Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing
Alternative splicing (AS) and alternative back-splicing (ABS) are essential to understanding the development of cancer and may play a role as a target of personalized cancer therapeutics. However, the existing reference transcriptome annotation databases are far from being complete. Thus, detecting novel splicing events is an important yet a challenging task. This is partially due to the fact that traditional short-read sequencing (SRS) technologies, despite their low error rate, are limited by their short read lengths. On the other hand, the more recent long-read sequencing (LRS) technologies, while having the potential for capturing full-length transcripts, are marred with high error rates and significant structural artifacts.
In this talk, two computational methods will be presented: CircMiner and Freddie. CircMiner accurately and efficiently detects back-splice sites and their abundances from SRS data using a novel splice-aware pseudo-alignment algorithm. Freddie is an annotation-free isoform discovery and detection tool that uses genome alignments of transcriptomic LRS as input with no reliance on transcriptome annotation databases by solving a combinatorial problem called MErCi (Minimum Error Clustering into Isoforms).
Dr. Faraz Hach is an assistant professor in the Department of Urologic Sciences at the University of British Columbia and a senior research scientist at Vancouver Prostate Centre. He completed his PhD in computing science in Simon Fraser University and was a recipient of the Governor General’s Gold Medal. His goal is to build bridges between computational algorithm design and biological problems pertaining to precision medicine with a special focus on cancer genomes. His research involves designing novel and high performance algorithms for analyzing large, high dimensional omics data produced by sequencing technologies. Recently, he is working on developing computational algorithms for the detection of aberrations using sequencing data obtained from tissue and liquid biopsies in order to understand clonal evolution in cancer genomes.
(Monday) 3:00 pm - 4:00 pm
CDSLNCI CCR Cancer Data Science Lab