11、转录组数据分析zsci rna服务

1、转录组服务转录组是指在某一特定发育时期或者某一生理条件下，细胞内所有转录产物的集合，包括mRNA、non-coding RNA和small RNA。转录组为生命科学研究提供了全新的角度，可用于结构、可变剪切和其他后转录修饰、并可定量测定每个转录本在生长过程中和不同的条件下的表达水平的变化。RNA-seq技术，也被称为“转录组”，是指利用高通量技术对cDNA进序从而得到一个样品中所有RNA的信息。利用RNA-seq技术，为转录组提供全方位、一站式解决方案。标准化生物信息分析图表示例：1.数据质量错误率分布检查（如图1）图1错误率分布图横坐标为reads的碱基位置，纵坐标为单碱基错误率。（Jian

2、g L. Schlesinger F. et al. 2011）均一性分布检查（如图2，图3）7006005004003002001000High Medium LowDistance from 5 end of cDNA (%)图2 转录本的reads密度分布图横坐标为转录本的相对位置（以百分比表示），纵坐标为reads频率。图3 不同表达水平的转录本的reads密度分布图横坐标为距离转录本5端的相对位置（以百分比表示），纵坐标为覆盖深度的中值。(Li P. Ponnala L. et al. 2010)- 2 -Median depth of coverage重复相关性检查（如图4，图5，

3、图6，图7）Technical replicatesR2 = 0.961041031001010.00.1110100 1,000 10,000Bra echnical 1 (RPKM)图4RNA-seq技术重复相关性分析图5RNA-Seq 生物学重复相关性分析对取自同一时间点、同一均进行RNA-Seq 2个样本rep1和rep2进行生物学重复相关性分析。( Lott SE. Villalta JE. et al. 2010)横、纵坐标分别代表技术重复1、2。RPKM(reads per kilo bases per million reads)是每百万reads中来自于某的每千碱基长度的re

4、ads数，代表的表达量。（Mortazavi A. Williams B. A. et al. 2008）图6横、纵坐标分别为点部分为RNA-seq与RNA-Seq技术比较和RNA-seq的表达差异倍数。红图7 RT-PCR与RNA-seq比较横坐标为实时定量PCR(qRT-PCR)测定的为RNA-seq技术的表达量红色表示新表达量，纵坐标的转录本，的表达差异， 黑点部分，绿色部分为。为RNA-seq未检测到差异表达蓝色表示已被注释。（Pearson）相关系数为0.929。RNA-seq中低表达量的(Marioni J. C. Mason C.E. Mane S.M. et al. 2011)

5、饱和曲线检查（如图8，图9）Spli(millions)1.00.80.60.4Robustness officationas a function of read number3,000 + RPKM n 243002,999 RPKM n 18730299 RPKM n 1,591329 RPKMn 6,3690.20Mapped reads (millions)图8 饱和曲线检查分布图图9 剪切位点（junction）、转录本及检测在横坐标为定位到组上的reads数量，纵坐标为区的不同深度下的数量覆盖程度，顶端横坐标为可变剪切的reads数。（Mortazavi A. Williams

6、B. A. et al. 2008）横坐标为定位的reads数量（以百万为），纵坐标为检测到相应单元的百分比（剪切位点、转录本、）。(Toung J.M. Morley M. Li M.Y. et al. 2011)- 3 -Becrahnical 2 (RPKM)Fraction of genes wi hin5% of final value0.822.054.108.200.050.120.250.4816.400.9624.601.4432.801.9141.002.402.参考组比对以及表达水平分析组对比统计（如图10，表1）RNA-seq reads参考ergenic 2.5%ro

7、n 1.5%Repeat 6.5%SJ 5.5%Exon 84.0%图10 RNA得到的reads比对到参考组不同区域上的分布情况（He G. Zhu X. Axel A. Elling et al. 2010） (Li P. Ponnala L. et al. 2010)表1 RNA-seq检测到的、可变剪接和新转录位点一览表MapsummaryHEK 293B cellsTotal readsLow-quality readsReads with multiple matches8,638,919234,1601,546,3614,640,1123,712,4767,682,230194,

