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Deciphering Cancer Genomes and Networks
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Deciphering Cancer Genomes and Networks
Air date: Wednesday, February 5, 2020, 3:00:00 PM
Category: WALS - Wednesday Afternoon Lectures
Runtime: 01:00:59
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.
Author: Mona Singh, Ph.D., Lewis Sigler Institute for Integrative Genomics
Air date: Wednesday, February 5, 2020, 3:00:00 PM
Category: WALS - Wednesday Afternoon Lectures
Runtime: 01:00:59
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.
Author: Mona Singh, Ph.D., Lewis Sigler Institute for Integrative Genomics
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