Dr. Casey Mueller
Assistant Professor, CSUSM, Biology
Lab Website
Thermal physiology: exploring themes of development and variability in different animal models
Tuesday, Dec. 3, 12 PM, AHF 153 (Torrey Webb Room)
Abstract: Dr. Mueller will present some of her lab’s data on copepods, as well as some work on chorus frogs and possibly rainbow trout. Many of the ideas can be applied to marine organisms.
Monday, December 2, 2019
QCB Colloquium | Dr. Shilpa Kobren
Dr. Shilpa Kobren
Research Fellow in Biomedical Informatics, Harvard Medical School
Research Profile
Uncovering genes with significantly perturbed functionalities in cancer
Thursday, Dec. 5, 2 PM, RRI 101
Abstract: A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. This is a difficult task as there is substantial mutational heterogeneity across tumors, and only a small subset of the numerous mutations in a given tumor may be functionally relevant for the disease. In my talk, I will introduce our newly developed, unified analytical framework that enables rapid integration of multiple sources of information in order to identify cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. Our method PertInInt combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions or small molecules with domain, evolutionary conservation and gene-level mutation data. When applied to 10,037 tumor samples across 33 cancer types, PertInInt efficiently uncovers both known and newly predicted cancer genes. Importantly, our analytical integration of data allows PertInInt to simultaneously reveal whether interaction potential or other molecular functionalities are disrupted, thereby enabling valuable insights that may help guide personalized cancer treatments. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in binding residues and functional domains in cancer genes, and implicates interaction perturbation as a pervasive cancer driving event.
Research Fellow in Biomedical Informatics, Harvard Medical School
Research Profile
Uncovering genes with significantly perturbed functionalities in cancer
Thursday, Dec. 5, 2 PM, RRI 101
Abstract: A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. This is a difficult task as there is substantial mutational heterogeneity across tumors, and only a small subset of the numerous mutations in a given tumor may be functionally relevant for the disease. In my talk, I will introduce our newly developed, unified analytical framework that enables rapid integration of multiple sources of information in order to identify cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. Our method PertInInt combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions or small molecules with domain, evolutionary conservation and gene-level mutation data. When applied to 10,037 tumor samples across 33 cancer types, PertInInt efficiently uncovers both known and newly predicted cancer genes. Importantly, our analytical integration of data allows PertInInt to simultaneously reveal whether interaction potential or other molecular functionalities are disrupted, thereby enabling valuable insights that may help guide personalized cancer treatments. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in binding residues and functional domains in cancer genes, and implicates interaction perturbation as a pervasive cancer driving event.
QCB Colloquium | Dr. Pei Wang
Dr. Pei Wang
Professor, Icahn School of Medicine at Mount Sinai, Genetics and Genomic Sciences
Lab Website
Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity
Monday, Dec. 2, 2 PM, RRI 101
Abstract: Tumor tissue samples often contain an unknown fraction of normal cells. This problem well known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between normal and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose TSNet a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. The advantage of TSNet over existing methods ignoring TPH is illustrated through extensive simulation examples. We then apply TSNet to estimate tumor specific co-expression networks based on ovarian cancer expression profiles. We identify novel co-expression modules and hub structure specific to tumor cells
Professor, Icahn School of Medicine at Mount Sinai, Genetics and Genomic Sciences
Lab Website
Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity
Monday, Dec. 2, 2 PM, RRI 101
Abstract: Tumor tissue samples often contain an unknown fraction of normal cells. This problem well known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between normal and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose TSNet a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. The advantage of TSNet over existing methods ignoring TPH is illustrated through extensive simulation examples. We then apply TSNet to estimate tumor specific co-expression networks based on ovarian cancer expression profiles. We identify novel co-expression modules and hub structure specific to tumor cells
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