Research

Detecting genomic deletions from NGS data with unsupervised learning

Develop a new method called EigenDel. EigenDel first uses discordant & clipped reads to get initial deletion candidates, and then it clusters similar candidates by using PCA and hierarchical clustering. Finally, EigenDel uses a carefully designed approach for calling true deletions from each cluster

Detecting circular RNA from high-throughput sequence data with de Bruijn graph

Develop a new method named CircDBG. It creates a de Bruijn graph based on k-mers from the boundary parts of exons in annotated genome. Then, CircDBG takes advantage of this graph to find the relationship between k-mer of reads and the potential donor/acceptor exon by tracking the path in the graph for circRNA detection. A special case of circRNA, chimeric circRNA, is reported by CircDBG.

CircMarker: a fast and accurate algorithm for circular RNA detection

Develop a new computational approach, named CircMarker. CircMarker is based on k-mers rather than reads mapping and takes advantage of annotation files to create the k-mer table for circRNA detection.