A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease

BMC Medical Genomics

Gregory J Kato⁵  Peter J Munson⁴  Christopher J O‘Donnell¹  Daniel Levy²  Kimberly Woodhouse³  Poching Liu³  Yanqin Yang³  Jennifer Barb⁴  Nalini Raghavachari³ 

[1] The Center for Cardiovascular Genomics and the Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA;The Center for Population Studies and the Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA;Genomics Core Facility, Genetics and Development Biology, NHLBI, The National Institutes of Health, 10 Center Drive, Bldg 10, 8C 103B, Bethesda, 20892, USA;Mathematical and Statistical computing Laboratory, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA;Hematology Branch, National Institutes of Health, Bethesda, MD, USA

关键词: Clinical genomics;    Transcriptome;    Exon arrays;    RNA-Seq;    Sickle cell disease;   

Others  :  1134834
DOI  :  10.1186/1755-8794-5-28

 received in 2011-11-23, accepted in 2012-06-29,  发布年份 2012

【 摘 要 】

Background

Transcriptomic studies in clinical research are essential tools for deciphering the functional elements of the genome and unraveling underlying disease mechanisms. Various technologies have been developed to deduce and quantify the transcriptome including hybridization and sequencing-based approaches. Recently, high density exon microarrays have been successfully employed for detecting differentially expressed genes and alternative splicing events for biomarker discovery and disease diagnostics. The field of transcriptomics is currently being revolutionized by high throughput DNA sequencing methodologies to map, characterize, and quantify the transcriptome.

Methods

In an effort to understand the merits and limitations of each of these tools, we undertook a study of the transcriptome in sickle cell disease, a monogenic disease comparing the Affymetrix Human Exon 1.0 ST microarray (Exon array) and Illumina’s deep sequencing technology (RNA-seq) on whole blood clinical specimens.

Results

Analysis indicated a strong concordance (R = 0.64) between Exon array and RNA-seq data at both gene level and exon level transcript expression. The magnitude of differential expression was found to be generally higher in RNA-seq than in the Exon microarrays. We also demonstrate for the first time the ability of RNA-seq technology to discover novel transcript variants and differential expression in previously unannotated genomic regions in sickle cell disease. In addition to detecting expression level changes, RNA-seq technology was also able to identify sequence variation in the expressed transcripts.

Conclusions

Our findings suggest that microarrays remain useful and accurate for transcriptomic analysis of clinical samples with low input requirements, while RNA-seq technology complements and extends microarray measurements for novel discoveries.

【 授权许可】

   
2012 Raghavachari et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1 .

Figure 2 .

Figure 3 .

Figure 4 .

Figure 5 .

Figure 6 .

Figure 7 .

Figure 8 .

Figure 9 .

