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Correlating Expression Data with Gene function Using Gene Ontology

Chinese Journal of Chemistry 2006年第9期24卷 1247-1254页

作者：刘琪邓勇王川石铁流李亦学 School of Life Science & Biotechnology Shanghai Jiao Tong University Shanghai 200240 China Zhejiang Police Vocational Academy Hangzhou Zhejiang 310018 China Biomformatton Center Shanghat Instttutesfor Btologtcal Sctences Chinese Academy of Sciences Shanghai 200031 China

Clustering is perhaps one of the most widely used tools for microarray data analysis. Proposed roles for genes of unknown function are inferred from clusters of genes similarity expressed across many biological conditions. However, whether function annotation by similarity metrics is reliable or not and to what extent the similarity in gene expression patterns is useful for annotation of gene functions, has not been evaluated. This paper made a comprehensive research on the correlation between the similarity of expression data and of gene functions using Gene Ontology. It has been found that although the similarity in expression patterns and the similarity in gene functions are significantly dependent on each other, this association is rather weak. In addition, among the three categories of Gene Ontology, the similarity of expression data is more useful for cellular component annotation than for biological process and molecular function. The results presented are interesting for the gene functions prediction research area.

关键词： microarray data gene ontology similarity of expression data function annotation

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Comprehensive functional annotation of susceptibility variants identifies genetic heterogeneity between lung adenocarcinoma and squamous cell carcinoma

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Frontiers of Medicine 2021年第2期15卷 275-291页

作者： Na Qin Yuancheng Li Cheng Wang Meng Zhu Juncheng Dai Tongtong Hong Demetrius Albanes Stephen Lam Adonina Tardon Chu Chen Gary Goodman Stig EBojesen Maria Teresa Landi Mattias Johansson Angela Risch H-Erich Wichmann Heike Bickeboller Gadi Rennert Susanne Arnold Paul Brennan John KField Sanjay Shete Loic Le Marchand Olle Melander Hans Brunnstrom Geoffrey Liu Rayjean JHung Angeline Andrew Lambertus AKiemeney Shan Zienolddiny Kjell Grankvist Mikael Johansson Neil Caporaso Penella Woll Philip Lazarus Matthew BSchabath Melinda CAldrich Victoria LStevens Guangfu Jin David CChristiani Zhibin Hu Christopher IAmos Hongxia Ma Hongbing Shen Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjing 211166China State Key Laboratory of Reproductive Medicine Center for Global HealthNanjing Medical UniversityNanjing 211166China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer MedicineNanjing Medical UniversityNanjing 211166China China International Cooperation Center for Environment and Human Health School of Public HealthNanjing Medical UniversityNanjing 211166China Division of Cancer Epidemiology and Genetics National Cancer InstituteBethesdaMD 20892-9304USA Department of Integrative Oncology British Columbia Cancer AgencyVancouverBC V5Z 1L3Canada Faculty of Medicine University of Oviedo and CIBERESPOviedo 33006Spain Program in Epidemiology Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWA 98109-1024USA Public Health Sciences Division Swedish Cancer InstituteSeattleWA 98026USA Department of Clinical Biochemistry Copenhagen University HospitalCopenhagen DK-1017Denmark National Cancer Institute BethesdaMD 20892-9304USA Genetic Epidemiology Group International Agency for Research on CancerLyon 69372France Cancer Center Cluster Salzburg at PLUS Department of Molecular BiologyUniversity of SalzburgHeidelberg 5020Austria Institute of Medical Informatics Biometry and EpidemiologyChair of EpidemiologyLudwig Maximilians UniversityMunichBavaria 80539Germany Department of Genetic Epidemiology University Medical Center GoettingenGoettingen 37075Germany Technion Faculty of Medicine Carmel Medical CenterHaifa 3448516Israel Markey Cancer Center University of KentuckyLexingtonKY 40506-0054USA Department of Molecular and Clinical Cancer Medicine Roy Castle Lung Cancer Research ProgrammeThe University of Liverpool Institute of Translational MedicineLiverpool L697Z

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer(NSCLC)risk,biological mechanisms of these variants remain largely *** integrating a large-scale genotype data of 15581 lung adenocarcinoma(AD)cases,8350 squamous cell carcinoma(SqCC)cases,and 27355 controls,as well as multiple transcriptome and epigenomic databases,we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility *** identified 3064 credible risk variants for NSCLC,which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive *** factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was *** annotation and genebased analysis implicated 894 target genes,including 274 specifics for AD and 123 for SqCC,which were overrepresented in somatic driver genes(ER=1.95,P=0.005).Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways,while SqCC genes were homologous recombination deficiency *** results illustrate the molecular basis of both wellstudied and new susceptibility loci of NSCLC,providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.

关键词： lung cancer genome-wide association study function annotation immune homologous recombination repair deficiency genetic heterogeneity

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Genome-wide identification of cancer-related polyadenylated and non-polyadenylated RNAs in human breast and lung cell lines

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Science China(Life Sciences) 2013年第5期56卷 503-512,1-4页

作者： ZHAO GuoGuang JIAO Fei LIAO Qi LUO HaiTao LI Hui SUN Liang BU DeChao YU KunTao ZHAO Yi CHEN RunSheng Bioinformatic Research Group Key Laboratory of Intelligent Information ProcessingAdvanced Computing Research LaboratoryInstitute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences Department of Biochemistry and Molecular Biology Binzhou Medical College Department of Preventive Medicine School of MedicineNingbo University College of Computer Science and Technology Jilin University Bioinformatics Laboratory and National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China

Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 ***(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) mRNA are largely *** studies have shown that a significant proportion of gene transcripts are poly(A) or bimorphic(containing both poly(A)+ and poly(A) transcripts).We compared the expression levels of poly(A) and poly(A)+ RNA mRNAs in normal and cancer cell *** also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A) transcriptome sequences between a normal human mammary gland cell line(HMEC) and a breast cancer cell line(MCF-7),as well as between a normal human lung cell line(NHLF) and a lung cancer cell line(A549).The data showed that normal and cancer cell lines differentially express these two forms of *** ontology(GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their *** data showed that cell cycle-,apoptosis-,and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts,which were also associated with the ***,translational elongation and translation functions were also found for the poly(A) protein-coding genes in cancer cell *** demonstrate that poly(A) transcripts play an important role in cancer development.

关键词： polyadenylation non-polyadenylation function annotation high throughput sequencing cancer bioinformatics

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