The Biotreat.org Research Group

• Tongbin Li, Ph.D. joined LC Sciences as Director of Bioinformatics in November 2009. Prior to his appointment at LC Sciences, Dr. Li was an Assistant Professor at the Department of Neuroscience, University of Minnesota.

• Dr. Li's contact info: Email: toli@biotreat.org. Office: 651-340-1974.

• Small RNA Research

  Small interfering RNAs (siRNAs) have allowed the development of clean and easily regulated methods for disruption of gene expression. However, designing effective siRNA experiments continues to be a challenging task. We have developed siRecords, the largest public database of experimentally validated siRNAs with consistent efficacy ratings. We are analyzing this uniquely large and diverse siRNA efficacy dataset using statistical and machine learning-based techniques for the purpose of developing improved siRNA design protocols.

  MicroRNAs (miRNAs) are a class of small (19-27 nt) non-coding RNAs capable of base-paring to the transcripts of protein-coding genes (which are termed the targets of the miRNAs), leading to down-regulation or repression of the targeted genes. We have developed miRecords, an integrated resource for microRNA-target interactions, in collaboration with Dr. Xiaolian Gao at the University of Houston. Recently, in collaboration with the labs of Drs. James Ferrell and Pat Brown at Stanford University, we have carried out deep analyses of Argonaute 2 immunopurification array assays (or Ago2 IP array assays) to investigate the mechanisms by which miRNAs recognize their target genes.

  In collaboration with the laboratories of Dr. Steve Ekker at Mayo Clinic and Dr. Lynda Ellis at the Department of Laboratory Medicine and Pathology, University of Minnesota, we have developed MODB, a database of Morpholino screening experiments in zebrafish. Morpholinos are small nuclei acid analogs capable of knocking down gene functions in several model organisms including zebrafish, frog and sea urchins.

• Immunoinformatics

  The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. In collaboration with Dr. Darren Flower at Oxford University, we have developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.

• Peptide Microarray Analysis and Protein-Protein Interactions

  In collaboration with the laboratory of Dr. Xiaolian Gao at the University of Houston, we are developing analysis methods for quantitative peptide microarray data in screening significant peptides that bind strongly with important proteins, including kinases and PPBD (phosphopeptide binding domain)-carrying proteins.

  We have been developing and improving µPepArray Pro, a web-based program for designing peptide microarrays, and SVM-PEPARRAY, a program package for analyzing peptide microarray-based binding experiments.

  We have developed PepCyber:PPEP, a database of human protein protein interactions mediated by PPBDs. We have been working on developing improved machine-learning based models of PPBD binding specificity for SH2 domains and WW domains.

• NMR Spectroscopy Analysis of Huntington’s Disease

  In collaboration with Dr. Janet Dubinsky at the Department of Neuroscience and Dr. Ivan Tkac at the Center of Magnetic Resonance Research, University of Minnesota, we have been developing multivariate methods for analyzing in vivo proton nuclear magnetic resonance spectroscopy data obtained in Huntinton's Disease mouse models. These analyses will hopefully result in important insights for assessing phenotypes and predicting disease progression in the animal models and in human patients.

• Meta-prediction

  Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. In collaboration with Dr. Lynda Ellis at the Department of Laboratory Medicine and Pathology and Dr. Arindam Banerjee at the Department of Computer Science and Engineering, University of Minnesota, we have developed voting-based linear meta-predictors for two important biological problems: protein subcellular localization prediction and phosphorylation site prediction.

• Others

  We are interested in applying machine learning techniques to investigate significant biomedical problems. We are collaborating with multiple other laboratories both inside and outside the University of Minnesota in analyzing DNA microarray data and mass spectrometry data using various statistical and machine learning methods.

• siRecords, a database of mammalian siRNAs with efficacy ratings.

• siDRM, an effective and generally applicable online siRNA design tool.

• miRecords, an integrated resource for microRNA-target interactions.

• SVRMHC, a SVR (support vector regression)-based prediction server for MHC-binding peptides. Source code of SVRMHC modeling code can be downloaded here.

• PepCyber:PPEP, a database of human protein protein interactions mediated by phosphoprotein-binding domains.

• MetaPredSCL, a meta-predicting server for protein subcellular localization.

• MetaPredPS, a meta-predicting server for serine/threonine phosphorylation sites.

• MODB: A PATO-compliant zebrafish Morpholino screening database.

• µPepArray Pro, a web-based peptide microarray design design program.

• SVM-PEPARRAY, a qualitative and quantitative analysis program of peptide microarray binding experiments.

