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潘玮华课题组

  潘玮华课题组

  Pan, Weihua Lab

  

  课题组长

  潘玮华, 研究员,博士生导师。2019年毕业于美国加州大学(河滨分校),获得计算机科学博士学位和统计学硕士学位。2019-2020年在美国卡内基梅隆大学计算机学院计算生物学系担任Lane Fellow博士后研究员。主要从事基因组学,尤其是基因组序列分析相关的生物信息算法研究。近年来以第一作者在生物信息学顶级会议RECOMB、ISMB和知名期刊Bioinformatics、Journal of Computational Biology等发表研究论文6篇。长期担任生物信息顶级会议和知名期刊审稿人。

  

  工作经历

  2020.09-至今           中国农业科学院(深圳)农业基因组研究所,研究员

  2019.09-2020.08     美国卡内基梅隆大学,Lane Fellow博士后研究员

  

  教育经历

  2014.09-2019.09        美国加州大学(河滨分校), 计算机科学, 博士

  2016.09-2018.06        美国加州大学(河滨分校), 统计学, 硕士

  2011.09-2014.06        中国科学技术大学, 计算机软件与理论, 硕士

  2007.09-2011.06        南京师范大学, 计算机科学与技术, 学士

  

  研究方向

  本团队主要从事基因组学相关的生物信息学算法研究。目前尤其关注于基因组组装、单体分型、变异检测等关键性计算问题。将前沿的基因组学技术,如Pacbio HiFi,Oxford Nanopore,Hi-C,BioNano,10x Genomics等,与先进的计算技术,如组合优化算法、图论、概率统计、机器学习等相结合,开发新颖、准确、高效的算法用于攻克本领域尚未解决或完全解决的难题,如多倍体基因组组装、端到端组装、单体型组装、宏基因组组装等。并将开发的算法技术应用于科研项目,辅助解决重大科研问题。

  

  研究进展

  开发了一套基于BioNano光学图谱数据的基因组组装后处理流程,显著提高了组装结果的连续性和正确性,具体包括以下三项核心算法。

  1. 开发了基于BioNano数据的chimeric contig检测算法Chimericognizer。采用投票机制,利用多条组装结果与光学图谱冲突准确检测错误组装的contig和光学图谱片段。相比于之前的方法显著降低了检测的假阳性。

  2. 开发了基于BioNano数据的reconciliation算法Novo&StitchNovo&Stitch算法通过合并多个组装结果产生一个新的组装结果,使得新的组装结果在连续性和正确性上均优于原有的任何一个组装结果。

  3. 开发了基于BioNano数据的scaffolding算法OMGSOMGS算法首次综合利用了多条光学图谱的信息更加准确地进行scaffolding

    

  PI

  Weihua Pan, Principle Investigator, Doctoral Advisor. 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 

  Peer-reviewed 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)

  潘玮华课题组更新于2021年2月

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