Graduate

Workshop in Biostatistics: Cellwalker: a network model to resolve gene regulatory elements in single cells

Topic: 
Cellwalker: a network model to resolve gene regulatory elements in single cells
Abstract / Description: 

TBA


 

Contact Katie Kanagawa for Zoom dial-in details. (kkanagaw@stanford.edu)

Date and Time: 
Thursday, April 22, 2021 - 2:30pm

Workshop in Biostatistics: Interrogating the Gut Microbiome: Estimation of Bacterial Growth Rates and Prediction of Biosynthetic Gene Clusters

Topic: 
Interrogating the Gut Microbiome: Estimation of Bacterial Growth Rates and Prediction of Biosynthetic Gene Clusters
Abstract / Description: 

The gut microbiome plays an important role in maintenance of human health. High-throughput shotgun metagenomic sequencing of a large set of samples provides an important tool to interrogate the gut microbiome. Besides providing footprints of taxonomic community composition and genes, these data can be further explored to study the bacterial growth dynamics and metabolic potentials via generation of small molecules and secondary metabolites. In this talk, Dr. Lee will present several computational and statistical methods for estimating the bacterial growth rate for metagenome-assembled genomes (MAGs) and for predicting all biosynthetic gene clusters (BGCs) in bacterial genomes. The key statistical and computational tools used include optimal permutation recovery based on low-rank matrix projection and improved LSTM deep learning methods to improve prediction of BGCs. He will demonstrate the application of these methods using several ongoing microbiome studies of inflammatory bowel disease at the University of Pennsylvania.


 

Contact Katie Kanagawa for Zoom dial-in details. (kkanagaw@stanford.edu)

Date and Time: 
Thursday, April 15, 2021 - 2:30pm

Workshop in Biostatistics: Leveraging local and polygenic signal for GWAS gene prioritization

Topic: 
Leveraging local and polygenic signal for GWAS gene prioritization
Abstract / Description: 

Prioritizing likely causal genes from GWAS data is a fundamental problem. There are many methods for GWAS gene prioritization, including methods that map candidate SNPs to their target genes, and methods that leverage patterns of enrichment from across the genome. In this talk, I will introduce a new method for leveraging genome-wide patterns of enrichment to prioritize genes at GWAS loci, incorporating information about genes from many sources. I will then discuss the problem of benchmarking gene prioritization methods, and I will describe a large-scale analysis to benchmark many different methods and combinations of methods on data from the UK Biobank. Our analyses show that the highest confidence can be achieved by combining multiple lines of evidence, and I will conclude by giving examples of genes prioritized in this way.


 

Contact Katie Kanagawa for Zoom dial-in details. (kkanagaw@stanford.edu)

Date and Time: 
Thursday, April 1, 2021 - 2:30pm

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