<div dir="ltr"><div style="font-size:12.8px"><div class="gmail_default"><font color="#000000" face="arial, helvetica, sans-serif">When: Monday, February 20th at 11:00 am </font></div><div class="gmail_default"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div class="gmail_default"><font color="#000000" face="arial, helvetica, sans-serif">Where: <span class="gmail-m_4582416343268402917m_3485000624078657807gmail-m_5114113929504086742gmail-m_4588640196162890717m_3136881027827703783gmail-m_-5845083723734738781gmail-m_8936094986121047744gmail-m_-3164973227124251497gmail-m_2953668934074478317gmail-m_-3155518689668024534m_9067904842688472155gmail-m_3071693547520408192gmail-il">TTIC</span>, 6045 S Kenwood Avenue, 5th Floor, Room 526</font></div><div class="gmail_default"><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><font color="#000000">Who:<b> </b> </font><font color="#000000">Bo Li, UC Berkeley</font></font></div></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><br></div><font face="arial, helvetica, sans-serif" style="font-size:12.8px">Title: <font color="#666666"><b>Taming Big Sequencing Data for RNA Biology</b></font></font><div style="font-size:12.8px"><i style="color:black"><font face="arial, helvetica, sans-serif"> From Transcript Abundance Estimation to ‘Epitranscriptomic’ Mark Detection</font></i></div><div style="font-size:12.8px"><i style="color:black"><font face="arial, helvetica, sans-serif"><br></font></i></div><div style="font-size:12.8px"><font color="#000000" face="arial, helvetica, sans-serif">Abstract:</font></div><div style="font-size:12.8px"><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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. </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"><i>Nature Methods</i> 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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"><br></font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">Bio:</font></p><font face="arial, helvetica, sans-serif">Dr. <span class="gmail-m_4582416343268402917m_3485000624078657807gmail-il">Bo</span> <span class="gmail-m_4582416343268402917m_3485000624078657807gmail-il">Li</span> 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.</font></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif">Host: <a href="mailto:j3xu@ttic.edu" target="_blank">Jinbo Xu</a></font></div><div style="font-size:12.8px"><font face="arial, helvetica, sans-serif"><br></font></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font><div><font face="arial, helvetica, sans-serif">Administrative Assistant</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 504</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:(773) 834-1757</font></i></div><div><i><font face="arial, helvetica, sans-serif">f: (773) 357-6970</font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div>
<br><div class="gmail_quote">On Tue, Feb 14, 2017 at 7:17 PM, Mary Marre <span dir="ltr"><<a href="mailto:mmarre@ttic.edu" target="_blank">mmarre@ttic.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div><font color="#000000" face="arial, helvetica, sans-serif">When: Monday, February 20th at 11:00 am </font></div><div><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font color="#000000" face="arial, helvetica, sans-serif">Where: <span class="m_4582416343268402917m_3485000624078657807gmail-m_5114113929504086742gmail-m_4588640196162890717m_3136881027827703783gmail-m_-5845083723734738781gmail-m_8936094986121047744gmail-m_-3164973227124251497gmail-m_2953668934074478317gmail-m_-3155518689668024534m_9067904842688472155gmail-m_3071693547520408192gmail-il">TTIC</span>, 6045 S Kenwood Avenue, 5th Floor, Room 526</font></div><div><font color="#000000" face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><font color="#000000">Who:<b> </b> </font><font color="#000000">Bo Li, UC Berkeley</font></font></div></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div><br></div><font face="arial, helvetica, sans-serif">Title: <font color="#666666"><b>Taming Big Sequencing Data for RNA Biology</b></font></font><div><i style="color:black"><font face="arial, helvetica, sans-serif"> From Transcript Abundance Estimation to ‘Epitranscriptomic’ Mark Detection</font></i></div><div><i style="color:black"><font face="arial, helvetica, sans-serif"><br></font></i></div><div><font color="#000000" face="arial, helvetica, sans-serif">Abstract:</font></div><div><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">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. </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"> </font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"><i>Nature Methods</i> 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.</font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif"><br></font></p><p class="MsoNormal"><font face="arial, helvetica, sans-serif">Bio:</font></p><font face="arial, helvetica, sans-serif">Dr. <span class="m_4582416343268402917m_3485000624078657807gmail-il">Bo</span> <span class="m_4582416343268402917m_3485000624078657807gmail-il">Li</span> 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.</font></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif">Host: <a href="mailto:j3xu@ttic.edu" target="_blank">Jinbo Xu</a></font></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div><font face="arial, helvetica, sans-serif"><br></font></div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px"> </div><div><div class="m_4582416343268402917m_3485000624078657807gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font face="arial, helvetica, sans-serif">Mary C. Marre</font></div><div dir="ltr"><div><font face="arial, helvetica, sans-serif">Administrative Assistant</font></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6"><b>Toyota Technological Institute</b></font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">6045 S. Kenwood Avenue</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Room 504</font></i></div><div><i><font face="arial, helvetica, sans-serif" color="#3d85c6">Chicago, IL 60637</font></i></div><div><i><font face="arial, helvetica, sans-serif">p:<a href="tel:(773)%20834-1757" value="+17738341757" target="_blank">(773) 834-1757</a></font></i></div><div><i><font face="arial, helvetica, sans-serif">f: <a href="tel:(773)%20357-6970" value="+17733576970" target="_blank">(773) 357-6970</a></font></i></div><div><b><i><a href="mailto:mmarre@ttic.edu" target="_blank"><font face="arial, helvetica, sans-serif">mmarre@ttic.edu</font></a></i></b></div></div></div></div></div></div></div></div></div></div>
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