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中华肾病研究电子杂志 ›› 2017, Vol. 06 ›› Issue (01) : 14 -19. doi: 10.3877/cma.j.issn.2095-3216.2017.01.004

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生物信息学技术及其在肾脏病研究中的应用
陈禹保1,(), 闫鹏程1   
  1. 1. 100094 北京市计算中心生物计算事业部、北京市基因测序与功能分析工程技术研究中心
  • 收稿日期:2017-01-12 出版日期:2017-02-28
  • 通信作者: 陈禹保
  • 基金资助:
    863计划项目(2008AA02Z441); 北京市自然基金委员会重大项目(7120001); 北京市科学技术研究院创新团队项目(IG201406N)

Bioinformatics analysis and its applications in kidney disease studies

Yubao Chen1,(), Pengcheng Yan1   

  1. 1. Department of Computational Biology in Beijing Computing Center, Beijing Engineering Research Centre of Gene Sequencing and Functional Analysis, Beijing Key Laboratory of Cloud Computing Key Technology and Application, Beijing 100094, China
  • Received:2017-01-12 Published:2017-02-28
  • Corresponding author: Yubao Chen
  • About author:
    Corresponding author: Chen Yubao, Email:
引用本文:

陈禹保, 闫鹏程. 生物信息学技术及其在肾脏病研究中的应用[J/OL]. 中华肾病研究电子杂志, 2017, 06(01): 14-19.

Yubao Chen, Pengcheng Yan. Bioinformatics analysis and its applications in kidney disease studies[J/OL]. Chinese Journal of Kidney Disease Investigation(Electronic Edition), 2017, 06(01): 14-19.

高通量组学技术的快速发展获得了海量不同类型的生物分子数据。生物信息学技术的快速发展则帮助我们可以深入解读如此庞大的数据,理解生命或疾病发生过程中潜在的分子机制。高通量组学技术与生物信息学技术已经在肾脏病研究中得到了广泛应用,帮助我们更好地理解肾脏病发生的遗传基础和背后的生物学过程。随着高通量组学技术和生物信息学技术的快速发展,多种组学数据的整合生物信息学分析将为肾脏病的病理过程、早期诊断及治疗决策研究提供帮助。本文中,主要讨论生物信息学技术与高通量组学技术应用的进展,并以多种肾脏病研究为例介绍了相关方法的应用。

Under the recent development of high-throughput omics technologies, data of different omics can be accumulated to an enormous number easily. The rapid development of bioinformatics technology helps us to interpret such a large amount of data, and to understand the underlying molecular mechanisms at baseline and in disease conditions. High-throughput omics technologies and bioinformatics analysis have been widely used in the study of kidney diseases, helping us to better understand the genetic basis and biological process behind kidney diseases. Under the aid of the rapid development of omics technology and analysis methods, multi-omics will be widely used to provide more information on biological and pathological processes of kidney diseases, further giving guidance on the early diagnosis and treatment decisions in kidney diseases. In this review, the authors mainly discussed the development of high-throughput omics technology and bioinformatics analysis with examples of kidney disease research.

