Regulatory Genomics and Epigenomics

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source: Simons Institute 2016年3月11日
Regulatory Genomics and Epigenomics
Mar. 7 – Mar. 10, 2016
Regulatory Genomics and Epigenomics aim to understand the way gene expression is controlled in cells by understanding the sequence elements and organization that govern this expression. Over the last decade this field has been revolutionized by new experimental data stemming from massively parallel sequencing technologies. Classical computational biology techniques, such as algorithms for motif discovery at the sequence level, are facing challenges on a new scale. At the same time, diverse data types need to be analyzed in an integrative manner, leading to challenging machine learning problems on large and noisy data sets. The long term goal is the development of models explaining how DNA sequence is read out differently under different circumstances, with the static sequence thus giving rise to highly dynamic responses.
For more information, please visit https://simons.berkeley.edu/workshops/genomics2016-2.
These presentations were supported in part by an award from the Simons Foundation.

Algorithms for Single Cell RNAseq Analysis 40:33 Serafim Batzoglou, Stanford University https://simons.berkeley.edu/talks/ser...
Spectral Algorithms for Learning HMMs and Tree HMMs for Epigenetics Data 36:49
Accurate, Fast, and Model-Aware Transcript Expression Quantification with Salmon 26:31
Reconstructing Dynamic Signaling and Regulatory Networks 33:12
Learning Feature-Based Protein-DNA Recognition Models from SELEX Data 31:47
Beyond Enhancer Modularity: Locus-level Control of Gene Expression in Development 42:52
Co-occupancy Networks for Histone Modifications and Chromatin Associated Proteins 42:20
Scoring Transcript Variation in Single Cell RNA-seq Data 37:29
Mapping Nucleosome Positions Using DNase 35:25
Computational Genomics of Post-Transcriptional Gene Regulation 40:09
Deep Learning Frameworks for Regulatory Genomics and Epigenomics 38:15
Genome in 3D: Modeling Chromosome Organization 35:13
Understanding DNA-binding Preferences of Transcription Factor Homologs 36:34
Challenges in Sequence-to-Expression Modeling 36:30
Modeling Gene Expression and Chromatin State in Terms of Regulatory Sites 37:04
Selecting Genomics Assays 33:30
Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments 37:03
Personal Transcription Factor Binding Site Mutations Point to Personal Medical Histories 38:44
Telomere Length, Nature and Nurture 37:26
Hierarchical Regulatory Domain Inference from Hi-C Data 34:29
Analysis Methods for Single Cell RNA-seq with Application to T-cell Function 34:04
Genome-wide Prediction of Enhancers and Their Target Genes Using ENCODE Data 34:52
Utilizing de Bruijn Graphs in Universal Sequence Design for Discovery of Regulatory Elements 31:52
Modelling Gene Expression Dynamics with Gaussian Processes 34:02
Discovery of Transcription Factor Binding Motifs from Large Sequence Sets 40:48
Chromatin Dynamics and Gene Regulation During Motor Neuron Programming 27:16

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