A robust estimation of exon expression to identify alternative spliced genes applied to human tissues and cancer samples

BMC Genomics

Javier De Las Rivas³  Lars Bullinger⁴  Jesus M Hernandez-Rivas²  Anna Dolnik⁴  Beatriz Roson-Burgo³  Alberto Risueño¹ 

[1] Celgene Institute for Translational Research Europe (CITRE), Sevilla, Spain;Servicio de Hematología y Departamento de Medicina, Hospital Universitario de Salamanca (HUS), Salamanca 37007, Spain;Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca 37007, Spain;Department of Internal Medicine III, University Hospital of Ulm, Ulm 89081, Germany

关键词: Acute myeloid leukemia;    R algorithm;    Bioinformatics;    Differential expression;    Gene expression;    Transcripts;    Exons;    Human genomics;    Splicing index;    Alternative splicing;   

Others  :  1128479
DOI  :  10.1186/1471-2164-15-879

 received in 2014-05-05, accepted in 2014-09-22,  发布年份 2014

【 摘 要 】

Background

Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.

Results

Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.

Conclusions

The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.

【 授权许可】

   
2014 Risueño et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Röder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Bar NS, Batut P, Bell K, Bell I, Chakrabortty S, Chen X, Chrast J, Curado J, et al.: Landscape of transcription in human cells. Nature 2012, 489:101-108.
• [2]Graveley BR: Alternative splicing: increasing diversity in the proteomic world. Trends Genet 2001, 17:100-107.
• [3]Smith CW, Valcárcel J: Alternative pre-mRNA splicing: the logic of combinatorial control. Trends Biochem Sci 2000, 25:381-388.
• [4]Wahl MC, Will CL, Lührmann R: The spliceosome: design principles of a dynamic RNP machine. Cell 2009, 136:701-718.
• [5]Venables JP: Aberrant and alternative splicing in cancer. Cancer Res 2004, 64:7647-7654.
• [6]Grosso AR, Martins S, Carmo-Fonseca M: The emerging role of splicing factors in cancer. EMBO Rep 2008, 9:1087-1093.
• [7]Clark TA, Schweitzer AC, Chen TX, Staples MK, Lu G, Wang H, Williams A, Blume JE: Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol 2007, 8:R64.
• [8]Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB: Alternative isoform regulation in human tissue transcriptomes. Nature 2008, 456:470-476.
• [9]Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 2003, 31:e15.
• [10]Gautier L, Cope L, Bolstad BM, Irizarry RA: affy--analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 2004, 20:307-315.
• [11]Smyth GK: Limma: linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconductor. Edited by Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S, Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S. New York: Springer; 2005:397-420.
• [12]Kapur K, Jiang H, Xing Y, Wong WH: Cross-hybridization modeling on Affymetrix exon arrays. Bioinformatics 2008, 24:2887-2893.
• [13]McCall MN, Murakami PN, Lukk M, Huber W, Irizarry RA: Assessing affymetrix GeneChip microarray quality. BMC Bioinformatics 2011, 12:137.
• [14]Risueño A, Fontanillo C, Dinger ME, De Las Rivas J: GATExplorer: genomic and transcriptomic explorer; mapping expression probes to gene loci, transcripts, exons and ncRNAs. BMC Bioinformatics 2010, 11:221.
• [15]Purdom E, Simpson KM, Robinson MD, Conboy JG, Lapuk AV, Speed TP: FIRMA: a method for detection of alternative splicing from exon array data. Bioinformatics 2008, 24:1707-1714.
• [16]Rasche A, Herwig R: ARH: predicting splice variants from genome-wide data with modified entropy. Bioinformatics 2010, 26:84-90.
• [17]Gaidatzis D, Jacobeit K, Oakeley EJ, Stadler MB: Overestimation of alternative splicing caused by variable probe characteristics in exon arrays. Nucleic Acids Res 2009, 37:e107.
• [18]Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics 2005, 21:3940-3941.
• [19]Srinivasan K, Shiue L, Hayes JD, Centers R, Fitzwater S, Loewen R, Edmondson LR, Bryant J, Smith M, Rommelfanger C, Welch V, Clark TA, Sugnet CW, Howe KJ, Mandel-Gutfreund Y, Ares M Jr: Detection and measurement of alternative splicing using splicing-sensitive microarrays. Methods 2005, 37:345-359.
• [20]Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ: Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 2008, 40:1413-1415.
• [21]Gardina PJ, Clark TA, Shimada B, Staples MK, Yang Q, Veitch J, Schweitzer A, Awad T, Sugnet C, Dee S, Davies C, Williams A, Turpaz Y: Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array. BMC Genomics 2006, 7:325.
• [22]Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003, 4:249-264.
• [23]ᅟ: Affymetrix Whitepaper: Alternative Transcript Analysis Methods For Exon Arrays. ᅟ ᅟ. http://media.affymetrix.com/support/technical/whitepapers/exon_alt_transcript_analysis_whitepaper.pdf webcite
• [24]Chang TY, Li YY, Jen CH, Yang TP, Lin CH, Hsu MT, Wang HW: easyExon - a Java-based GUI tool for processing and visualization of Affymetrix exon array data. BMC Bioinformatics 2008, 9:432.
• [25]Gellert P, Uchida S, Braun T: Exon Array Analyzer: a web interface for Affymetrix exon array analysis. Bioinformatics 2009, 25:3323-3324.
• [26]Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M: AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res 2010, 38:W755-W762.
• [27]Consiglio A, Carella M, De Caro G, Delle Foglie G, Giovannelli C, Grillo G, Ianigro M, Licciulli F, Palumbo O, Piepoli A, Ranieri E, Liuni S: BEAT: Bioinformatics Exon Array Tool to store, analyze and visualize Affymetrix GeneChip Human Exon Array data from disease experiments. BMC Bioinformatics 2012, 13(Suppl 4):S21.
• [28]Xing Y, Stoilov P, Kapur K, Han A, Jiang H, Shen S, Black DL, Wong WH: MADS: a new and improved method for analysis of differential alternative splicing by exon-tiling microarrays. RNA 2008, 14:1470-1479.
• [29]Rossi M, Colecchia D, Iavarone C, Strambi A, Piccioni F, Verrotti Di Pianella A, Chiariello M: Extracellular signal-regulated kinase 8 (ERK8) controls estrogen-related receptor α (ERRα) cellular localization and inhibits its transcriptional activity. J Biol Chem 2011, 286:8507-8522.
• [30]Swiercz JM, Worzfeld T, Offermanns S: ErbB-2 and met reciprocally regulate cellular signaling via plexin-B1. J Biol Chem 2008, 283:1893-1901.
• [31]Hooper JE: A survey of software for genome-wide discovery of differential splicing in RNA-Seq data. Human Genomics 2014, 8:3.