表1 肾脏病研究相关的数据库
[1]
Metzker ML. Sequencing technologies - the next generation [J]. Nat Rev Genet, 2010, 11(1): 31-46.
[2]
Ozsolak F, Milos PM. RNA sequencing: advances, challenges and opportunities [J]. Nat Rev Genet, 2011, 12(2): 87-98.
[3]
Laird PW. Principles and challenges of genomewide DNA methylation analysis [J]. Nat Rev Genet, 2010, 11(3): 191-203.
[4]
Altelaar AF, Munoz J, Heck AJ. Next-generation proteomics: towards an integrative view of proteome dynamics [J]. Nat Rev Genet, 2013, 14(1): 35-48.
[5]
Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma [J]. Nature, 2011, 474(7353): 609-615.
[6]
Cancer Genome Atlas Research Network, Kandoth C, Schultz N, et al. Integrated genomic characterization of endometrial carcinoma [J]. Nature, 2013, 497(7447): 67-73.
[7]
Zhang H, Liu T, Zhang Z, et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer [J]. Cell, 2016, 166(3): 755-765.
[8]
Rubingh CM, Bijlsma S, Derks EP, et al. Assessing the performance of statistical validation tools for megavariate metabolomics data [J]. Metabolomics, 2006, 2(2): 53-61.
[9]
Brougham DF, Ivanova G, Gottschalk M, et al. Artificial neural networks for classification in metabolomic studies of whole cells using 1H nuclear magnetic resonance [J]. J Biomed Biotechnol, 2011, 2011: 158094.
[10]
Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions [J]. Nat Rev Genet, 2015, 16(2): 85-97.
[11]
Fridley BL, Lund S, Jenkins GD, et al. A Bayesian integrative genomic model for pathway analysis of complex traits [J]. Genet Epidemiol, 2012, 36(4): 352-359.
[12]
Lanckriet GR, De Bie T, Cristianini N, et al. A statistical framework for genomic data fusion [J]. Bioinformatics, 2004, 20(16): 2626-2635.
[13]
Kim D, Shin H, Song YS, et al. Synergistic effect of different levels of genomic data for cancer clinical outcome prediction [J]. J Biomed Inform, 2012, 45(6): 1191-1198.
[14]
Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs [J]. Nucleic Acids Res, 2017, 45(D1): D353-D361.
[15]
Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life [J]. Nucleic Acids Res, 2015, 43(Database issue): D447-D452.
[16]
Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants [J]. Nucleic Acids Res, 2012, 40(Database issue): D930-D934.
[17]
Boyle AP, Hong EL, Hariharan M, et al. Annotation of functional variation in personal genomes using RegulomeDB [J]. Genome Res, 2012, 22(9): 1790-1797.
[18]
Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM.org: Online Mendelian Inheritance in Man (OMIM(R), an online catalog of human genes and genetic disorders [J]. Nucleic Acids Res, 2015, 43(Database issue): D789-D798.
[19]
Forbes SA, Bindal N, Bamford S, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer [J]. Nucleic Acids Res, 2011, 39(Database issue): D945-D950.
[20]
Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations [J]. Nucleic Acids Res, 2014, 42(Database issue): D1001-D1006.
[21]
Feng C, Xiong Z, Jiang H, et al. Genetic alteration in notch pathway is associated with better prognosis in renal cell carcinoma [J]. Biofactors, 2016, 42(1): 41-48.
[22]
Kottgen A, Glazer NL, Dehghan A, et al. Multiple loci associated with indices of renal function and chronic kidney disease [J]. Nat Genet, 2009, 41(6): 712-717.
[23]
Kottgen A, Hwang SJ, Larson MG, et al. Uromodulin levels associate with a common UMOD variant and risk for incident CKD [J]. J Am Soc Nephrol, 2010, 21(2): 337-344.
[24]
Pattaro C, Kottgen A, Teumer A, et al. Genome-wide association and functional follow-up reveals new loci for kidney function [J]. PLoS Genet, 2012, 8(3): e1002584.
[25]
Sandholm N, Salem RM, McKnight AJ, et al. New susceptibility loci associated with kidney disease in type 1 diabetes [J]. PLoS Genet, 2012, 8(9): e1002921.
[26]
Yu XQ, Li M, Zhang H, et al. A genome-wide association study in Han Chinese identifies multiple susceptibility loci for IgA nephropathy [J]. Nat Genet, 2011, 44(2): 178-182.
[27]
Otto EA, Hurd TW, Airik R, et al. Candidate exome capture identifies mutation of SDCCAG8 as the cause of a retinal-renal ciliopathy [J]. Nat Genet, 2010, 42(10): 840-850.
[28]
Badal SS, Danesh FR. MicroRNAs and their applications in kidney diseases [J]. Pediatr Nephrol, 2015, 30(5): 727-740.
[29]
Woroniecka KI, Park AS, Mohtat D, et al. Transcriptome analysis of human diabetic kidney disease [J]. Diabetes, 2011, 60(9): 2354-2369.
[30]
Godwin JG, Ge X, Stephan K, et al. Identification of a microRNA signature of renal ischemia reperfusion injury [J]. Proc Natl Acad Sci USA, 2010, 107(32): 14339-14344.
[31]
Schanstra JP, Mischak H. Proteomic urinary biomarker approach in renal disease: from discovery to implementation [J]. Pediatr Nephrol, 2015, 30(5): 713-725.
[32]
Menon V, Shlipak MG, Wang X, et al. Cystatin C as a risk factor for outcomes in chronic kidney disease [J]. Ann Intern Med, 2007, 147(1): 19-27.
[33]
Nauta FL, Boertien WE, Bakker SJ, et al. Glomerular and tubular damage markers are elevated in patients with diabetes [J]. Diabetes Care, 2011, 34(4): 975-981.
[34]
Mitsnefes MM, Kathman TS, Mishra J, et al. Serum neutrophil gelatinase-associated lipocalin as a marker of renal function in children with chronic kidney disease [J]. Pediatr Nephrol, 2007, 22(1): 101-108.
[35]
Hirayama A, Nakashima E, Sugimoto M, et al. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy [J]. Anal Bioanal Chem, 2012, 404(10): 3101-3109.
[36]
Toyohara T, Akiyama Y, Suzuki T, et al. Metabolomic profiling of uremic solutes in CKD patients [J]. Hypertens Res, 2010, 33(9): 944-952.
[37]
Smyth LJ, McKay GJ, Maxwell AP, et al. DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease [J]. Epigenetics, 2014, 9(3): 366-376.
[38]
Ko YA, Mohtat D, Suzuki M, et al. Cytosine methylation changes in enhancer regions of core pro-fibrotic genes characterize kidney fibrosis development [J]. Genome Biol, 2013, 14(10): R108.
[39]
Pesce F, Pathan S, Schena FP. From-omics to personalized medicine in nephrology: integration is the key [J]. Nephrol Dial Transplant, 2013, 28(1): 24-28.
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