【 参考文献 】

• [1]Melton SD, Genta RM, Souza RF: Biomarkers and molecular diagnosis of gastrointestinal and pancreatic neoplasms. Nat Rev Gastroenterol Hepatol 2010, 7(11):620-628.
• [2]Rudan I: New technologies provide insights into genetic basis of psychiatric disorders and explain their co-morbidity. Psychiatr Danub 2010, 22(2):190-192.
• [3]Weitzel JN, Blazer KR, Macdonald DJ, Culver JO, Offit K: Genetics, genomics, and cancer risk assessment: State of the Art and Future Directions in the Era of Personalized Medicine. CA Cancer J Clin 2011, 61(5):327-359.
• [4]Yang X, Jiao R, Yang L, Wu LP, Li YR, Wang J: New-generation high-throughput technologies based 'omics' research strategy in human disease. Yi Chuan 2011, 33(8):829-846.
• [5]Offit K: Personalized medicine: new genomics, old lessons. Hum Genet 2011, 130(1):3-14.
• [6]Lam CW, Lau KC, Tong SF: Microarrays for personalized genomic medicine. Adv Clin Chem 2011, 52:1-18.
• [7]Abdueva D, Wing MR, Schaub B, Triche TJ: Experimental comparison and evaluation of the Affymetrix exon and U133Plus2 GeneChip arrays. PLoS One 2007, 2(9):e913.
• [8]Bradford JR, Hey Y, Yates T, Li Y, Pepper SD, Miller CJ: A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC Genomics 2010, 11:282.
• [9]Liu JM, Camilli A: Discovery of bacterial sRNAs by high-throughput sequencing. Methods Mol Biol 2011, 733:63-79.
• [10]Cirulli ET, Singh A, Shianna KV, Ge D, Smith JP, Maia JM, Heinzen EL, Goedert JJ, Goldstein DB: Screening the human exome: a comparison of whole genome and whole transcriptome sequencing. Genome Biol 2010, 11(5):R57.
• [11]Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, Lao K, Surani MA: RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 2010, 5(3):516-535.
• [12]Nagalakshmi U, Waern K, Snyder M: RNA-Seq: a method for comprehensive transcriptome analysis. Curr Protoc Mol Biol 2010, Chapter 4:11-13. Unit 4 11
• [13]Tariq MA, Kim HJ, Jejelowo O, Pourmand N: Whole-transcriptome RNAseq analysis from minute amount of total RNA. Nucleic Acids Res 2011, 39(18):e120.
• [14]Zhao J, Grant SF: Advances in whole genome sequencing technology. Curr Pharm Biotechnol 2010, 12(2):293-305.
• [15]Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008, 18(9):1509-1517.
• [16]Liu GE: Recent Applications of DNA Sequencing Technologies in Food, Nutrition and Agriculture. Recent Pat Food Nutr Agric 2011, 3(3):187-195.
• [17]Raghavachari N, Xu X, Munson PJ, Gladwin MT: Characterization of whole blood gene expression profiles as a sequel to globin mRNA reduction in patients with sickle cell disease. PLoS One 2009, 4(8):e6484.
• [18]Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 1995, 57(1):289-300.
• [19]Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25(9):1105-1111.
• [20]Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26(6):841-842.
• [21]Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5(7):621-628.
• [22]Bieche I, Parfait B, Tozlu S, Lidereau R, Vidaud M: Quantitation of androgen receptor gene expression in sporadic breast tumors by real-time RT-PCR: evidence that MYC is an AR-regulated gene. Carcinogenesis 2001, 22(9):1521-1526.
• [23]Ma Q, Lu AY: Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol Rev 2011, 63(2):437-459.
• [24]Mardis ER: A decade's perspective on DNA sequencing technology. Nature 2011, 470(7333):198-203.
• [25]Taylor BS, Ladanyi M: Clinical cancer genomics: how soon is now? J Pathol 2010, 223(2):318-326.
• [26]Tommasi S, Danza K, Pilato B, De Summa S: Innovative technology for cancer risk analysis. Ann Oncol 2011, 22(Suppl 1):i37-43.
• [27]Schweiger MR, Kerick M, Timmermann B, Isau M: The power of NGS technologies to delineate the genome organization in cancer: from mutations to structural variations and epigenetic alterations. Cancer Metastasis Rev 2011, 30(2):199-210.
• [28]Settle SH, Sulman EP: Tumor profiling: development of prognostic and predictive factors to guide brain tumor treatment. Curr Oncol Rep 2010, 13(1):26-36.
• [29]Xu W, Seok J, Mindrinos MN, Schweitzer AC, Jiang H, Wilhelmy J, Clark TA, Kapur K, Xing Y, Faham M, et al.: Human transcriptome array for high-throughput clinical studies. Proc Natl Acad Sci U S A 2011, 108(9):3707-3712.
• [30]Morris CR: Mechanisms of vasculopathy in sickle cell disease and thalassemia. Hematology Am Soc Hematol Educ Program 2008, 177-185.
• [31]Jison ML, Munson PJ, Barb JJ, Suffredini AF, Talwar S, Logun C, Raghavachari N, Beigel JH, Shelhamer JH, Danner RL, Gladwin MT: Blood mononuclear cell gene expression profiles characterize the oxidant, hemolytic, and inflammatory stress of sickle cell disease. Blood 2004, 104(1):270-280.
• [32]Steinberg MH, Brugnara C: Pathophysiological-based approaches to treatment of sickle cell disease. Annu Rev Med 2003, 54:89-112.
• [33]Raghavachari N, Xu X, Harris A, Villagra J, Logun C, Barb J, Solomon MA, Suffredini AF, Danner RL, Kato G, et al.: Amplified expression profiling of platelet transcriptome reveals changes in arginine metabolic pathways in patients with sickle cell disease. Circulation 2007, 115(12):1551-1562.
• [34]Kato GJ, Hebbel RP, Steinberg MH, Gladwin MT: Vasculopathy in sickle cell disease: Biology, pathophysiology, genetics, translational medicine, and new research directions. Am J Hematol 2009, 84(9):618-625.
• [35]Tanno T, Bhanu NV, Oneal PA, Goh SH, Staker P, Lee YT, Moroney JW, Reed CH, Luban NL, Wang RH, et al.: High levels of GDF15 in thalassemia suppress expression of the iron regulatory protein hepcidin. Nat Med 2007, 13(9):1096-1101.
• [36]Feldmann J, Callebaut I, Raposo G, Certain S, Bacq D, Dumont C, Lambert N, Ouachee-Chardin M, Chedeville G, Tamary H, et al.: Munc13-4 is essential for cytolytic granules fusion and is mutated in a form of familial hemophagocytic lymphohistiocytosis (FHL3). Cell 2003, 115(4):461-473.
• [37]Cotter PD, Willard HF, Gorski JL, Bishop DF: Assignment of human erythroid delta-aminolevulinate synthase (ALAS2) to a distal subregion of band Xp11.21 by PCR analysis of somatic cell hybrids containing X; autosome translocations. Genomics 1992, 13(1):211-212.
• [38]Cox TC, Bawden MJ, Martin A, May BK: Human erythroid 5-aminolevulinate synthase: promoter analysis and identification of an iron-responsive element in the mRNA. The EMBO journal 1991, 10(7):1891-1902.