8、9991,324,7703,895,6432,902,387Reads wit Reads mapique matchesto annoed RNAs(ENSEMBL + Eldorado)ENSEMBL genes wi ENSEMBL genes wit least five reads t least one read12,56714,96338,598144510,66813,73944,7811409Reads inronic clustersENSEMBL genes with clustersrons with read clusters Reads with no match

9、to tronic read18622,218,286307,90478,880(81,302)10,29218472,266,818229,45362,596(66,981)8655omeReads aligned to splice junctions Identified junctions(expected)Genesjunctions Genesjunctions Geneseast five reads) witheast one read) with10,5588910east one read) with20781732previously unknown junctions

10、Previously unknown junctions Previously unknown junctions23972031965182identified by lessn one read（Sultan M. Schulz M. H. et al. 2008）- 4 -Reads密度分布（如图11，图12，图13）图11 水稻1号（He G.所包含的转座子和编码Zhu X. Axel A. Elling et al. 2010）的分布图12 Reads上分布，红色为表达密度分布、蓝色为的非表达表达在。(Toung J.M. Morley M. Li M.Y. et al. 2011)

11、图13 转录组在组上的分布将比对上的reads以50kb的窗口在上用不同颜色进行标示，进而评估Reads密度分布。(Lu T.T. Lu G.j. Fan D.L. et al. 2011)- 5 -表达水平统计（如图14 ，图15）图14 不同区域表达水平的盒形图图15 样品间高表达量的RNA-Seq得到的reads比对到间隔区、内含子、为样本E18和P7有的高丰度表达数目为974个，E18中特异外显子和新转录本区所对应的表达数值变异范围，纵坐标表示为RPKM标准化的表达量数值。表达的高丰度有396个，P7中特异表达的高丰度有399个。（Wang B. Guo G. W. et al. 20

12、10；Han X. W. Wu X. et al. 2009）3.无参考组转录组的拼接分析（如图16）Piece RNA-Seq reads o contigs (Inchworm)Cluster contigs o components (Chrysalis)Assign reads to components (Chrysalis)Split overlaptranscripts based on coverageand re airingsEnumerate transcript isoforms using reads (Butterfly)AAUAAAAAUAAAAAUAAAAAUA

13、AAAAUAAAIncongruencies with reference genomeInsights fromde novo transcriptome-specific assemblyAlternative promotersAlternative splicingAberration from erchromosomal rearrangementResult of conventionalde novo assemb ly图16 无参考组转录组的拼接分析（Lyer M. K. Chinnaiyan A. M. 2011）- 6 -4.可变式剪切形式分析（如图17）图17 已知和未知

14、的可变剪切事件检测左侧图为在样本中检测到的7种不同类型的可变剪切形式，右侧的表格为相对应的可变剪切事件的相关统计数据。（Ramani A. K. Calarco J. A. et al.）5.样品间差异表达分析MA散点图（如图18）图18 利用Fisher模型进行差异表达(Lu T.T. Lu G.j. Fan D.L. et al. 2011)- 7 -多样品（如图19）1 cm4121,112+4 cm 204515869BS1,00619284TipBaseTip81514,0222101,601745,04818,99244132M1,256227317196158图19 玉米B73的

15、叶片转录组分析不同样品间的相互比较，找出差异（仅针对多个样品）注 Base、-1cm、4cm、Tip为玉米叶片取样时的位置信息。(Li P. Ponnala L. et al. 2010)6.样品间可变式剪切形式特异性表达分析（如图20）图20 样品间可变式剪切形式的几种模式(Wang E. T. Sandberg R. et al. 2009)- 8 -7.差异聚类分析（如图21，图22）图22 对不同时间点的VACV中mRNA表达的聚类分析。A图 代表缺少药物处理的0-4小时内比对到VACV 中ORF位置上的相关转录本的差异表达热点图，颜色由蓝色到黄色、再到红色代表在该时间点内检测到的OR

