JCVI: About / Bios / Weizhong Li
 
 
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About

Biographies

Weizhong Li, Ph.D.
Associate Professor

Research Interests and Accomplishments

Dr. Weizhong Li is an Associate Professor at the J. Craig Venter Institute (JCVI). Dr. Li is interested in developing novel bioinformatics algorithms, computational methods and integrated workflows in genomics, metagenomics, transcriptomics, systems biology, protein structure and function prediction, computer aided drug discovery and so on. He is especially interested in addressing the Big Data challenges in various genomics fields with novel computational algorithms and high performance computing technologies. Dr. Li has contributed several computational methods to the community, including CD-HIT, an ultrafast sequence clustering program that is widely used.

Dr. Li received his Bachelor Degree (1991) in Chemistry and Ph.D. (1996) in Computational Chemistry From Nankai University in China. He was a Postdoctoral researcher in bioinformatics at the University of California San Diego, San Diego Supercomputer Center and then the Sanford Burnham Institute at La Jolla, California from 1999-2002. He worked as Staff Bioinformatics Scientist in Quorex Pharmaceuticals, Inc, California and Sanford Burnham Institute before he joined UCSD in 2006. During his appointment at UCSD as a Principal Investigator until 2014, Dr. Li led a group and developed several computational methods in metagenomics data analysis.

In addition to his research, Dr. Li has been a reviewer for several journals, including Bioinformatics, BMC bioinformatics, Genome Research, Genome Biology, ISME, PLoS Computational Biology, Nucleic Acids Research, Briefings in Bioinformatics, PLoS ONE, BMC Genomic, ISMB, and RECOMB. He also reviews grant proposals for many agencies and serves as an editor for scientific journals.

Select Publications

Zhu Z, Niu B, et al.
MGAviewer: a Desktop Visualization Tool for Analysis of Metagenomics Alignment Data.

Bioinformatics (Oxford, England). 2013 Jan 01; 29: 122-3.[more]

Fu L, Niu B, et al.
CD-HIT: Accelerated for Clustering the Next-generation Sequencing Data.

Bioinformatics (Oxford, England). 2012 Dec 01; 28: 3150-2.[more]

Li W, Fu L, et al.
Ultrafast Clustering Algorithms for Metagenomic Sequence Analysis.

Briefings in Bioinformatics. 2012 Nov 01; 13: 656-68.[more]

Niu B, Zhu Z, et al.
FR-HIT, a Very Fast Program to Recruit Metagenomic Reads to Homologous Reference Genomes.

Bioinformatics (Oxford, England). 2011 Jun 15; 27: 1704-5.[more]

Wu S, Zhu Z, et al.
WebMGA: a Customizable Web Server for Fast Metagenomic Sequence Analysis.

BMC Genomics. 2011 Apr 01; 12: 444.[more]

Niu B, Fu L, et al.
Artificial and Natural Duplicates in Pyrosequencing Reads of Metagenomic Data.

BMC Bioinformatics. 2010 Apr 01; 11: 187.[more]

Huang Y, Niu B, et al.
CD-HIT Suite: a Web Server for Clustering and Comparing Biological Sequences.

Bioinformatics (Oxford, England). 2010 Mar 01; 26: 680-2.[more]

Huang Y, Gilna P, et al.
Identification of Ribosomal RNA Genes in Metagenomic Fragments.

Bioinformatics (Oxford, England). 2009 May 15; 25: 1338-40.[more]

Li W
Analysis and Comparison of Very Large Metagenomes With Fast Clustering and Functional Annotation.

BMC Bioinformatics. 2009 Apr 01; 10: 359.[more]

Yooseph S, Li W, et al.
Gene Identification and Protein Classification in Microbial Metagenomic Sequence Data via Incremental Clustering.

BMC Bioinformatics. 2008 Apr 01; 9(1): 182.[more]

Yooseph S, Sutton G, et al.
The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families.

PLoS Biology. 2007 Mar 01; 5(3): e16.[more]

Li W, Godzik A
Cd-hit: a Fast Program for Clustering and Comparing Large Sets of Protein or Nucleotide Sequences.

Bioinformatics (Oxford, England). 2006 Jul 01; 22: 1658-9.[more]

Li W, Jaroszewski L, et al.
Tolerating Some Redundancy Significantly Speeds Up Clustering of Large Protein Databases.

Bioinformatics (Oxford, England). 2002 Jan 01; 18: 77-82.[more]

Li W, Jaroszewski L, et al.
Clustering of Highly Homologous Sequences to Reduce the Size of Large Protein Databases.

Bioinformatics (Oxford, England). 2001 Mar 01; 17: 282-3.[more]