Re: REMINDER: Information needed for your talk at UCB Center for Computational Biology - Thurs, Dec 12

Hi Mark,

Many thanks for sending us your abstract.

We are working on scheduling the meetings for you with our faculty and I'll send you the schedule for your visit by next Tuesday.

Best,

Xuan

On Wed, Dec 4, 2019 at 11:40 AM Mark Gerstein <mark@gersteinlab.org> wrote:

Title: Topics in Cancer Genomics

Abstract: My talk will focus on how to leverage thousands of cancer
genomes and functional genomics datasets to discover
disease-associated regulators and variations. First, I will go over
the ENCODE annotation related to the cancer genome. I will show how
extended gene annotation allows us to place oncogenic transformations
in the context of a large cell space; here, many normal-to-tumor
transitions move towards a stem-like state, while oncogene knockdowns
show an opposing trend. Second, I will look at how these can be recast
into a comprehensive regulatory network of both transcription factors
and RNA-binding proteins (TFs and RBPs). I will showcase their value
by highlighting how SUB1, a previously uncharacterized RBP, drives
aberrant tumor expression and amplifies the effect of the well-known
oncogenic TF MYC. Third, I will describe a workflow to prioritize key
elements and variants. I will showcase the application of this
prioritization to somatic burdening, cancer differential expression
and GWAS (LARVA, MOAT & uORF tools). Finally, I will put all these
methods together through application to the PCAWG dataset. In this
analysis, we integrate genomic annotations and predicted functional
impact scores to quantify the overall burdening of various elements in
cancer genomes. We also show how the overall functional burdening of
various genomic elements correlates with patient survival time and
tumor clonality. Finally, we adapted an additive-effects model from
complex-trait studies to show that the aggregated effect of putative
passengers, including undetected weak drivers, provides significant
additional power (~12% additive variance) for predicting cancerous
phenotypes, beyond identified driver mutations. Furthermore, this
framework allowed us to estimate potential weak-driver mutations in
samples lacking any well-characterized driver alterations.

==

i0ucb19

--

Xuan Quach

Executive Director
Center for Computational Biology
108 Stanley Hall, UC Berkeley, 94720-3220
ph:  510.666.3342
fax: 510.666.3399

http://ccb.berkeley.edu 

Re: REMINDER: Information needed for your talk at UCB Center for Computational Biology - Thurs, Dec 12

Title: Topics in Cancer Genomics

Abstract: My talk will focus on how to leverage thousands of cancer
genomes and functional genomics datasets to discover
disease-associated regulators and variations. First, I will go over
the ENCODE annotation related to the cancer genome. I will show how
extended gene annotation allows us to place oncogenic transformations
in the context of a large cell space; here, many normal-to-tumor
transitions move towards a stem-like state, while oncogene knockdowns
show an opposing trend. Second, I will look at how these can be recast
into a comprehensive regulatory network of both transcription factors
and RNA-binding proteins (TFs and RBPs). I will showcase their value
by highlighting how SUB1, a previously uncharacterized RBP, drives
aberrant tumor expression and amplifies the effect of the well-known
oncogenic TF MYC. Third, I will describe a workflow to prioritize key
elements and variants. I will showcase the application of this
prioritization to somatic burdening, cancer differential expression
and GWAS (LARVA, MOAT & uORF tools). Finally, I will put all these
methods together through application to the PCAWG dataset. In this
analysis, we integrate genomic annotations and predicted functional
impact scores to quantify the overall burdening of various elements in
cancer genomes. We also show how the overall functional burdening of
various genomic elements correlates with patient survival time and
tumor clonality. Finally, we adapted an additive-effects model from
complex-trait studies to show that the aggregated effect of putative
passengers, including undetected weak drivers, provides significant
additional power (~12% additive variance) for predicting cancerous
phenotypes, beyond identified driver mutations. Furthermore, this
framework allowed us to estimate potential weak-driver mutations in
samples lacking any well-characterized driver alterations.

==

i0ucb19

Re: reply

Hello, please confirm if you receive my previous email to you. Thank you. Mrs Lister.