16、F的表达量不断增图21 线虫四个发育阶段中发生显著差异表达的转录本的热点图柱形代表4个发育时期，分别标作L2、L3、L4及YA，纵坐标的每一行代表不同的转录本。黄色代表与对照样品相比，处理样品的上调表达，蓝色代表对照相样品相比，处理样加，C1.1和C1.2聚类中包含早期表达的，而C2聚类中包品的下调表达。含在DNA后表达的。（Hilr LD.W. Reinke V. et al. 2009）B图为与A图相关的不同时间点检测到的发生差异表达的转录本线性分布图。（Z.L. Bruno DP. et al. 2010）8.差异GeneOntology 功能显著性分析（如图23，图24，表2）图23

17、水稻中差异的Gene Ontology 分类。纵坐标代表富集的程度，横坐标代表不同的（He G. Zhu X. Axel A. Elling et al. 2010）功能。- 9 -图24Gene Ontology分级结构功能富集图，其中黄域代表具有统计上显著富集的功能。表2DEX应答相关的GO功能富集(Reddy TE. Pauli F.et al. 2009)- 10 -9.差异代谢通路富集分析（如图25，图26）图25 黑色素瘤和正常黑素细胞MAPK信号通路的差异表达。红色和绿色分别代表上调和下调的，调控程度以颜色深浅表示。（An J. Wan H. et al. 2011）Base 1

18、 cm+4 cm Tip02 (log10 P)K13,707K1 K2 K3 K4 K5 K6Cell wall Cell cycleCellanizationK25,257Vesicle transport DNA/chromatinHormone metabolismLimetabolismK31,393Major CHO metabolism Minor CHO metabolism Respiration Protein synthesis Protein degradationK41,070ProteodificationChloroplastingProtein assembly

19、 Photosynthesis Redox regulation S-assimilationK5662Secondary metabolism Signalling Tetrapyrrole synthesis TransportK61,396图26 玉米叶片转录组的变化过程左图所示 K1-K6代表6个不同样品，以及不同样品和组织间差异数目右图所示 6个不同样品之间的差异的功能分类及富集情况注 Base、-1cm、4cm、Tip为玉米叶片取样时的位置信息(Li P. Ponnala L. et al. 2010)- 11 -10.参考组中新和已知注释表3 水稻的结构优化（如表3）组中新(He

20、 G. Zhu X. Axel A et al. 2010)11.融合鉴定（如表4，图27）表4 融合的鉴别及验证(Henrik E. Astrid M. Sara K et al. 2011)图27 融合示意图(Pflueger D. Terry S. et al. 2011)- 12 -12.SNP和编码蛋白突变类型统计（如表5）表5 错义突变的验证注 通过分型的方法验证27个新的错义突变。这27个错义突变在肿瘤细胞中表达，但在生殖细胞中沉默(Michael F. B. Joshua Z. L. Krishna V et al. 2011)- 13 -参考文献:1Peter J. T.,

21、Christopher, Q., Jeremiah J. F et al.anismal, genetic, and transcriptional variationhedeeply sequenced gut microbiomes of identical twins. PNAS, 2010, ( Me ranscriptome )2Alejandro R., Matthew H., Nicole H et al.eshe faecal microbiota of monozygotic twins and theirmothers. Nature, 2010, ( Me ranscri

22、ptome )3Stephen B. M., Micha S., Maria G-A et al. Transcriptome genetics using second generation sequencing in aCaucasian population. Nature, 2010, ( eQTL )4expres5Joseph K. P., John C. M., Athma A. Pai et al. Understanding mechanisms underlying human gene variation with RNA sequencing. Nature, 2010

23、, ( eQTL )David R. M., Qun, P., Patrick T. R et al. Smg1 is required for embryogenesis and regulates diverse genesvia alternative splicing coupled to nonsense-mediated mRNA decay. PNAS, 2010, ( Mutant )6Magdalini. P., Clotilde. L-T., Kasey R. H et al. Long pre-mRNA depletion and RNA missplicing cont

24、ributeto neuronal vulnerability from loss of TDP-43. Nature Neuroscience, 2011, ( Mutant )7Daniel R., Eric T. W., Christopher B. B., Rickard S. An Abundance of Ubiquitously Expressed GenesRevealed by Tie Transcriptome Sequence Data. PLoS Compuional Biology, 2009, ( ExpresLevel Estimate )8Jason G. U.