• Ren, Y., Gong, W., Zhou, H., Wang, Y., Xiao, F. and Li, T. (2009) siRecords: a database of mammalian RNAi experiments and efficacies. Nucleic Acids Res , 37 , D146-149. [PubMed] [Full text]

• Xiao, F., Zuo, Z., Cai, G., Kang, S., Gao, X. and Li, T. (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res, 37, D105-110. [PubMed] [Full text]

• Li, T., Zuo, Z., Zhu, Q., Hong, A., Zhou, X. and Gao, X. (2009) Web-Based Design of Peptide Microarrays Using muPepArray Pro. Methods Mol Biol, 570, 391-401. [PubMed]

• Chen, G., Zuo, Z., Zhu, Q., Hong, A., Zhou, X., Gao, X. and Li, T. (2009) Qualitative and Quantitative Analysis of Peptide Microarray Binding Experiments Using SVM-PEPARRAY.Methods Mol Biol, 570, 403-411. [PubMed]

• Aaker, J.D., Patineau, A.L., Yang, H.J., Ewart, D.T., Gong, W., Li, T., Nakagawa, Y., McLoon, S.C. and Koyano-Nakagawa, N. (2009) Feedback regulation of NEUROG2 activity by MTGR1 is required for progression of neurogenesis. Mol Cell Neurosci. [PubMed] [Full text]

• Gong, W., Ren, Y., Zhou, H., Wang, Y., Kang, S. and Li, T. (2008) siDRM: an effective and generally applicable online siRNA design tool. Bioinformatics , 24 , 2405-2406. [PubMed]

• Wan, J., Kang, S., Tang, C., Yan, J., Ren, Y., Liu, J., Gao, X., Banerjee, A., Ellis, L.B. and Li, T. (2008) Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection. Nucleic Acids Res , 36 , e22. [PubMed] [Full text]

• Gong, W., Zhou, D., Ren, Y., Wang, Y., Zuo, Z., Shen, Y., Xiao, F., Zhu, Q., Hong, A., Zhou, X., Gao, X. and Li, T. (2008) PepCyber:P~PEP: a database of human protein protein interactions mediated by phosphoprotein-binding domains. Nucleic Acids Res , 36 , D679-683. [PubMed] [Full text]

• Knowlton, M.N., Li, T., Ren, Y., Bill, B.R., Ellis, L.B. and Ekker, S.C. (2008) A PATO-compliant zebrafish screening database (MODB): management of morpholino knockdown screen information. BMC Bioinformatics , 9 , 7. [PubMed] [Full text]

• Liu, J., Kang, S., Tang, C., Ellis, L.B. and Li, T. (2007) Meta-prediction of protein subcellular localization with reduced voting. Nucleic Acids Res , 35 , e96. [PubMed] [Full text]

• Liu, W., Wan, J., Meng, X., Flower, D.R. and Li, T. (2007) In silico prediction of peptide-MHC binding affinity using SVRMHC. Methods Mol Biol , 409 , 283-291. [PubMed]

• Gong, W., Ren, Y., Xu, Q., Wang, Y., Lin, D., Zhou, H. and Li, T. (2006) Integrated siRNA design based on surveying of features associated with high RNAi effectiveness. BMC Bioinformatics , 7 , 516. [PubMed] [Full text]

• Wan, J., Liu, W., Xu, Q., Ren, Y., Flower, D.R. and Li, T. (2006) SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics , 7 , 463. [PubMed] [Full text]

• Liu, W., Meng, X., Xu, Q., Flower, D.R. and Li, T. (2006) Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics , 7 , 182. [PubMed] [Full text]

  [Identified by Thomson Scientific (Essential Scientific Indicators, ESI) as a "New Hot Paper" in the field of Computer Science in November-December 2007, selected by virtue of being cited among the top one-tenth of one percent (0.1%) in the corresponding bimonthly period in the field. Interview available at http://www.esi-topics.com/nhp/2007/november-07-Flower_Li.html.]

• Ren, Y., Gong, W., Xu, Q., Zheng, X., Lin, D., Wang, Y. and Li, T. (2006) siRecords: an extensive database of mammalian siRNAs with efficacy ratings. Bioinformatics , 22 , 1027-1028. [PubMed]

• Chumakov, S., Belapurkar, C., Putonti, C., Li, T., Pettitt, B.M., Fox, G.E., Willson, R.C. and Fofanov, Y. (2005) Theoretical Basis for Universal Identification Systems for Bacteria and Viruses. Journal of Biological Physics and Chemistry , 5 , 121-128.

• Tucker, D.L., Karouia, F., Wang, J., Luo, Y., Li, T., Willson, R.C., Fofanov, Y. and Fox, G.E. (2005) Effect of an Artificial RNA Marker on Gene Expression in Escherichia coli. Appl Environ Microbiol , 71 , 4156-4159. [PubMed]

Collaborators