11、轉(zhuǎn)錄組數(shù)據(jù)分析zsci rna服務(wù)

1、轉(zhuǎn)錄組服務(wù)轉(zhuǎn)錄組是指在某一特定發(fā)育時(shí)期或者某一生理?xiàng)l件下，細(xì)胞內(nèi)所有轉(zhuǎn)錄產(chǎn)物的集合，包括mRNA、non-coding RNA和small RNA。轉(zhuǎn)錄組為生命科學(xué)研究提供了全新的角度，可用于結(jié)構(gòu)、可變剪切和其他后轉(zhuǎn)錄修飾、并可定量測(cè)定每個(gè)轉(zhuǎn)錄本在生長過程中和不同的條件下的表達(dá)水平的變化。RNA-seq技術(shù)，也被稱為“轉(zhuǎn)錄組”，是指利用高通量技術(shù)對(duì)cDNA進(jìn)序從而得到一個(gè)樣品中所有RNA的信息。利用RNA-seq技術(shù)，為轉(zhuǎn)錄組提供全方位、一站式解決方案。標(biāo)準(zhǔn)化生物信息分析圖表示例：1.數(shù)據(jù)質(zhì)量錯(cuò)誤率分布檢查（如圖1）圖1錯(cuò)誤率分布圖橫坐標(biāo)為reads的堿基位置，縱坐標(biāo)為單堿基錯(cuò)誤率。（Jian

2、g L. Schlesinger F. et al. 2011）均一性分布檢查（如圖2，圖3）7006005004003002001000High Medium LowDistance from 5 end of cDNA (%)圖2 轉(zhuǎn)錄本的reads密度分布圖橫坐標(biāo)為轉(zhuǎn)錄本的相對(duì)位置（以百分比表示），縱坐標(biāo)為reads頻率。圖3 不同表達(dá)水平的轉(zhuǎn)錄本的reads密度分布圖橫坐標(biāo)為距離轉(zhuǎn)錄本5端的相對(duì)位置（以百分比表示），縱坐標(biāo)為覆蓋深度的中值。(Li P. Ponnala L. et al. 2010)- 2 -Median depth of coverage重復(fù)相關(guān)性檢查（如圖4，圖5，

3、圖6，圖7）Technical replicatesR2 = 0.961041031001010.00.1110100 1,000 10,000Bra echnical 1 (RPKM)圖4RNA-seq技術(shù)重復(fù)相關(guān)性分析圖5RNA-Seq 生物學(xué)重復(fù)相關(guān)性分析對(duì)取自同一時(shí)間點(diǎn)、同一均進(jìn)行RNA-Seq 2個(gè)樣本rep1和rep2進(jìn)行生物學(xué)重復(fù)相關(guān)性分析。( Lott SE. Villalta JE. et al. 2010)橫、縱坐標(biāo)分別代表技術(shù)重復(fù)1、2。RPKM(reads per kilo bases per million reads)是每百萬reads中來自于某的每千堿基長度的re

4、ads數(shù)，代表的表達(dá)量。（Mortazavi A. Williams B. A. et al. 2008）圖6橫、縱坐標(biāo)分別為點(diǎn)部分為RNA-seq與RNA-Seq技術(shù)比較和RNA-seq的表達(dá)差異倍數(shù)。紅圖7 RT-PCR與RNA-seq比較橫坐標(biāo)為實(shí)時(shí)定量PCR(qRT-PCR)測(cè)定的為RNA-seq技術(shù)的表達(dá)量紅色表示新表達(dá)量，縱坐標(biāo)的轉(zhuǎn)錄本，的表達(dá)差異， 黑點(diǎn)部分，綠色部分為。為RNA-seq未檢測(cè)到差異表達(dá)藍(lán)色表示已被注釋。（Pearson）相關(guān)系數(shù)為0.929。RNA-seq中低表達(dá)量的(Marioni J. C. Mason C.E. Mane S.M. et al. 2011)

