DEPARTMENT OF BIOLOGY FACULTY PROFILE
RESEARCH SHAOLEI TENG PUBLICATIONS
Contact Information
Shaolei Teng, PhD
Assistant Professor - Department of Biology
E-mail: shaolei.teng@howard.edu
Office Location: EE Just Hall Room 340A
Lab Location: EE Just Hall Room 136
Link to Google Scholar: Shaolei Teng

Education
B.S., Soochow University, China (2004)
M.S., Gyeongsang National University, South Korea (2006)
Ph.D., Clemson University (2011)
Postdoctoral Fellow, Cold Spring Harbor Laboratory (2011-2015)


Courses
Bioinformatics (BIOL/BIOG 419)
Topics in Cell and Molecular Biology (BIOL 500)

Research Interests
Bioinformatics
Mental Disorders
Next-Generation Sequencing
Machine Learning
Protein Structure Modeling

Research Detailed

Bioinformatics is a new scientific discipline that utilizes computational approaches for understanding the biological meanings behind the large amounts of complex data generated from high-throughput technologies. The goal of the Teng lab is to develop and apply bioinformatics approaches for analyzing the genetic variations associated with human diseases and discovering biological knowledge hidden in the massive data sets. We are currently focused on three research areas including next-generation sequencing, machine learning and protein structure modeling.

NEXT-GENERATION SEQUENCING (NGS) technologies make the sequencing much faster and cheaper, which have revolutionized genetic research and accelerated precision medicine. We are using whole genome sequencing and targeted sequencing to identify the sequence variants that contribute to major psychiatric disorders including schizophrenia, bipolar disorder and major depressive disorder. We are applying RNA sequencing to study gene expression changes in model organisms. In addition, we are developing bioinformatics tools and databases to analyze NGS data for mental illness and cancer research.

MACHINE LEARNING can recognize complex and hidden pattern from massive data sets, and it is particularly appealing for biological knowledge discovery in big data. We are developing new machine learning methods to analyze NGS data, disease-causing mutations and protein post-translational modifications.

PROTEIN STRUCTURE MODELING provides an efficient and accurate way to investigate the functional effects of coding sequence variants by mapping them on the corresponding structure models. We are using homology modeling and energy calculation approaches to estimate the effects of missense variants on protein stability and protein-protein interactions, and investigate the roles of the mutations in complex diseases such as obesity, cancers and psychiatric disorders.

Selected Publications

  L Li, Z Jia, Y Peng, S Godar, I Getov, S Teng, J Alper, E Alexov. Forces and Disease: Electrostatic force differences caused by mutations in kinesin motor domains can distinguish between disease-causing and non-disease-causing mutations. Sci Rep. 2017 Aug 15;7(1):8237. doi: 10.1038/s41598-017-08419-7.
  Shaolei Teng, Pippa A Thomson, Shane McCarthy, Melissa Kramer, Stephanie Muller, Jayon Lihm, Stewart Morris, DC Soares, William Hennah, Sarah Harris, Luiz Miguel Camargo, Vladislav Malkov, Andrew M McIntosh, J Kirsty Millar, DH Blackwood, Kathryn L Evans, Ian J Deary, David J Porteous, W Richard McCombie. Rare disruptive variants in the DISC1 Interactome and Regulome: association with cognitive ability and schizophrenia. Molecular Psychiatry, 2017.
  Lisha Shao, Binyan Lu, Zhexing Wen, Shaolei Teng, Lingling Wang, Yi Zhao, Liyuan Wang, Koko Ishizuka, Xiufeng Xu, Akira Sawa, Hongjun Song, Guoli Ming, Yi Zhong. Disrupted-in-Schizophrenia-1 (DISC1) protein disturbs neural function in multiple disease-risk pathways. Hum Mol Genet. 2017 Jul 15;26(14):2634-2648. doi: 10.1093/hmg/ddx147.
  Bailey D, Basar MA, Nag S, Bondhu N, Teng S, Duttaroy A. The essential requirement of an animal heme peroxidase protein during the wing maturation process in Drosophila. BMC Dev Biol. 2017 Jan 11;17(1):1. doi: 10.1186/s12861-016-0143-8.
  Xin Li, Shaolei Teng. RNA-Seq in Schizophrenia. (2016) Bioinformatics and Biology insights 9 (Suppl 1), 53
  Shaolei Teng, Jack Y Yang and Liangjiang Wang. (2013) Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data. BMC Medical Genomics, 6(Suppl 1):S10
  Hafumi Nishi, Manoj Tyagi, Shaolei Teng, Benjamin Shoemaker, Kosuke Hashimoto, Emil Alexov, Stefan Wuchty and Anna R. Panchenko. (2013) Cancer missense mutations alter binding properties of proteins and their interaction networks. PLOS ONE. 8(6): e66273
  Shaolei Teng, Hong Luo, Liangjiang Wang. (2011) Predicting protein sumoylation sites from sequence features. Amino Acids, 43(1):447-55.
  Zhe Zhang, Shaolei Teng, Liangjiang Wang, Charles E. Schwartz, Emil Alexov. (2010) Computational analysis of missense mutations causing Snyder-Robinson syndrome. Human Mutation, Sep;31(9):1043-9.
  Shaolei Teng, Petras Kundrotas, Thomas Madej, Anna Panchenko and Emil Alexov. (2009) Modeling effects of human SNPs on protein-protein interactions. Biophysical Journal 96(6): 2178-2188.