Profile
LiangJiang (LJ) Wang
Genetics and Biochemistry
Associate Professor
Life Sciences Building 228B [Office]
Educational Background
Ph.D., Botany, University of Georgia, 1999
M.S., Computer Science, Mississippi State University, 2001
M.S., Biology, Zhejiang University, China, 1989
B.S., Biology, Zhejiang University, China, 1986
Research Interests
The research in my lab has focused on biological knowledge discovery, genomic data integration and mining, and computational RNA biology. We previously developed machine learning models and web-based tools for biomedical research, including BindN and BindN+ for predicting DNA/RNA-binding residues in protein sequence, MuStab for protein stability prediction, and seeSUMO for protein sumoylation site prediction. We also integrated and mined the vast amount of publicly available gene expression data for understanding the molecular pathways involved in human diseases, including intellectual disability, autism, and cancer.
Recently, we have been developing machine learning and data mining approaches for the functional annotation of human long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) by leveraging the vast amount of genetic and genomic data ("biological big data"). We have constructed machine learning models and gene co-expression networks to predict and prioritize candidate lncRNAs associated with intellectual disability and autism spectrum disorders. We have also applied deep learning techniques to the prediction and pattern analysis of lncRNA subcellular localization, circRNA back-splicing code, and RNA-protein interactions. Our studies demonstrate that genomic data mining can not only give insights into RNA functions in gene regulation and 3D genome organization, but also provide valuable information for experimental studies of candidate genes associated with human diseases.
Research Group (Lab)
Current Students:
Snehal Shah (2019 - present, PhD student in Healthcare Genetics)
Anqi Wei (2017 - present, PhD student in Biochemistry)
Former Students:
Shuzhen Kuang (2017 - 2020, PhD in Biology, Postdoc at UCSF School of Medicine)
Jun Wang (2015 - 2020, PhD in Biochemistry, Postdoc at Yale School of Medicine)
Brian Gudenas (2014 - 2018, PhD in Genetics, Postdoc at St Jude Research Hospital)
Jose Guevara (2012 - 2017, PhD in Biochemistry, Professor at University of Costa Rica)
Steven Cogill (2012 - 2016, PhD in Genetics, Postdoc at Stanford School of Medicine)
Shaolei Teng (2007 - 2011, PhD in Biochemistry, Postdoc at Cold Spring Harbor Laboratory)
Courses Taught
GEN/BCHM 4400/4400H/6400 Bioinformatics
GEN/BCHM 4930 Senior Seminar
GEN 3020/3020H Introduction to Genetics
BCHM 3050 Essential Elements of Biochemistry
GEN/BCHM 8100 Principles of Molecular Biology
Selected Publications
(Selected from 79 peer-reviewed publications. *Correspondence author)
Kuang, S., Wei, Y. and Wang, L.* (2021) Expression-based prediction of human essential genes and candidate lncRNAs in cancer cells. Bioinformatics, 37(3):396-403. https://pubmed.ncbi.nlm.nih.gov/32790840/
Kuang, S., Wang, L.* (2021) Deep learning of sequence patterns for CTCF-mediated chromatin loop formation. Journal of Computational Biology, 28(2):133-145. https://pubmed.ncbi.nlm.nih.gov/33232622/
Wang, J., Wang, L.* (2020) Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features. BMC Bioinformatics, 21(1):505. https://pubmed.ncbi.nlm.nih.gov/33160303/
Kuang, S. and Wang, L.* (2020) Identification and analysis of consensus RNA motifs binding to the genome regulator CTCF. NAR Genomics and Bioinformatics, 2(2):lqaa031. https://pubmed.ncbi.nlm.nih.gov/33575587/
Wang, J. and Wang, L.* (2020) Deep analysis of RNA N6-adenosine methylation (m6A) patterns in human cells. NAR Genomics and Bioinformatics, 2(1):lqaa007. https://pubmed.ncbi.nlm.nih.gov/33575554/
Wang, J., Wang, L.* (2019) Deep learning of the back-splicing code for circular RNA formation. Bioinformatics, 35(24):5235-5242. https://www.ncbi.nlm.nih.gov/pubmed/31077303
