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A novel model-free approach for reconstruction of time-delayed gene regulatory networks

中国科学：生命科学英文版 2006年第2期49卷 190-200页

作者： JIANG Wei1,2, LI Xia1,2,3,4, GUO Zheng1,2,3, LI Chuanxing1, WANG Lihong1 & RAO Shaoqi1,5 1. Department of Bioinformatics, Harbin Medical University, Harbin 150086, China 2. Department of Computer Science, Harbin Institute of Technology, Harbin 150080, China 3. School of Life Science and Technology, Tongji University, Shanghai 200092, China 4. Biomedical Engineering Institute, Capital University of Medical Sciences, Beijing 100054, China 5. departments of cardiovascular medicine and molecular cardiology, cleveland clinic foundation, cleveland, ohio 44195, usa Department of Bioinformatics Harbin Medical University Harbin 150086 China Department of Computer Science Harbin Institute of Technology Harbin 150080 China School of Life Science Technology Tongji University Shanghai 200092 China Biomedical Engineering Institute Capital University of Medical Sciences Beijing 100054 China departments of cardiovascular medicine molecular cardiology Cleveland Clinic Foundation Cleveland Ohio 44195 USA

Reconstruction of genetic networks is one of the key scientific challenges in functional genomics. This paper describes a novel approach for addressing the regulatory dependencies be-tween genes whose activities can be delayed by multiple units of time. The aim of the proposed ap-proach termed TdGRN (time-delayed gene regulatory networking) is to reversely engineer the dy-namic mechanisms of gene regulations, which is realized by identifying the time-delayed gene regu-lations through supervised decision-tree analysis of the newly designed time-delayed gene expres-sion matrix, derived from the original time-series microarray data. A permutation technique is used to determine the statistical classification threshold of a tree, from which a gene regulatory rule(s) is ex-tracted. The proposed TdGRN is a model-free approach that attempts to learn the underlying regula-tory rules without relying on any model assumptions. Compared with model-based approaches, it has several significant advantages: it requires neither any arbitrary threshold for discretization of gene transcriptional values nor the definition of the number of regulators (k). We have applied this novel method to the publicly available data for budding yeast cell cycling. The numerical results demonstrate that most of the identified time-delayed gene regulations have current biological knowledge supports.

关键词： gene regulatory networks, time delayed, decision tree.

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Screening of GJB2 mutations in Chinese population

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Journal of Otology 2007年第1期2卷 18-22页

作者： HAN Ming-kun1, HAN Dong-yi1, GUO Yu-fen2, LI Qing-zhong3, ZHAO Ya-li1, 4, RAO Shao-qi1, 5, 6, YUAN Hu1, ZONG Liang1, GUAN Jing2, XU Bai-cheng2, WANG Da-yong1, LAN lan1, WANG Qiu-ju1, 7 1 Department of Otorhinolaryngology/ Head and Neck Surgery, and Institute of Otolaryngology, China People Liberation Army General Hospital, Beijing, China 2 Department of Otorhinolaryngology/ Head and Neck Surgery, the Second Affiliated Hospital of LanZhou University, Lanzhou, China 3 Deparment of Otlolaryngology, Eye and ENT Hospital of Fudan University, Shanghai, China 4 Department of Biochemistry and molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China 5 departments of cardiovascular medicine and molecular cardiology, the cleveland clinic foundation, cleveland, ohio, usa 6 Department of Bioinformatics, Harbin Medical University, Harbin, China 7 China National Human Genome Center, Beijing, China Department of Otorhinolaryngology/ Head and Neck Surgery and Institute of Otolaryngology China People Liberation Army General Hospital Beijing China Department of Otorhinolaryngology/ Head and Neck Surgery the Second Affiliated Hospital of LanZhou University Lanzhou China Deparment of Otlolaryngology Eye and ENT Hospital of Fudan University Shanghai China Department of Biochemistry and molecular Biology Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China departments of cardiovascular medicine and molecular cardiology the Cleveland Clinic Foundation Cleveland Ohio USA Department of Bioinformatics Harbin Medical University Harbin China China National Human Genome Center Beijing China

