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Weihua Pan Lab

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Weihua Pan, Principle Investigator. He graduated from University of California, Riverside in 2019 and received a Ph.D. degree in Computer Science and a master degree in Statistics. He subsequently worked as a Lane Fellow in School of Computer Science, Carnegie Mellon University. His lab focuses on algorithm design in genomic sequence analysis. In recent years, as first author, he has published 6 research papers on top conferences and journals in the area of bioinformatics such as ISMB, RECOMB, Bioinformatics and Journal of Computational Biology. He is a reviewer of bioinformatics top conferences and journals.

 

Research Experience

 

2020.09-Present: Agricultural Genomics Institute at Shenzhen-CAAS         Principle Investigator / Research Professor

2019.09-2020.08: Carnegie Mellon University, Lane Fellow

 

Education

 

2014.09-2019.09: University of California, Riverside, Computer Science, Ph.D.

2016.09-2018.06: University of California, Riverside, Statistics, M.S.

2011.09-2014.06: University of Science and Technology of China, Computer Software and Theory, M.E.

2007.09-2011.06:  Nanjing Normal University, Computer Science and Technology, B.E.

 

Research Interest

 

Our research focuses on designing algorithms to solve key problems in genomics such as genome assembly, haplotype phasing and variant calling. We integrate cutting-edge genomics technologies such as Pacbio HiFi, Oxford Nanopore, Hi-C, BioNano and 10x Genomics with computational methods such as combinatorial optimization algorithms, graph theory, statistics and machine learning to develop novel, accurate and efficient algorithms to solve computational problems like polyploid genome assembly, haplotype reconstruction, telomere to telomere assembly and metagenome assembly. And we apply computational methods developed to important scientific discovery.

 

Major Achievements

 

We developed a post-assembly-processing pipeline to improve the contiguity and correctness of assemblies with the help of BioNano optical maps. The three key algorithms are as follow.

1. Chimericognizer for detecting and correcting chimeric contigs with BioNano maps. Chimericognizer uses voting strategy to accurately detect chimeric contigs and BioNano fragments from the conflicts between one or more optical maps and multiple assemblies. It significantly reduces the false positive compared with existing methods.

2. Novo&Stich for assembly reconciliation with BioNano maps. Novo&Stich is able to merge multiple assemblies into a new assembly of which the contiguity and correctness are both better than any input assembly.

3. OMGS for scaffolding using BioNano maps. OMGS is the first algorithm which can fully take advantage of multiple BioNano maps to carry out scaffolding accurately.

 

 

 

Selected Publication

 

Conference Papers:

1. W. Pan, T. Jiang, S. Lonardi. “OMGS: Optical Map-based Genome Scaffolding.” Proceedings of Conference on Research in Computational Molecular Biology (RECOMB), pp. 190-207, Washington, DC, 2019.

2. W. Pan, S. Wanamaker, A. Ah-Fong, H. Judelson, S. Lonardi. “Novo&Stitch: Accurate Reconciliation of Genome Assemblies via Optical Maps.” Proceedings of Conference on Intelligent Systems for Molecular Biology (ISMB), pp. i43-i51, Chicago, IL, 2018.

3. A. Polishko, M. A. Hasan, W. Pan, E. Bunnik, K. L. Roch, S. Lonardi. “ThIEF: Finding Genome-wide Trajectories of Epigenetics Marks.” Proceedings of the Workshop on Algorithms in Bioinformatics (WABI), 19:1-19:16, Boston, MA, 2017.

 

Journal Papers:

1. C. Schwartz, J.F. Cheng, R. Evans, C.A. Schwartz, J.M. Wagner, S. Anglin, A. Beitz, W. Pan, S. Lonardi, M. Blenner, H.S. Alper. “Validating genome-wide CRISPR-Cas9 function improves screening in the oleaginous yeast Yarrowia lipolytica.” Metabolic Engineering, vol. 55, pp. 102-110, 2019.

2. W. Pan, T. Jiang, S. Lonardi. “OMGS: Optical Map-based Genome Scaffolding.” Journal of Computational Biology, pp. 519-533, 2019.

3. W. Pan, S. Lonardi. “Accurate detection of chimeric contigs via Bionano optical maps.” Bioinformatics, vol. 35, no. 10, pp. 1760-1762, 2018.

4. W. Pan, S. Wanamaker, A. Ah-Fong, H. Judelson, S. Lonardi. “Novo&Stitch: Accurate Reconciliation of Genome Assemblies via Optical Maps.” Bioinformatics, vol. 34, no. 13, pp. i43-i51, 2018.

5. W. Pan, B. Chen, Y. Xu. “MetaObtainer: A Tool for Obtaining Specified Species from Metagenomic Reads of Next-generation Sequencing.” Interdisciplinary Sciences: Computational Life Sciences, vol. 7, no. 4, pp. 405-413, 2015.

6. W. Pan, Y. Zhao, Y. Xu, F. Zhou. “WinHAP2: an extremely fast haplotype phasing program for long genotype sequences.” BMC bioinformatics vol. 15, no. 1, pp. 164, 2014.

7. X. Su*, W. Pan*, B. Song, J. Xu, K. Ning. “Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization.” PLoS One, vol. 9, no. 3, pp. e89323, 2014. (*co-first author)

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