Gudenas, B.L., Wang, J., Kuang, S., Wei, A., Cogill, S.B., Wang, L.* (2019) Genomic data mining for functional annotation of human long noncoding RNAs. Journal of Zhejiang University - Science B, 20(6):476-487. https://www.ncbi.nlm.nih.gov/pubmed/31090273
Gudenas, B.L., Wang, L.* (2018) Prediction of lncRNA subcellular localization with deep learning from sequence features. Scientific Reports, 8:16385. https://www.ncbi.nlm.nih.gov/pubmed/30401954
Cogill, S.B., Srivastava, A.K., Yang, M.Q., Wang, L.* (2018) Co-expression of long non-coding RNAs and autism risk genes in the developing human brain. BMC Systems Biology, 12(Suppl 7):91. https://www.ncbi.nlm.nih.gov/pubmed/30547845
Yang, X., Kuang, S., Wang, L.*, Wei, Y.* (2018) MHC class I chain-related A: Polymorphism, regulation and therapeutic value in cancer. Biomedicine & Pharmacotherapy, 103:111-117. https://www.ncbi.nlm.nih.gov/pubmed/29635123
Gudenas, B.L., Srivastava, A.K., Wang, L.* (2017) Integrative genomic analyses for identification and prioritization of long non-coding RNAs associated with autism. PLOS ONE, 12(5):e0178532. https://www.ncbi.nlm.nih.gov/pubmed/28562671
Cogill, S.B., Wang, L.* (2016) Support vector machine model of developmental brain gene expression data for prioritization of autism risk gene candidates. Bioinformatics, 32 (23):3611-3618. https://www.ncbi.nlm.nih.gov/pubmed/27506227
Gudenas, B.L., Wang, L.* (2015) Gene co-expression networks in human brain developmental transcriptomes implicate the association of long non-coding RNAs with intellectual disability. Bioinformatics and Biology Insights, 9(Suppl 1):21-27. https://www.ncbi.nlm.nih.gov/pubmed/26523118
Cogill, S.B., Wang, L.* (2014) Co-expression network analysis of human lncRNAs and cancer genes. Cancer Informatics, 13(S5):49-59. https://www.ncbi.nlm.nih.gov/pubmed/25392693
Teng, S., Yang, J.Y., Wang, L.* (2013) Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data. BMC Medical Genomics, 6(Suppl 1):S10. https://www.ncbi.nlm.nih.gov/pubmed/23369200
Teng, S., Luo, H., Wang, L.* (2012) Predicting protein sumoylation sites from sequence features. Amino Acids, 43(1):447-455. https://www.ncbi.nlm.nih.gov/pubmed/21986959
Wang, L. *, Huang, C., Yang, J.Y. (2010) Predicting siRNA potency with random forests and support vector machines. BMC Genomics, 11(Suppl 3):S2. https://www.ncbi.nlm.nih.gov/pubmed/21143784
Teng, S., Srivastava, A.K., Wang, L.* (2010) Sequence feature-based prediction of protein stability changes upon amino acid substitutions. BMC Genomics, 11(Suppl 2):S5. https://www.ncbi.nlm.nih.gov/pubmed/21047386
Wang, L.*, Huang, C., Yang, M.Q., Yang, J.Y. (2010) BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Systems Biology, 4(Suppl 1):S3. https://www.ncbi.nlm.nih.gov/pubmed/20522253
Tribolium Genome Sequencing Consortium (Wang, L. as a coauthor) (2008) The genome of the model beetle and pest Tribolium castaneum. Nature, 452(7190):949-955. https://www.ncbi.nlm.nih.gov/pubmed/18362917
Wang, L., Wang, S., Li, Y., Paradesi, M.S.R., Brown, S.J.* (2007) BeetleBase: the model organism database for Tribolium castaneum. Nucleic Acids Research, 35(Database issue):D476-D479. https://www.ncbi.nlm.nih.gov/pubmed/17090595
Wang, L.*, Brown, S.J. (2006) BindN: a web-based tool for efficient prediction of DNA and RNA binding sites in amino acid sequences. Nucleic Acids Research, 34(Web Server issue):W243-W248. https://www.ncbi.nlm.nih.gov/pubmed/16845003
Casa, A.M., Brouwer, C., Nagel, A., Wang, L., Zhang, Q., Kresovich, S., Wessler, S.R.* (2000) Inaugural Article: The MITE family Heartbreaker (Hbr): Molecular markers in maize. Proc. Natl. Acad. Sci. USA, 97(18):10083-10089. https://www.ncbi.nlm.nih.gov/pubmed/10963671
Wang, L., Wessler, S.R.* (1998) Inefficient reinitiation is responsible for upstream open reading frame-mediated translational repression of the maize R gene. Plant Cell, 10(10):1733-1745. https://www.ncbi.nlm.nih.gov/pubmed/9761799
