Welcome to Jin Gu’s Homepage
Bioinformatics & Systems Biology Research Group
Jin Gu Ph.D. (古槿), Assistant Professor
MOE Key Laboratory of Bioinformatics
Tsinghua University, Beijing 100084, China
Email: wellgoo at gmail dot com
Phone (Lab): +86 10 62794294-866
Fax (Lab): +86 10 62773552
Last Update: May 24, 2015
Last Update: May 24, 2015
[2014/09/30] A computational-experimental integrative discovery paper is accepted: we identified a tumor suppressor miR-139 inhibiting ETS1 in colon cancer (Mol BioSyst 2014).
[2014/05/24] A new version of FastDMA (v1.2.1) is released. The and output formats for region-based analyses are improved. A Win32 version is also provided.
[2014/03/18] OncomiRDB, a database for the experimentally verified oncogenic and tumor-suppressive microRNAs, is accepted for publication by Bioinformatics.
1) MicroRNAs (miRNAs) regulatory networks. MiRNAs, a class of ~22nt endogenous small regulatory RNAs, play essential roles in multiple cellular processes. Most of the annotated protein-coding genes are predicted to be regulated by these tiny molecules through 3'-UTR sequence specific binding sites. Till now, ~1400 miRNAs are identified in human, but most of them are lack of functional annotations. We aim to develop new computational methods and theories to understand the principles of miRNA regulations and their functions during multiple biological processes, such as angiogenesis, inflammation and cancer. Now, we are trying to integrate gene expression data, protein-protein interaction (PPI) network data and miRNA target gene data to infer the dys-regulated miRNA pathways in cancers. We proposed to analyze the miRNA functions based on their target gene modularity in gene co-expression and PPI networks, especially in cancers (BMC Syst Biol 2010; Mol BioSyst 2014). We also work on building reference resources for studying cancer related miRNAs (oncomiRs) (oncomiRDB, Bioinformatics 2014).
2) Analyzing and modeling the regulatory and functional networks in cancers. Dys-regulated biological networks (including genetics, epigenetics, transcription regulation and miRNA regulation, etc.) in cancer development provide important information for cancer diagnosis and therapy. We are developing methods to identify the dys-regulated networks by integrating genomic, epigenomic, transcriptomic and proteomic data produced by high-throughput technology (BMC Syst Biol 2010; Mol BioSyst 2014). Now, we mainly focus on: the methods and tools to analyze DNA methylation data (FastDMA, PLoS ONE 2013); learning the hidden patterns and deep structures from large-scale cancer omic data; and the characteristics of cancer genomic and epigenomic landscapes.
Probabilistic Graphical Models: Principles and Techniques (Graduate course, from Fall 2011, with Dr. Michael Zhang)
Selected Topics in Bioinformatics (Graduate course, Spring 2010, with Dr. Shao Li)
[NEW!! Request via email] LRAcluster: a low-rank regularized probabilistic model for fast dimension reduction and integrative clustering of large-scale multi-omics data [Updated: 2015/05/24]
NP-miRNA: a network propagation based method for inferring perturbed miRNA regulatory networks [Updated: 2014/06/23]
[HOT!!] OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs [Updated: 2014/03/14]
FastDMA: an Infinium HumanMethylation450 beadchip (450k methylation array) analyzer [Updated: 2014/05/24]
miRHiC: enrichment analysis of a set of genes in hierarchical gene co-expression signatures [Updated: 2013/03/19]
sGSCA: signature-based gene set co-expression analysis (using sparse canonical correlation analysis) [Updated: 2013/11/22]
ClustEx: responsive gene module identification package (v0.32) [Updated: 2012/05/03]
PCS: de novo k-mer analysis package (v1.5) [Updated: 2009/10/8]
LQ_Bioinfo: a collection for useful resources in Bioinformatics and Systems Biology [Updated: 2011/10/07] (Click Here)
Applications in Signaling Transduction Research
//Cell Signaling: Fundamental Theory and Practical Techniques [In Chinese], Edited by Hongyang Wang
Scientific & Technological Education Publisher
Fast dimension reduction and integrative
clustering of large-scale multi-omics data using low-rank approximation:
application to cancer molecular classification.
Submitted Manuscript, 2015. [Software]
|Gene module based regulator inference identifying miR-139 as a tumor suppressor in colorectal cancer.|
|Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method.|
|OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs.|
|Epigenetic modification of
liver tumour-initiating cell properties by targeting Rb binding protein
2015, 64(1):156-167. [Abstract]
|Inferring the perturbed microRNA regulatory networks in cancer using hierarchical gene co-expression signatures.|
|FastDMA: An Infinium HumanMethylation450 Beadchip Analyzer.|
|Imbalanced network biomarkers for traditional Chinese medicine Syndrome
in gastritis patients.
Scientific Reports 2013, 3:1543. [Full
|Inferring pathway crosstalk networks using gene set co-expression signatures.|
Chromatin state and microRNA determine different gene expression dynamics
responsive to TNF stimulation.
Genomics 2012, 100(5):297-302. [Abstract]
|Towards integrative annotating the cell-type specific gene functional and signaling map in vascular endothelial cells.|
|Time-course network analysis reveals TNF-alpha can promote G1/S transition of cell cycle in vascular endothelial cells.|
|Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis.|
|A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions.|
|Integrative computational identifications of the signaling pathway network related to TNF-alpha stimulus in vascular endothelial cells.|
|Brief review: frontiers in the computational studies of gene regulations.|
|Identification of phylogenetically conserved microRNA cis-regulatory elements across 12 Drosophila species.|
|Computational Identification of 99 Invertebrate MicroRNAs with Comparative Genomics.|
|Identifications of conserved 7-mers in 3'-UTRs and microRNAs in Drosophila.|
|Primary Transcripts and Expressions of Mammal Intergenic MicroRNAs Detected by Mapping ESTs to Their Flanking Sequences.|
|MicroRNA Identification Based on Sequence and Structure Alignment.|