Novel algorithms for efficient subsequence searching and mapping in nanopore raw signals towards targeted sequencing

Speaker:   Dr. Sheng Wang

Time:       16:30-17:30, Sep. 2

Location:  SIST 1A 212

Host:       Prof. Jie Zheng

Abstract:

Genome diagnostics have gradually become a prevailing routine for human healthcare. With the advances in understanding the causal genes for many human diseases, targeted sequencing provides a rapid, cost-efficient and focused option for clinical applications, such as SNP detection and haplotype classification, in a specific genomic region. Although nanopore sequencing offers a perfect tool for targeted sequencing because of its mobility, PCR-freeness, and long read properties, it poses a challenging computational problem of how to efficiently and accurately search and map genomic subsequences of interest in a pool of nanopore reads (or raw signals). Due to its relatively low sequencing accuracy, there is currently no reliable solution to this problem, especially at low sequencing coverage.

Here, we propose a brand new signal-based subsequence inquiry pipeline as well as two novel algorithms to tackle this problem. The proposed algorithms follow the principle of subsequence dynamic time warping and directly operate on the electrical current signals, without loss of information in base-calling. Therefore, the proposed algorithms can serve as a tool for sequence inquiry in targeted sequencing. Two novel criteria are offered for the consequent signal quality analysis and data classification. Comprehensive experiments on real-world nanopore datasets show the efficiency and effectiveness of the proposed algorithms. We further demonstrate the potential applications of the proposed algorithms in two typical tasks in nanopore-based targeted sequencing: SNP detection under low sequencing coverage, and haplotype classification under low sequencing accuracy.

Bio:

Dr. Sheng Wang is a Research Scientist at King Abdullah University of Science and Technology (KAUST). He got his bachelor degree in biochemistry from Shanghai Jiaotong University (SJTU) in 2005; PhD diploma in bioinformatics from University of Chinese Academy of Sciences (UCAS) in 2010. Then he went over to Toyota Technological Institute at Chicago (TTIC) and University of Chicago to work with Prof. Jinbo Xu as a Postdoc on protein structure prediction. His current research mainly focuses on the algorithm design and the data analysis of nanopore-based third-generation sequencing. 

Dr. Wang has published 17 conference articles in the fields of computational biology, including top conferences such as ISMB, RECOMB, ECCB, etc. He also published 46 articles in well-known journals, including 1 PNAS, 1 Nature Protocols, 1 Cell Systems, 6 Nucleic Acids Research, 13 Bioinformatics, 2 PLoS Computational Biology. Three of his published papers were awarded as Highly Cited Papers by Web of Science; one of his published paper has won the 2018 PLoS Computational Biology Research Prize in the category of breakthrough and innovation. His publications have been cited over ~2000 times according to Google Scholar. More details about him and his research can be found at http://ttic.uchicago.edu/~wangsheng/.

SIST-Seminar 18196