25、, Andrew V. U., Sol K et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nature Method, 2010, ( RNA Structure )9Mitschke J., GeJ., et al. An experimentally anchored map of transcriptional start siteshe mcyanobacteriumSynechocystis sp. PCC6803. PNAS, 2011, ( Mi

26、cro10 Engelmann I., Griffon A., et al. A Comprehensiveanism )ysis of Gene ExpresChanges Provoked byBacterial and Fungal Infection in C. elegans. PloS One, 2011, ( Host Infection )11 DiGuistini S., Wang,Y., et al. Genome and transcriptomeyses of the mountain pine beetle-fungalsymbiontGrosmanniaclavig

27、era, a lodgepolne pathogen. PNAS, 2011, ( Host Infection )12 Shah SP., Morin RD., et al. Muional evolution in a lobular breast tumour proled at single nucleotideresolution. Nature, 2009, ( RNA Editing )13 Li, M., Wang, I., et al. Widespread RNA and DNA Sequence DifferenSCIENCE, 2011, ( RNA Editing )

28、he Human Transcriptome.14 Chepelev I, Wei, G., et al. Detection of single nucleotide variations in expressed exons of the humangenome using RNA-Seq. Nucleic Acids Research, 2009, ( SNP )15 Tangiguchi, Y., Choi, PJ., et al.fying E. coli Proteome and Transcriptome with Single-MoleculeSensitivity in Si

29、ngle Cells. SCIENCE, 2011, ( Single Cell )16 Hittinger CT., Johnston M., et al. Leveraging skewed transcript abundance by RNA-Seq to increase the genomic depth of the tree of life. PNAS, 2010, ( Evolution )17 Wang E. T., Sandberg R., et al. Alternative isoform regulation in human ti2009, ( Alternati

30、ve Splicing )e transcriptomes. Nature,18 Ramani A. K., Calarco J. A., et al. Genome-wideysis of alternative splicing in Caenorhabditis elegans.Genome Research, 2011, ( Alternative Splicing )19 Zhang, K., Li J. B., et al. Digital RNA allelotyreveals ti)e-specific and allele-specific geneexpresin huma

31、n. Nature Method, 2009, ( Allele Specic Expres20 Young, F., Babak T., et al. Global survey of escfrom X inactivation by RNA-sequencingouse.Genome Research, 2010, ( Allele Specic Expres)Li, P., Ponnala L., et al. The developmental dynamics of the maize leaf transcriptome. Nature Genetics, 2010, ( Dev

32、elopment )Zenoni S., Ferrarini A., et al. Characterization of Transcriptional Complexity during Berry Development in Vitis vinifera Using RNA-Seq. Plant Physiology, 2010, ( Development )- 14 -23 Maher C. A., Kumar-Sinha C., et al. Transcriptome sequencing to detect gene fus in cancer. Nature,2009, (

33、 Chimeric RNA )24 Pflueger D., Terry S., et al. Discovery of non-ETS gene fugeneration RNA sequencing. Genome Research, 2011, ( Chimeric RNA )s in human prose cancer using next-25 He, G., Zhu, X., Axel A et al. Elling et al. GlobalSubspecies and their Reciprocal Hybrids. The Plant cell, 2010genetic

34、and Transcriptional Trends amono Rice26 Henrik E., Astrid M., Sara K et al. Identification of fusequencing. Genome Biology, 2011genes in breast cancer by paired-end RNA-Michael F. B., Joshua Z. L., Krishna V et al.Research, 2011Mortazavi A., Williams B. A., et al., MapNature Methods, 2008egrativeysi

35、s of the melanoma transcriptome. Genomeandfying mammalian transcriptomes by RNA-Seq.293031Jiang, L., Schlesinger F., et al., Synthetice-in standards for RNA-seq experiments. Genome Research, 2011Lyer M. K., Chinnaiyan A. M., RNA-Seq unleashed. Nature Biotechnology, 2011Sultan M., Schulz M. H., et al

36、., A Global View of Gene Activity and Alternative Splicing by DeepSequencing of the Human Transcriptome. Science, 200832 An, J., Wan, H., et al., A Comparative Transcriptomic Melanocyte. Plos One, 2011ysis of Uveal Melanoma and Normal UvealLott SE., Villalta JE., et al. Noncanonical comepensation of zygotic transcription in early drosophila melanogaster devel