[Colloquium] Re: REMINDER: 2/20 Talks at TTIC: Bo Li, UC Berkeley

[Mary Marre via Colloquium]

When:     Monday, February 20th at 11:00 am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526

Who:       Bo Li, UC Berkeley


Title:       *Taming Big Sequencing Data for RNA Biology*
*               From Transcript Abundance Estimation to ‘Epitranscriptomic’
Mark Detection*

Abstract:

Next generation sequencing (NGS) technology is one of the most phenomenal
genomics innovations in the past decades. It brings us a promising future
of genomic diagnosis and personalized treatment of human disease. The power
of NGS is reflected in its ability to measure almost any molecular signal
of interest using the following paradigm: 1) signal embedding; 2)
sequencing; 3) signal extraction. This talk introduces two of my works on
extracting signal from big sequencing data.



The first work addresses the problem of accurately estimating transcript
abundance from RNA-sequencing (RNA-Seq) data. Transcript abundance is the
relative measure of transcript copy number in cells. It is a fundamental
quantity in biology and has a huge impact on human health: studies have
shown that transcript abundances are often altered in disease conditions.
To extract abundance “signal” from RNA-Seq data, we develop RSEM, a
probabilistic learning software that enables accurate estimation of
transcript abundances. Since RSEM was released, it has been extensively
used around the world. RSEM achievements include citations over 2,300 and
adoption in big consortium projects such as TCGA and ENCODE. I will discuss
the computational challenges existed in developing RSEM and how we address
these challenges.



The second work introduces PROBer, a unified probabilistic framework for
epitranscriptomic mark detection. In a general sense, epitranscriptomics
refers to the study of RNA structure, RNA modification and RNA-protein
interaction at the transcriptome scale. These three aspects are important
for understanding the mechanism of alternative splicing whose disturbance
often results in diseases such as cancer and Parkinson’s disease. PROBer
can be viewed as a generalization of RSEM since it extracts both transcript
abundance and epitranscriptomic mark position information from the
data. Recent studies suggest that RNA structure, RNA modification and
RNA-protein interaction are interrelated and their interaction regulates
alternative splicing. To better illustrate the secret of alternative
splicing, experiments profiling these three aspects are likely to be
performed and analyzed together. Therefore, a general tool that could
extract signals of these three aspects, such as PROBer, will become an
emerging need.



*Nature Methods* recently chose epitranscriptome analysis as method of the
year. In the end of my talk, I will discuss the bioinformatics challenges
and opportunities in epitranscriptome analysis, and my future research plan.


Bio:
Dr. Bo Li is a Postdoctoral researcher in the Center for RNA Systems
Biology at the University of California, Berkeley. His research focuses on
RNA-centric systems biology and next-generation sequencing data analysis
using modern statistical learning techniques. He received his Ph.D. in
computer science from University of Wisconsin-Madison under the supervision
of Colin Dewey. Then he did a postdoctoral training with Lior Pachter at
University of California, Berkeley.

