BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Peter Kharchenko (Harvard University) DTSTART:20210503T141000Z DTEND:20210503T144000Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/1 DESCRIPTION:Title: Bayesian segmentation of spatially resolved transcript omics data\nby Peter Kharchenko (Harvard University) as part of Comput ational Biology Symposium\n\n\nAbstract\nSpatial transcriptomics is an eme rging stack of technologies\, which adds spatial dimension to conventional single-cell RNA-sequencing. New protocols\, based on in situ sequencing o r multiplexed RNA fluorescent in situ hybridization register positions of single molecules in fixed tissue slices. Analysis of such data at the leve l of individual cells\, however\, requires accurate identification of cell boundaries. While many existing methods are able to approximate cell cent er positions using nuclei stains\, current protocols do not report robust signal on the cell membranes\, making accurate cell segmentation a key bar rier for downstream analysis and interpretation of the data. To address th is challenge\, we developed a tool for Bayesian Segmentation of Spatial Tr anscriptomics Data (Baysor)\, which optimizes segmentation considering the likelihood of transcriptional composition\, size and shape of the cell. T he Bayesian approach can take into account nuclear or cytoplasm staining\, however can also perform segmentation based on the detected transcripts a lone. We show that Baysor segmentation can in some cases nearly double the number of the identified cells\, while reducing contamination. Importantl y\, we demonstrate that Baysor performs well on data acquired using five d ifferent spatially-resolved protocols\, making it a useful general tool fo r analysis of high-resolution spatial data.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/1/ END:VEVENT BEGIN:VEVENT SUMMARY:Smita Krishnaswamy (Yale University) DTSTART:20210503T144500Z DTEND:20210503T151500Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/2 DESCRIPTION:Title: Geometric and Topological Approaches to Representation Learning in Biomedical Data\nby Smita Krishnaswamy (Yale University) as part of Computational Biology Symposium\n\n\nAbstract\nHigh-throughput\ , high-dimensional data has become ubiquitous in the biomedical\, health a nd social sciences as a result of breakthroughs in measurement technologie s and data collection. While these large datasets containing millions of o bservations of cells\, peoples\, or brain voxels hold great potential for understanding generative state space of the data\, as well as drivers of differentiation\, disease and progression\, they also pose new challenges in terms of noise\, missing data\, measurement artifacts\, and the so-call ed “curse of dimensionality.” In this talk\, I will cover data geometr ic and topological approaches to understanding the shape and structure of the data. First\, we show how diffusion geometry and deep learning can be used to obtain useful representations of the data that enable denoising (MAGIC)\, dimensionality reduction (PHATE)\, and factor analysis (Archetyp al Analysis Network) of the data. Next we will show how to learn dynamics from static snapshot data by using a manifold-regularized neural ODE-base d optimal transport (TrajectoryNet). Finally\, we cover a novel approach t o combine diffusion geometry with topology to extract multi-granular featu res from the data (Diffusion Condensation and Multiscale PHATE) to assist in differential and predictive analysis. On the flip side\, we also create a manifold geometry from topological descriptors\, and show its applicati ons to neuroscience. Together\, we will show a complete framework for expl oratory and unsupervised analysis of big biomedical data.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/2/ END:VEVENT BEGIN:VEVENT SUMMARY:Meromit Singer (Harvard Medical School) DTSTART:20210503T152000Z DTEND:20210503T155000Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/3 DESCRIPTION:Title: Utilizing coupled single-cell RNA-seq and TCR-seq to r eveal Th17 systemic dynamics during homeostasis and disease\nby Meromi t Singer (Harvard Medical School) as part of Computational Biology Symposi um\n\n\nAbstract\nIn this talk we will describe a systemic study of the tr anscriptional and clonal characteristics and function of Th17 cells throug hout multiple mouse organs\, as revealed by coupled single-cell RNA-seq an d TCR-seq\, and validated in follow-up experiments in the lab. We will des cribe how we utilized 84\,000 tissue Th17 cells profiled during homeostasi s and disease to characterize their heterogeneity\, plasticity\, and migra tion at homeostasis and during CNS autoimmunity. We discovered a homeostat ic Th17 cell population\, that is induced by the intestinal microbiota\, i s present in both lymphoid organs and the intestine\, and expresses IL-17. We discovered that during EAE this homeostatic population gives rise to a pathogenic Th17 cell population\, that migrates specifically through the draining lymph nodes and the spleen to the CNS\, and highly expresses a sp ecific subset of cytokines. \nIn this talk we will emphasize how coupled s ingle-cell RNA-seq and TCR data was used to generate hypotheses regarding cell subtype characterization and T cell clonality and migration\, and how such hypotheses were followed-up on experimentally.