCs) have the potential to improve the therapeutic index of traditional chemotherapeutic agents due to their ability to deliver cytotoxic drugs specifically to cancer cells while sparing normal cells. One of the conjugated designed by us has been to obtain the conjugation of cytotoxic drugs to an aptamer previously described by our group, the pancreatic cancer RNA aptamer P19. To this end, P19 was incorporated with gemcitabine and 5-fluorouracil (5-FU), or conjugated to monomethyl auristatin E (MMAE) and derivative of maytansine 1 (3). We are also continuing our long-standing collaboration with Brian Oliver's group on gene regulation in Drosophila. In particular we used the data generated in (4) for constructing sex specific gene regulatory networks (GRN) for adult drosophila. GRN describe regulatory relationship between transcription factors and their target genes. Methods to infer GRNs are typically context-agnostic based on evidence collected in many different conditions, disregarding the fact that regulatory programs are conditioned on tissue type, developmental stage, sex, and other factors. We focused our studies on developing a novel network inference method NetREX, that given a context-agnostic network as a prior and context-specific expression data (e.g. expression in an adult fly), constructs a context-specific GRN by rewiring the prior network. We reported the preliminary results related to the method development on RECOMB 2017, one of the most prestigious conferences in Computational Biology, where this work has been awarded the best paper award. Several new methodological advancements introduced in this contributed to the success of NetREX. In particular, one of the key contributions is the development of a convergent algorithm that can estimate unknown TFAs while rewiring the prior network based the recently proposed PALM framework. Finally, we are also continuing our long standing collaboration with David Levens group focusing on the role of DNA conformational dynamics in gene. DNA in cells is predominantly B-form double helix. Though certain DNA sequences in vitro may fold into other structures, such as triplex, left-handed Z form, or quadruplex DNA, the stability and prevalence of these structures in vivo were not known. Using computational analysis of sequence motifs, RNA polymerase II binding data, and genome-wide potassium permanganate-dependent nuclease footprinting data, we mapped thousands of putative non-B DNA sites at high resolution in mouse B cells. Computational analysis associated these non-B DNAs with particular structures and indicates that they form at locations compatible with an involvement in gene regulation. Further analyses supported the notion that non-B DNA structure formation influences the occupancy and positioning of nucleosomes in chromatin. These results, published in Cell Systems (5) suggested that non-B DNAs contribute to the control of a variety of critical cellular and organismal processes.
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