Host:  Jinbo Xu <j3xu at ttic.edu>




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Sun, Feb 19, 2017 at 8:58 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Monday, February 20th at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Bo Li, UC Berkeley
>
>
> Title:       *Taming Big Sequencing Data for RNA Biology*
> *               From Transcript Abundance Estimation to
> ‘Epitranscriptomic’ Mark Detection*
>
> Abstract:
>
> Next generation sequencing (NGS) technology is one of the most phenomenal
> genomics innovations in the past decades. It brings us a promising future
> of genomic diagnosis and personalized treatment of human disease. The power
> of NGS is reflected in its ability to measure almost any molecular signal
> of interest using the following paradigm: 1) signal embedding; 2)
> sequencing; 3) signal extraction. This talk introduces two of my works on
> extracting signal from big sequencing data.
>
>
>
> The first work addresses the problem of accurately estimating transcript
> abundance from RNA-sequencing (RNA-Seq) data. Transcript abundance is the
> relative measure of transcript copy number in cells. It is a fundamental
> quantity in biology and has a huge impact on human health: studies have
> shown that transcript abundances are often altered in disease conditions.
> To extract abundance “signal” from RNA-Seq data, we develop RSEM, a
> probabilistic learning software that enables accurate estimation of
> transcript abundances. Since RSEM was released, it has been extensively
> used around the world. RSEM achievements include citations over 2,300 and
> adoption in big consortium projects such as TCGA and ENCODE. I will discuss
> the computational challenges existed in developing RSEM and how we address
> these challenges.
>
>
>
> The second work introduces PROBer, a unified probabilistic framework for
> epitranscriptomic mark detection. In a general sense, epitranscriptomics
> refers to the study of RNA structure, RNA modification and RNA-protein
> interaction at the transcriptome scale. These three aspects are important
> for understanding the mechanism of alternative splicing whose disturbance
> often results in diseases such as cancer and Parkinson’s disease. PROBer
> can be viewed as a generalization of RSEM since it extracts both transcript
> abundance and epitranscriptomic mark position information from the
> data. Recent studies suggest that RNA structure, RNA modification and
> RNA-protein interaction are interrelated and their interaction regulates
> alternative splicing. To better illustrate the secret of alternative
> splicing, experiments profiling these three aspects are likely to be
> performed and analyzed together. Therefore, a general tool that could
> extract signals of these three aspects, such as PROBer, will become an
> emerging need.
>
>
>
> *Nature Methods* recently chose epitranscriptome analysis as method of
> the year. In the end of my talk, I will discuss the bioinformatics
> challenges and opportunities in epitranscriptome analysis, and my future
> research plan.
>
>
> Bio:
> Dr. Bo Li is a Postdoctoral researcher in the Center for RNA Systems
> Biology at the University of California, Berkeley. His research focuses on
> RNA-centric systems biology and next-generation sequencing data analysis
> using modern statistical learning techniques. He received his Ph.D. in
> computer science from University of Wisconsin-Madison under the supervision
> of Colin Dewey. Then he did a postdoctoral training with Lior Pachter at
> University of California, Berkeley.
>
> Host:  Jinbo Xu <j3xu at ttic.edu>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Tue, Feb 14, 2017 at 7:17 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Monday, February 20th at 11:00 am
>>
>> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Bo Li, UC Berkeley
>>
>>
>> Title:       *Taming Big Sequencing Data for RNA Biology*
>> *               From Transcript Abundance Estimation to
>> ‘Epitranscriptomic’ Mark Detection*
>>
>> Abstract:
>>
>> Next generation sequencing (NGS) technology is one of the most phenomenal
>> genomics innovations in the past decades. It brings us a promising future
>> of genomic diagnosis and personalized treatment of human disease. The power
>> of NGS is reflected in its ability to measure almost any molecular signal
>> of interest using the following paradigm: 1) signal embedding; 2)
>> sequencing; 3) signal extraction. This talk introduces two of my works on
>> extracting signal from big sequencing data.
>>
>>
>>
>> The first work addresses the problem of accurately estimating transcript
>> abundance from RNA-sequencing (RNA-Seq) data. Transcript abundance is the
>> relative measure of transcript copy number in cells. It is a fundamental
>> quantity in biology and has a huge impact on human health: studies have
>> shown that transcript abundances are often altered in disease conditions.
>> To extract abundance “signal” from RNA-Seq data, we develop RSEM, a
>> probabilistic learning software that enables accurate estimation of
>> transcript abundances. Since RSEM was released, it has been extensively
>> used around the world. RSEM achievements include citations over 2,300 and
>> adoption in big consortium projects such as TCGA and ENCODE. I will discuss
>> the computational challenges existed in developing RSEM and how we address
>> these challenges.
>>
>>
>>
>> The second work introduces PROBer, a unified probabilistic framework for
>> epitranscriptomic mark detection. In a general sense, epitranscriptomics
>> refers to the study of RNA structure, RNA modification and RNA-protein
>> interaction at the transcriptome scale. These three aspects are important
>> for understanding the mechanism of alternative splicing whose disturbance
>> often results in diseases such as cancer and Parkinson’s disease. PROBer
>> can be viewed as a generalization of RSEM since it extracts both transcript
>> abundance and epitranscriptomic mark position information from the
>> data. Recent studies suggest that RNA structure, RNA modification and
>> RNA-protein interaction are interrelated and their interaction regulates
>> alternative splicing. To better illustrate the secret of alternative
>> splicing, experiments profiling these three aspects are likely to be
>> performed and analyzed together. Therefore, a general tool that could
>> extract signals of these three aspects, such as PROBer, will become an
>> emerging need.
>>
>>
>>
>> *Nature Methods* recently chose epitranscriptome analysis as method of
>> the year. In the end of my talk, I will discuss the bioinformatics
>> challenges and opportunities in epitranscriptome analysis, and my future
>> research plan.
>>
>>
>> Bio:
>> Dr. Bo Li is a Postdoctoral researcher in the Center for RNA Systems
>> Biology at the University of California, Berkeley. His research focuses on
>> RNA-centric systems biology and next-generation sequencing data analysis
>> using modern statistical learning techniques. He received his Ph.D. in
>> computer science from University of Wisconsin-Madison under the supervision
>> of Colin Dewey. Then he did a postdoctoral training with Lior Pachter at
>> University of California, Berkeley.
>>
>> Host:  Jinbo Xu <j3xu at ttic.edu>
>>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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