Fwd: genopri abstract

https://broadinstitute.swoogo.com/ga4gh7thplenary/agenda
then
https://www.genopri.org/program.html

9:30 – 10:15am Presentation Session 3: "Models" (Session Chair: Xiaoqian Jiang)

Enhancing open data sharing for functional genomics experiments:
Measures to quantify genomic information leakage & identify file
formats for privacy preservation

Mark Gerstein, Yale University (USA)

Abstract: Functional genomics experiments on human subjects present a
privacy conundrum. On one hand, many of the conclusions we infer from
these experiments are not tied to the identity of individuals but
represent universal statements about biology and disease. On the other
hand, the raw sequencing reads or the phenotypic information inferred
from these experiments can leak information about patients' variants,
which presents privacy challenges in terms of data sharing. There is a
great desire to share data as broadly as possible. Therefore,
measuring the amount of variant information leaked in a variety of
experiments is a key first step in protecting private information. In
this study, we propose metrics to quantify private information leakage
in functional genomics data, linking attacks to validate the proposed
metrics and file formats that maximize the potential for data sharing
while protecting individuals' private information.

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Abstract for talk at the MRC LMB Cambridge

Personal Genomics & Data Science 

In this seminar, I will discuss issues in transcriptome analysis. I 
will first talk about some core aspects - how we analyze the activity 
patterns of genes in human disease. In particular, I will focus on 
disorders of the brain, which affect nearly a fifth of the world's 
population. Robust phenotype-genotype associations have been 
established for a number of brain disorders including psychiatric 
diseases (e.g., schizophrenia, bipolar disorder). However, 
understanding the molecular causes of brain disorders is still a 
challenge. To address this, the PsychENCODE consortium generated 
thousands of transcriptome (bulk and single-cell) datasets from 1,866 
individuals. Using these data, we have developed a set of 
interpretable machine learning approaches for deciphering functional 
genomic elements and linkages in the brain and psychiatric disorders. 
In particular, we deconvolved the bulk tissue expression across 
individuals using single-cell data via non-negative matrix 
factorization and found that 
differences in the proportions of cell types explain >85% of the 
cross-population variation. Additionally, we developed an 
interpretable deep-learning model embedding the physical regulatory 
network to predict phenotype from genotype. Our model uses a 
conditional Deep Boltzmann Machine architecture and introduces lateral 
connectivity at the visible layer to embed the biological structure 
learned from the regulatory network and QTL linkages. Our model 
improves disease prediction (by 6-fold compared to additive polygenic 
risk scores), highlights key genes for disorders, and allows 
imputation of missing transcriptome information from genotype data 
alone. In the second half of the talk, I will look at the "data 
exhaust" from transcriptome analysis - that is, how one can find 
additional things from this data than what is necessarily intended. 
First, I will focus on genomic privacy: How looking at the 
quantifications of expression levels can potentially reveal something 
about the subjects studied, and how one can take steps to protect 
patient anonymity. Next, I will look at how the social activity of 
researchers generating transcriptome datasets in itself creates 
revealing patterns in the scientific literature. 

==

i0lon19-mrc

Abstract for talk at the UNIVERSITY OF BERN

Abstract: 

My talk will focus on how to leverage thousands of functional genomics datasets to deeply annotate the disease genome and perform data mining to discover disease-associated regulators and variations.

 

First, I will introduce our computational efforts to perform large-scale integration to accurately define distal and proximal regulatory elements (MatchedFilter) and then show how our extended gene annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend.

 

Second, I will look at our comprehensive regulatory networks of both transcription factors and RNA-binding proteins (TFs and RBPs). I will showcase their value by highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of the well-known oncogenic TF MYC.

 

Third, I will introduce a workflow to prioritize key elements and variants. I will showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS (LARVA, MOAT & uORF tools). Targeted validations of the prioritized regulators, elements and variants demonstrate the value of our annotation resource.

 

Finally, I will put all these methods together through application to kidney and prostate cancers.

==

i0lon19-bern

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Re: Thank you!