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/3/ END:VEVENT BEGIN:VEVENT SUMMARY:John Marioni (EMBL-EBI) DTSTART:20210503T155500Z DTEND:20210503T162500Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/4 DESCRIPTION:by John Marioni (EMBL-EBI) as part of Computational Biology Sy mposium\n\nAbstract: TBA\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/4/ END:VEVENT BEGIN:VEVENT SUMMARY:Uri Alon (Weizmann Institute) DTSTART:20210503T172500Z DTEND:20210503T175500Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/5 DESCRIPTION:Title: Design principles of hormone circuits\nby Uri Alon (Weizmann Institute) as part of Computational Biology Symposium\n\nAbstra ct: TBA\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/5/ END:VEVENT BEGIN:VEVENT SUMMARY:Eran Segal (Weizmann Institute) DTSTART:20210503T180000Z DTEND:20210503T183000Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/6 DESCRIPTION:Title: Harnessing big data for personalized medicine\nby Eran Segal (Weizmann Institute) as part of Computational Biology Symposium \n\n\nAbstract\nThe recent availability of diverse health data resources o n large cohorts of human individuals presents many challenges and opportun ities. I will present our work aimed at developing machine learning algori thms for predicting future onset of disease and identifying causal drivers of disease based on nationwide electronic health record data as well as d ata from high-throughput omics profiling technologies such as genetics\, m icrobiome\, and metabolomics. Our models provide novel insights into poten tial drivers of obesity\, diabetes\, and heart disease\, and identify hund reds of novel markers at the microbiome\, metabolite\, and immune system l evel. Overall\, our predictive models can be translated into personalized disease prevention and treatment plans\, and to the development of new the rapeutic modalities based on metabolites and the microbiome.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/6/ END:VEVENT BEGIN:VEVENT SUMMARY:Martin Hemberg (Brigham and Women’s Hospital) DTSTART:20210503T183500Z DTEND:20210503T190500Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/7 DESCRIPTION:Title: Searching for alien DNA – characterization of sequen ces that are not present in the DNA\nby Martin Hemberg (Brigham and Wo men’s Hospital) as part of Computational Biology Symposium\n\n\nAbstract \nNullomers and nullpeptides are short DNA or amino acid sequences that ar e absent from a genome or proteome\, respectively. One potential cause for their absence could be that they have a detrimental impact on an organism . Here\, we identified all possible nullomers and nullpeptides in the geno mes and proteomes of over thirty species and show that a significant propo rtion of these sequences are under negative selection. We assign nullomers to different functional categories (coding sequences\, exons\, introns\, 5’UTR\, 3’UTR\, regulatory regions and promoters) and show that nullom ers from coding sequences and promoters are most likely to be selected aga inst. Similarly\, we find that regulatory regions and transcription factor binding sites harbor more mutations resulting in nullomers than expected. Further analysis of coding regions also reveals specific pathways where m utations are more likely to result in nullomers or nullpeptides. Utilizing variants in the human population\, we annotate variant-associated nullome rs\, highlighting their potential use as DNA ‘fingerprints’.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/7/ END:VEVENT BEGIN:VEVENT SUMMARY:Elana Fertig (Johns Hopkins) DTSTART:20210503T191000Z DTEND:20210503T194000Z DTSTAMP:20260404T095547Z UID:ComputationalBiology/8 DESCRIPTION:Title: Uncovering hidden sources of transcriptional dysregula tion arising from inter- and intra-tumor heterogeneity.\nby Elana Fert ig (Johns Hopkins) as part of Computational Biology Symposium\n\n\nAbstrac t\nHeterogeneity poses a major challenge in translational research. For ex ample\, inter-tumor heterogeneity limits the biomarker discovery and intra -tumor heterogeneity enables therapeutic resistance. Moreover\, in some ca ncers driver mutations are insufficient to account for the widespread tran scriptional variation responsible for these outcomes. Thus\, new computati onal tools to model transcriptional variation are essential. To address th is we develop an innovative computational framework\, Expression Variation Analysis (EVA)\, to model transcriptional dysregulation in cancer. Briefl y\, EVA quantifies transcriptional heterogeneity for one set of samples or cells from one phenotype using the expected dissimilarity between pairs o f expression profiles. U-statistics theory can then quantify the statistic al significance of the difference in transcriptional heterogeneity between phenotypes. We apply EVA to perform a comprehensive characterization of t ranscriptional variation in head and neck squamous cell carcinoma (HNSCC). At a pathway level\, transcriptional variation in HNSCC tumors is higher than normal controls. Applying EVA to integrate ChIP-seq data with RNA-seq reveals that these pervasive transcriptional differences occur in enhance rs. Similarly\, applying EVA at a gene level to model splicing reveals mor e heterogeneity in transcript usage in tumor samples than normals. HPV- HN SCC tumors are unique in having mutations in genes that regulate the splic ing machinery\, and the HPV- tumors with these alterations have a greater number of dysregulated splice variants than those without. Nonetheless\, t he EVA analysis identifies a similar number of alternative splice variants in HPV+ as HPV- tumors suggesting an alternative mechanism of transcripti onal heterogeneity in HPV+ disease. Adapting EVA to single cell data demon strates that increased fibroblast composition is associated with greater v ariation in immune pathway activity in HNSCC. Moreover\, we observe greate r transcriptional heterogeneity in HNSCC primary tumors than lymph node me tastasis consistent with a clonal outgrowth. We demonstrate that the stati stical framework from EVA enables differential heterogeneity analysis in H NSCC ranging from pathway dysregulation\, splice variation\, epigenetic re gulation\, and single cell analysis. This algorithm provides a critical fr amework to model the hidden multi-molecular mechanisms underlying the comp lex patient outcomes that are pervasive in cancer.\n LOCATION:https://stable.researchseminars.org/talk/ComputationalBiology/8/ END:VEVENT END:VCALENDAR