NIH VideoCast - Deciphering Cancer Genomes and Networks

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Deciphering Cancer Genomes and Networks

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Air date:	Wednesday, February 5, 2020, 3:00:00 PM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 523 (168 Live, 355 On-demand)
Category:	WALS - Wednesday Afternoon Lectures
Runtime:	01:00:59
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	NIH Director's Wednesday Afternoon Lecture Series Large-scale cancer genome sequencing consortia, such as TCGA, have provided a huge influx of somatic mutation data across large cohorts of patients. Understanding how these observed genetic alterations give rise to specific cancer phenotypes is a major aim of cancer genomics. This is challenging because numerous somatic mutations occur in each cancer genome, but only a subset are cancer-relevant; further, there is a high degree of mutational heterogeneity across individuals. Fortunately, the large and diverse biological datasets collected over the past few decades—including genome sequences across organisms and healthy individuals, protein structural data and interaction networks—provide a rich context within which to interpret cancer mutational data. In this talk, I will overview integrative computational methods my group has developed to interpret cancer mutational data, with an emphasis on identifying interactions perturbed in cancers. For more information go to https://oir.nih.gov/wals

NLM Title:	Deciphering cancer genomes and networks / Mona Singh.
Author:	Singh, Mona. National Institutes of Health (U.S.),
Publisher:
Abstract:	(CIT): Large-scale cancer genome sequencing consortia, such as TCGA, have provided a huge influx of somatic mutation data across large cohorts of patients. Understanding how these observed genetic alterations give rise to specific cancer phenotypes is a major aim of cancer genomics. This is challenging because numerous somatic mutations occur in each cancer genome, but only a subset are cancer-relevant; further, there is a high degree of mutational heterogeneity across individuals. Fortunately, the large and diverse biological datasets collected over the past few decades--including genome sequences across organisms and healthy individuals, protein structural data and interaction networks--provide a rich context within which to interpret cancer mutational data. In this talk, I will overview integrative computational methods my group has developed to interpret cancer mutational data, with an emphasis on identifying interactions perturbed in cancers.
Subjects:	Computational Biology Gene Regulatory Networks Genes, Neoplasm--genetics Genome, Human Neoplasm Proteins--metabolism Protein Interaction Maps
Publication Types:	Lecture Webcast

NLM Classification:	QZ 210
NLM ID:	101763746
CIT Live ID:	35119
Permanent link:	https://videocast.nih.gov/watch=35119

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