TITLE: Genomics & Data Science

ABSTRACT:

Data science is currently popular because it allows the extraction of
useful information and commercial value from large-scale datasets.
Here I will describe biomedical data science, particularly from the
framework of genomics, which is one of the leading sub-fields under
the data science umbrella. First, I will talk about the increase in
data generation and data availability in this discipline, and how
genomics has developed methods to cope with data growth, and how these
methods interrelate with data science in general. Then, I'll go
through a number of key applications of biomedical data science,
particularly in relation to drugs and therapeutics, and focus on
finding drug targets for neuropsychiatric disease. This application of
data science makes extensive use of biological regulatory networks and
I will show how one can analyze these networks, in terms of analogies
to social networks and hierarchies.

Fwd: Your upcoming Special Cell Circuits & Epigenomics seminar/visit on April 1st

TITLE:

Personal Genomics & Data Science

In this seminar, I will discuss issues in personal genome analysis.
In particular, I will focus on
disorders of the brain, which affect nearly a fifth of the world's
population. Robust phenotype-genotype associations have been
established for a number of brain disorders including psychiatric
diseases (e.g., schizophrenia, bipolar disorder). However,
understanding the molecular causes of brain disorders is still a
challenge. To address this, the PsychENCODE consortium generated
thousands of transcriptome (bulk and single-cell) datasets from 1,866
individuals. Using these data, we have developed a set of
interpretable machine learning approaches for deciphering functional
genomic elements and linkages in the brain and psychiatric disorders.
In particular, we deconvolved the bulk tissue expression across
individuals using single-cell data via non-negative matrix
factorization and non-negative least squares and found that
differences in the proportions of cell types explain >85% of the
cross-population variation observed. Additionally, we developed an
interpretable deep-learning model embedding the physical regulatory
network to predict phenotype from genotype. Our model uses a
conditional Deep Boltzmann Machine architecture and introduces lateral
connectivity at the visible layer to embed the biological structure
learned from the regulatory network and QTL linkages. It
improves disease prediction (by 6-fold compared to additive polygenic
risk scores), highlights key genes for disorders, and allows
imputation of missing transcriptome information from genotype data
alone.

In the second half of the talk, if there's time, I'll discuss various data
science issues in drug design, in particular in developing a predictor that
gives one's differential sensitivity to a drug, taking into account his or her
personal variants.

==
i0brd19

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RE: Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Thank you, Mark! Yes, this will be a great topic for the audience. Look forward to meeting you next month. Feel free to email or contact me should you need any further help. My cell phone is 317.622.8881.

Jake

-----Original Message-----
From: Mark Gerstein <mark@gersteinlab.org>
Sent: Thursday, February 14, 2019 7:07 PM
To: Chen, Jake Y <jakechen@uab.edu>
Cc: glabstracts.mbglab@blogger.com
Subject: Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Talk Title:

Brain Genomics

Abstract:

Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with
>88% reconstruction accuracy). It also allows the building of a gene
regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

URL:

resource.psychencode.org

Abstract for keynote talk at MCBIOS '19, Birmingham, AL Mar 28-30th (* i0mcbios *)

Talk Title:

Brain Genomics

Abstract:

Despite progress in defining genetic risk for psychiatric disorders,
their molecular mechanisms remain elusive. Addressing this, the
PsychENCODE Consortium has generated a comprehensive online resource
for the adult brain across 1866 individuals. The PsychENCODE resource
contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and
topologically associating domains; single-cell expression profiles for
many cell types; expression quantitative-trait loci (QTLs); and
further QTLs associated with chromatin, splicing, and cell-type
proportions. Integration shows that varying cell-type proportions
largely account for the cross-population variation in expression (with
>88% reconstruction accuracy). It also allows the building of a gene
regulatory network, linking genome-wide association study variants to
genes (e.g., 321 for schizophrenia). We embed this network into an
interpretable deep-learning model, which improves disease prediction
by ~6-fold versus polygenic risk scores and identifies key genes and
pathways in psychiatric disorders.

URL:

resource.psychencode.org