research.html

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<h2>Studying alternative splicing in single cells</h2>
<p>Alternative pre-mRNA splicing is a tightly regulated post-transcriptional process that amplifies the 
coding potential of the genome by co-expressing multiple variants of the same gene. Its investigation at 
the level of single cells have been challenging thus far due to the limitations of single-cell sequencing 
technology and current alternative splicing tools have inaccurately reported that most multi-exonic 
genes tend to express a single isoform at a time. We aim to develop new computational tools using 
novel statistical approaches to accurately quantify levels of exon splicing and implement it to study 
changes in alternative splicing during subclass-specific neurogenesis.</p>

<h2>Modelling nonsense-mediated decay (NMD) activity</h2>
<p>NMD is a highly conserved quality control mechanism that enforces the accuracy of gene expression by 
clearing transcripts harboring premature termination codons. The success of many biotechnology and 
biomedical applications such as CRISPR-Cas9 gene knockout systems and cancer immunotherapies rely 
on optimal NMD activity. The efficacy of NMD is highly variable between biological systems but little is 
known about its regulatory mechanisms. We aim to achieve broader understanding of the predictors of 
NMD activity using advanced statistical and machine learning models.</p>

<h2>Annotating novel mRNAs from custom-assembled transcriptomes</h2>
<p>We recently published {factR}, a suite of bioinformatics tools for the functional annotation of novel 
transcripts detected by next- and third-generation sequencing experiments. At its core, {factR} builds
coding sequences of newly-identified mRNA isoforms and determine its functional consequence based 
on predicted protein domain architectures and NMD-triggering features. We believe that this robust and 
easy-to-use software will help us to better understand the complexity of the cellular transcriptome.
{factR} is actively maintained on 
<a href="https://bioconductor.org/packages/release/bioc/html/factR.html">R/Bioconductor</a></p>.
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