Research
Topics
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Machine Learning
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Computational Biology
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Bioinformatics
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Alternative splicing quantification
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Post-transcriptional regulation
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Regulatory elements modeling
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Genetic variations effect on RNA processing
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Personalized medicine based on clinical, genetic, and genomic profiling
Projects
The role of splicing variations in human disease
Multiple projects in the lab involve the role of splicing variations in disease such as cancer and neurodegeneration. We develop algorithms to detect these splicing variations from RNASeq, combine them with other data (multi-omics), experimentally validate those in relevant cell lines/primary tissues, and study their functional consequences
In collaboration with
Kristen Lynch, Sara Cherry, Martin Carrol, Andrei Thomas-Tikhonenko, David Elliot, Elizabeth Bhoj
High-throughput assays development
We are involved in several projects involving the development of new high-throughput assays for RNA processing.
Lab member
Kevin Yang, Mathieu Quesnel-Vallieres
In collaboration with
Quantification and Visualization of RNA splicing
A main focus is developing methods and tools for the community to address the computational challenges posed by high throughput data for accurately quantifying and visualizing splice variants and how these change across different conditions.
RNA Processing Regulatory
Mechanisms
The lab iterates between computational modeling and experimental verifications of mechanisms involved in RNA processing
Lab member
In collaboration with
Kristen Lynch, Russ Carstens,
David Elliot, Steve Liebhaber
Alternative Splicing Prediction
A major focus of the lab is developing algorithms and tools for predicting and analyzing splicing outcome under varying conditions.
Genetic Variations Effect on RNA Processing
We study how genetic variations affect splicing or other RNA processing and how these changes correlate with maligant state and phenotipic diversity.
Lab member
Tools
