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Identification and prioritization of differentially expressed genes for time-series gene expression data

Frontiers of Computer Science 2018年第4期12卷 813-823页

作者： Linlin XING Maozu GUO Xiaoyan LIU Chunyu WANG School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

Identification of differentially expressed genes （DEGs） in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a fiat gene filter based on the Ljung-Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization ap- proach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.

关键词： time-series gene expression flat gene filter gene prioritization co-expression differentially expressed genes

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Integrating phenotypic features and tissue-specific information to prioritize disease genes

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Science China(Information Sciences) 2016年第7期59卷 5-14页

作者： Yue DENG Lin GAO Xingli GUO Bingbo WANG School of Computer Science and Technology Xidian University School of Software Xidian University

prioritization of candidate disease genes is crucial for improving medical care, and is one of the fundamental challenges in the post-genomic era. In recent years, different network-based methods for gene prioritization are proposed. Previous studies on gene prioritization show that tissue-specific protein-protein interaction(PPI) networks built by integrating PPIs with tissue-specific gene expression profiles can perform better than tissue-na¨?ve global PPI network. Based on the observations that diseases with similar phenotypes are likely to have common related genes, and genes associated with the same phenotype tend to interact with each other, we propose a method to prioritize disease genes based on a heterogeneous network built by integrating phenotypic features and tissue-specific information. In this heterogeneous network, the PPI network is built by integrating phenotypic features with a tissue-specific PPI network, and the disease network consists of the diseases that are associated with the same phenotype and tissue as the query disease. To determine the impacts of these two factors on gene prioritization, we test three typical network-based prioritization methods on heterogeneous networks consisting of combinations of different PPIs and disease networks built with or without phenotypic features and tissue-specific information. We also compare the proposed method with other tissuespecific networks. The results of case studies reveals that integrating phenotypic features with a tissue-specific PPI network improves the prioritization results. Moreover, the disease networks generated using our method not only show comparable performance with the widely used disease similarity dataset of 5080 human diseases,but are also effective for diseases that are not in the dataset.

关键词： gene prioritization tissue-specific network phenotype PPI network disease network

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Walking on multiple disease-gene networks to prioritize candidate genes

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Journal of Molecular Cell Biology 2015年第3期7卷 214-230页

作者： Rui Jiang MOE Key Laboratory of Bioinformatics Division and Center for Synthetic&Systems BiologyTNLISTDepartment of AutomationTsinghua UniversityBeijing 100084China Department of Statistics Stanford UniversityStanfordCA 94305USA

Uncovering causal genes for human inherited diseases,as the primary step toward understanding the pathogenesis of these diseases,requires a combined analysis of genetic and genomic *** bioinformatics methods have been designed to prioritize candidate genes resulting fromgenetic linkage analysis or association studies,the coverage of both diseases and genes in existing methods is quite limited,thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome *** overcome this limitation,we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic *** derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 *** on a pair of phenotype and gene similarities,we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease.A weighted version of the Fisher’s method with dependent correction is adopted to integrate 27 scores obtained in this way,and a final q-value is calibrated for prioritizing candidate genes.A series of validation experiments are conducted to demonstrate the superior performance of this *** further show the effectiveness of this method in exome sequencing studies of autism and epileptic *** online service and the standalone software of pgWalk can be found at http://***/jianglab/pgwalk.

关键词： disease-gene network gene prioritization random walk data fusion

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prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

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Protein & Cell 2012年第7期3卷 526-534页

作者： Suhua Chang Weina Zhang Lei Gao Jing Wang Key Laboratory of Mental Health Institute of PsychologyChinese Academy of SciencesBeijing 100101China Graduate University of the Chinese Academy of Sciences Beijing 100039China􀀍

Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased *** recent years,a large number of genetic studies for ADHD have been published and related ge-netic data has been accumulated *** pro-vide researchers a comprehensive ADHD genetic re-source,we previously developed the first genetic data-base for ADHD(ADHDgene).The abundant genetic data provides novel candidates for further ***,it also brings new challenge for selecting promising candidate genes for replication and verification *** this study,we surveyed the computational tools for candidate gene prioritization and selected five tools,which integrate multiple data sources for gene prioritiza-tion,to prioritize ADHD candidate genes in *** prioritization analysis resulted in 16 prioritized can-didate genes,which are mainly involved in several major neurotransmitter systems or in nervous system development *** these genes,nervous system development related genes,especially SNAP25,STX1A and the gene-gene interactions related with each of them deserve further *** results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

关键词： gene prioritization attention deficit hyper-activity disorder candidate genes multiple data sources

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Integration of network analysis and enrichment analysis to prioritize cancer-associated genes with microarray data

Integration of network analysis and enrichment analysis to p...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息学前沿研讨会高通量时代的生物信息学与系统生物学

作者： Chao Wu Jun Zhu Xuegong Zhang MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST and Department of AutomationTsinghua University

Background:To detect the most promising genes from the large list of candidate genes is defined as gene prioritization *** of cancer-associated genes would facilitate us to understand the mechanism of cancer development and provide us promising candidates of drug target. Methods:In this paper,we furthered our recent work and developed a method, Networked gene Prioritizer 2.0(NGP2),to prioritize cancer-associated *** existing network based methods(e.g.,heat kernel ranking method of PINTA) and the method based on differential expression analysis(DE analysis) prioritize genes by their difference between compared ***,NGP2 integrates network analysis and enrichment analysis to infer genes’ functional status in compared *** prioritizes genes in case data and control data respectively. Results:We applied NGP2,heat kernel ranking method of PINTA(HKR) and DE analysis on several breast cancer and lung cancer *** functional annotation results showed NGP2 performs better than HKR and DE *** enrichment results of topranking genes of HKR and DE analysis showed the pathways that are different between case and control samples while in NGP2 the enrichment results of top-ranking genes suggested the pathways that are activated in case samples and control samples *** addition, compared with HKR and DE analysis,the top-ranking genes of NGP2 tend to be enriched in cancer-associated *** the lung cancer datasets,we demonstrated that cell cycle related functional terms in gene Ontology(e.g.,"cell cycle") are activated in smoking lung cancer samples,non-smoking lung cancer samples but not in normal samples. Conclusions:In this paper,we have developed a method named NGP2,to prioritize cancer-associated *** results demonstrated NGP2 performs better than the existing methods.

关键词： gene prioritization cancer-associated genes microarray

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