5、飽和曲線檢查（如圖8，圖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)圖8 飽和曲線檢查分布圖圖9 剪切位點(diǎn)（junction）、轉(zhuǎn)錄本及檢測(cè)在橫坐標(biāo)為定位到組上的reads數(shù)量，縱坐標(biāo)為區(qū)的不同深度下的數(shù)量覆蓋程度，頂端橫坐標(biāo)為可變剪切的reads數(shù)。（Mortazavi A. Williams

6、B. A. et al. 2008）橫坐標(biāo)為定位的reads數(shù)量（以百萬為），縱坐標(biāo)為檢測(cè)到相應(yīng)單元的百分比（剪切位點(diǎn)、轉(zhuǎn)錄本、）。(Toung J.M. Morley M. Li M.Y. et al. 2011)- 3 -Becrahnical 2 (RPKM)Fraction of genes wi hin5% of final value0.822.054.108.200.050.120.250.4816.400.9624.601.4432.801.9141.002.402.參考組比對(duì)以及表達(dá)水平分析組對(duì)比統(tǒng)計(jì)（如圖10，表1）RNA-seq reads參考ergenic 2.5%ro

7、n 1.5%Repeat 6.5%SJ 5.5%Exon 84.0%圖10 RNA得到的reads比對(duì)到參考組不同區(qū)域上的分布情況（He G. Zhu X. Axel A. Elling et al. 2010） (Li P. Ponnala L. et al. 2010)表1 RNA-seq檢測(cè)到的、可變剪接和新轉(zhuǎn)錄位點(diǎn)一覽表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（如圖11，圖12，圖13）圖11 水稻1號(hào)（He G.所包含的轉(zhuǎn)座子和編碼Zhu X. Axel A. Elling et al. 2010）的分布圖12 Reads上分布，紅色為表達(dá)密度分布、藍(lán)色為的非表達(dá)表達(dá)在。(Toung J.M. Morley M. Li M.Y. et al. 2011)

11、圖13 轉(zhuǎn)錄組在組上的分布將比對(duì)上的reads以50kb的窗口在上用不同顏色進(jìn)行標(biāo)示，進(jìn)而評(píng)估Reads密度分布。(Lu T.T. Lu G.j. Fan D.L. et al. 2011)- 5 -表達(dá)水平統(tǒng)計(jì)（如圖14 ，圖15）圖14 不同區(qū)域表達(dá)水平的盒形圖圖15 樣品間高表達(dá)量的RNA-Seq得到的reads比對(duì)到間隔區(qū)、內(nèi)含子、為樣本E18和P7有的高豐度表達(dá)數(shù)目為974個(gè)，E18中特異外顯子和新轉(zhuǎn)錄本區(qū)所對(duì)應(yīng)的表達(dá)數(shù)值變異范圍，縱坐標(biāo)表示為RPKM標(biāo)準(zhǔn)化的表達(dá)量數(shù)值。表達(dá)的高豐度有396個(gè)，P7中特異表達(dá)的高豐度有399個(gè)。（Wang B. Guo G. W. et al. 20

12、10；Han X. W. Wu X. et al. 2009）3.無參考組轉(zhuǎn)錄組的拼接分析（如圖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圖16 無參考組轉(zhuǎn)錄組的拼接分析（Lyer M. K. Chinnaiyan A. M. 2011）- 6 -4.可變式剪切形式分析（如圖17）圖17 已知和未知

14、的可變剪切事件檢測(cè)左側(cè)圖為在樣本中檢測(cè)到的7種不同類型的可變剪切形式，右側(cè)的表格為相對(duì)應(yīng)的可變剪切事件的相關(guān)統(tǒng)計(jì)數(shù)據(jù)。（Ramani A. K. Calarco J. A. et al.）5.樣品間差異表達(dá)分析MA散點(diǎn)圖（如圖18）圖18 利用Fisher模型進(jìn)行差異表達(dá)(Lu T.T. Lu G.j. Fan D.L. et al. 2011)- 7 -多樣品（如圖19）1 cm4121,112+4 cm 204515869BS1,00619284TipBaseTip81514,0222101,601745,04818,99244132M1,256227317196158圖19 玉米B73的

