Date |
Speaker |
PI/Lab/Host |
Topic & Abstract |
Time (if other than 10am) |
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12/2/20 |
Vijay Tiwari Asst. Professor |
Plant Science & Landscape Architecture, UMD |
TBA
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11/12/20 |
Prof. Megan Fritz |
UMD Dept. of Entomology |
Title: Selection and Architectural Shifts in the Genomes of Agriculturally and Medically Important Insect
Abstract: Humans are sculptors of the natural world, making sweeping changes to the landscape. This places extraordinary pressure on plant and animal communities to adapt to novel conditions. Under these circumstances, a number of evolutionary processes act to shape the genomic architecture of species in these communities, including insects. Agriculturally and medically important insects are frequently the targets of strong, human-imposed selection, when we modify their habitats or manage their populations. My research program sheds light on the ways in which human activities impact genomic variation in insects, specifically focusing on the agricultural pest, Helicoverpa zea, and the medically important mosquito, Culex pipiens. Here, I qualitatively and quantitatively describe the shifts in genome architecture and gene expression that have occurred in these species in association with human-mediated environmental modifications. I then consider the ways in which knowledge of these genomic changes can be exploited to boost agricultural productivity and reduce vector-borne disease transmission.
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11/5/20 |
Jamshed Khan |
Patro's Lab |
TBA
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10/22/20 |
Dr. Justin Zook |
NIST |
Genome in a Bottle Benchmarks for Challenging Genome Regions
Abstract:
The NIST-led Genome in a Bottle Consortium has developed widely used benchmarks for human genome sequencing and variant calling. Recently, we’ve used long reads and new de novo assembly and AI-based methods to expand these benchmarks to increasingly challenging regions of the genome such as segmental duplications and the Major Histocompatibility Complex (MHC). These benchmarks were used in the precisionFDA Truth Challenge V2 to encourage innovation in variant calling from short and long reads in these challenging regions, as well as in the Human Pangenome Reference Consortium to benchmark diploid assembly methods. We’re now working on using diploid assembly methods and work from the Telomere-to-Telomere Consortium to expand these benchmarks to some of the most challenging, repetitive regions of the genome. |
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10/15/2020 |
Jason Fan |
Patro Lab |
Matrix (Factorization) Reloaded: Flexible Methods for Imputing Genetic Interactions with Cross-Species and Side Information
Abstract:
Motivation: Mapping genetic interactions (GIs) can reveal important insights into cellular function, and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker’s yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data is scarce.
Results: We address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein-protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker’s and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost.
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