The GJB2 gene(connexin 26) has been shown to be responsible for DFNB1 and DFNA3. We screened the GJB2 gene in 488 patients with prelingual deafness(Group 1), 124 with postlingual deafness(Group 2), and 117 normal hearing subjects(Group 3). We found that, in Group 1, 65 patients(13.32%) were homozygotes or compound heterozygotes and 51 patients (10.45%) carried a single pathogenic mutation. The 235delC mutation was the most frequent mutation, accounting for 73.22% of the known pathogenic alleles in Group 1. No homozygotes or compound heterozygotes were detected in Group 2 or Group 3. Some postlingual deaf patients (2.42%) and normal hearing subjects(4.27%) were 235delC carriers. Our preliminary data indicate that 235delC, the most frequent mutation identified in this study, is a major cause for prelingual deafness.

关键词： prelingual deafness postlingual deafness, GJB2 235delC Chinese

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Mapping and characterization of two relevance networks from SNP and gene levels

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Progress in Natural Science:Materials International 2009年第5期19卷 653-657页

作者： Wei Jiang a,1, Lijie Zhang b,1, Bo Na a, Lihong Wanga, Jiankai Xu a, Xia Li a,b,c, Yadong Wangb, Shaoqi Rao da College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China b Department of Mathematics and Computer Science, Harbin Institute of Technology, Harbin 150080, China c Department of Bioinformatics, Capital University of Medical Sciences, Beijing 100084, China d Department of cardiovascular medicine and molecular cardiology, the cleveland clinic foundation, 9600 Euclid Avenue, cleveland, OH 44195, usa [a]College of Bioinformatics Science and Technology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province Harbin Medical University Harbin 150081 China [b]Department of Mathematics and Computer Science Harbin Institute of Technology Harbin 150080 China [c]Department of Bioinformatics Capital University of Medical Sciences Beijing 100084 China [d]Department of Cardiovascular Medicine and Molecular Cardiology The Cleveland Clinic Foundation 9600 Euclid Avenue Cleveland OH 44195 USA

Variations of gene expression and DNA sequence are genetically associated. The goal of this study was to build genetic networks to map from SNPs to gene expressions and to characterize the two different kinds of networks. We employed mutual information to evaluate the strength of SNP-SNP and gene-gene associations based on SNPs identity by descent (IBD) data and differences of gene expressions. We applied the approach to one dataset of Genetics of Gene Expression in Humans, and discovered that both the SNP relevance network and the gene relevance network approximated the scale-free topology. We also found that 12.09% of SNP-SNP interactions matched 24.49% of gene-gene interactions, which was consistent with that of the previous studies. Finally, we identified 49 hub SNPs and 115 hub genes in their relevance networks, in which 27 hub SNPs were associated with 25 hub genes.

关键词： SNP relevance network Gene relevance network Network mapping Network topology

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Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

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Science China(Life Sciences) 2007年第1期50卷 125-134页

作者： GAO Lei1, LI Xia1,2, GUO Zheng1,2, ZHU MingZhu1, LI YanHui1 & RAO ShaoQi1,3 1 Department of Bioinformatics, Harbin Medical University, Harbin 150086, China 2 School of Biology Science and Technology, Tongji University, Shanghai 200092, China 3 departments of molecular cardiology and cardiovascular medicine, the cleveland clinic foundation, 9500 Euclid Avenue, Cleve-land, ohio 44195, usa 1. Department of Bioinformatics Harbin Medical University Harbin 150086 China 2. School of Biology Science and Technology Tongji University Shanghai 200092 China 3. departments of molecular cardiology and cardiovascular medicine the Cleveland Clinic Foundation 9500 Euclid Avenue Cleveland Ohio 44195 USA

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.

关键词： gene expression profile protein-protein interaction Gene Ontology similarity gene function prediction

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