15、葉片轉(zhuǎn)錄組分析不同樣品間的相互比較，找出差異（僅針對(duì)多個(gè)樣品）注 Base、-1cm、4cm、Tip為玉米葉片取樣時(shí)的位置信息。(Li P. Ponnala L. et al. 2010)6.樣品間可變式剪切形式特異性表達(dá)分析（如圖20）圖20 樣品間可變式剪切形式的幾種模式(Wang E. T. Sandberg R. et al. 2009)- 8 -7.差異聚類分析（如圖21，圖22）圖22 對(duì)不同時(shí)間點(diǎn)的VACV中mRNA表達(dá)的聚類分析。A圖 代表缺少藥物處理的0-4小時(shí)內(nèi)比對(duì)到VACV 中ORF位置上的相關(guān)轉(zhuǎn)錄本的差異表達(dá)熱點(diǎn)圖，顏色由藍(lán)色到黃色、再到紅色代表在該時(shí)間點(diǎn)內(nèi)檢測(cè)到的OR

16、F的表達(dá)量不斷增圖21 線蟲四個(gè)發(fā)育階段中發(fā)生顯著差異表達(dá)的轉(zhuǎn)錄本的熱點(diǎn)圖柱形代表4個(gè)發(fā)育時(shí)期，分別標(biāo)作L2、L3、L4及YA，縱坐標(biāo)的每一行代表不同的轉(zhuǎn)錄本。黃色代表與對(duì)照樣品相比，處理樣品的上調(diào)表達(dá)，藍(lán)色代表對(duì)照相樣品相比，處理樣加，C1.1和C1.2聚類中包含早期表達(dá)的，而C2聚類中包品的下調(diào)表達(dá)。含在DNA后表達(dá)的。（Hilr LD.W. Reinke V. et al. 2009）B圖為與A圖相關(guān)的不同時(shí)間點(diǎn)檢測(cè)到的發(fā)生差異表達(dá)的轉(zhuǎn)錄本線性分布圖。（Z.L. Bruno DP. et al. 2010）8.差異GeneOntology 功能顯著性分析（如圖23，圖24，表2）圖23

17、水稻中差異的Gene Ontology 分類?？v坐標(biāo)代表富集的程度，橫坐標(biāo)代表不同的（He G. Zhu X. Axel A. Elling et al. 2010）功能。- 9 -圖24Gene Ontology分級(jí)結(jié)構(gòu)功能富集圖，其中黃域代表具有統(tǒng)計(jì)上顯著富集的功能。表2DEX應(yīng)答相關(guān)的GO功能富集(Reddy TE. Pauli F.et al. 2009)- 10 -9.差異代謝通路富集分析（如圖25，圖26）圖25 黑色素瘤和正常黑素細(xì)胞MAPK信號(hào)通路的差異表達(dá)。紅色和綠色分別代表上調(diào)和下調(diào)的，調(diào)控程度以顏色深淺表示。（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圖26 玉米葉片轉(zhuǎn)錄組的變化過程左圖所示 K1-K6代表6個(gè)不同樣品，以及不同樣品和組織間差異數(shù)目右圖所示 6個(gè)不同樣品之間的差異的功能分類及富集情況注 Base、-1cm、4cm、Tip為玉米葉片取樣時(shí)的位置信息(Li P. Ponnala L. et al. 2010)- 11 -10.參考組中新和已知注釋表3 水稻的結(jié)構(gòu)優(yōu)化（如表3）組中新(He

20、 G. Zhu X. Axel A et al. 2010)11.融合鑒定（如表4，圖27）表4 融合的鑒別及驗(yàn)證(Henrik E. Astrid M. Sara K et al. 2011)圖27 融合示意圖(Pflueger D. Terry S. et al. 2011)- 12 -12.SNP和編碼蛋白突變類型統(tǒng)計(jì)（如表5）表5 錯(cuò)義突變的驗(yàn)證注 通過分型的方法驗(yàn)證27個(gè)新的錯(cuò)義突變。這27個(gè)錯(cuò)義突變?cè)谀[瘤細(xì)胞中表達(dá)，但在生殖細(xì)胞中沉默(Michael F. B. Joshua Z. L. Krishna V et al. 2011)- 13 -參考文獻(xiàn):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