【佳學(xué)基因檢測(cè)】RNA測(cè)序結(jié)果分析起點(diǎn)數(shù)據(jù)標(biāo)準(zhǔn)

RNA測(cè)序數(shù)據(jù)分析導(dǎo)讀：

出于推廣基因信息技術(shù)的目的，在這里，佳學(xué)基因所推出的數(shù)據(jù)分析和操作標(biāo)準(zhǔn)都可以采用共享程序、開(kāi)源程序可以完成的。RNA測(cè)序分析開(kāi)源程序大多數(shù)可以從Bioconductor軟件中找到，從而支持從端到端的基因水平的RNA測(cè)序數(shù)據(jù)中的基因差異差異表達(dá)分析。佳學(xué)基因從FASTQ文件開(kāi)始，展示這些文件是如何與人類(lèi)基因組的參考基因組對(duì)齊，并生成一個(gè)計(jì)數(shù)矩陣。從該矩陣統(tǒng)計(jì)每個(gè)樣本每個(gè)基因內(nèi)RNA測(cè)序數(shù)據(jù)中的測(cè)序數(shù)據(jù)、表達(dá)片段。佳學(xué)基因應(yīng)導(dǎo)大家進(jìn)行探索性數(shù)據(jù)分析（EDA），從而對(duì)數(shù)據(jù)質(zhì)量進(jìn)行質(zhì)量評(píng)估，并探索樣本之間的關(guān)系，執(zhí)行差異基因表達(dá)分析，并生成可用于高索引文章發(fā)表的圖表。

RNA測(cè)序數(shù)據(jù)開(kāi)源分析軟件介紹

佳學(xué)基因是國(guó)際開(kāi)源軟件聯(lián)盟成員。成員軟件庫(kù)Bioconductor有許多支持高通量序列數(shù)據(jù)分析的軟件包，包括RNA測(cè)序（RNA seq）。佳學(xué)基因在展示、示范過(guò)程中使用的軟件包包括由Bioconductor核心團(tuán)隊(duì)維護(hù)的核心軟件包，用于導(dǎo)入和處理原始測(cè)序數(shù)據(jù)以及對(duì)RNA測(cè)序數(shù)據(jù)進(jìn)行基因注釋。其中的部分軟件包可以進(jìn)行部分統(tǒng)計(jì)分析和序列數(shù)據(jù)圖表的生成。Bioconductor按計(jì)劃每6個(gè)月進(jìn)行一次更新，從而確保項(xiàng)目中的所有軟件包能夠協(xié)調(diào)一致地工作。此工作流中使用的軟件包帶有庫(kù)功能，可以按照Bioconductor軟件包安裝說(shuō)明進(jìn)行安裝。

RNA測(cè)序數(shù)據(jù)分析時(shí)的起點(diǎn)數(shù)據(jù)

該工作流程中使用的數(shù)據(jù)存儲(chǔ)來(lái)源于真實(shí)的實(shí)驗(yàn)數(shù)據(jù)。實(shí)驗(yàn)中的氣道平滑肌細(xì)胞使用地塞米松（一種具有抗炎作用的合成糖皮質(zhì)激素類(lèi)固醇）進(jìn)行處理。在現(xiàn)實(shí)生活中，哮喘患者使用糖皮質(zhì)激素來(lái)減輕氣道炎癥。在實(shí)驗(yàn)中，四個(gè)原代人氣道平滑肌細(xì)胞系用1微摩爾地塞米松處理18小時(shí)。對(duì)于四個(gè)細(xì)胞系中的每一個(gè)，有一個(gè)實(shí)驗(yàn)樣本和一個(gè)空白對(duì)照樣本。原代ASM細(xì)胞是從四名無(wú)慢性疾病的流產(chǎn)肺移植供體中分離出來(lái)的。第4至7代ASM細(xì)胞維持在添加10%FBS的Hams F12培養(yǎng)基中，用于所有實(shí)驗(yàn)。對(duì)于RNA Seq和qRT PCR驗(yàn)證實(shí)驗(yàn)，來(lái)自每個(gè)供體的細(xì)胞用1µM DEX或空白對(duì)照溶液處理18小時(shí)。
 

Preliminary processing of raw reads was performed using Casava 1.8 (Illumina, Inc., San Diego, CA). Subsequently, Taffeta scripts (https://github.com/blancahimes/taffeta) were used to analyze RNA-Seq data, which included use of FastQC [54] (v.0.10.0) to obtain overall QC metrics. Based on having sequence bias in the initial 12 bases on the 5′ end of reads, the first 12 bases of all reads were trimmed with the FASTX Toolkit (v.0.0.13) [55]. FastQC reports for each sample revealed that each was successfully sequenced. Trimmed reads for each sample were aligned to the reference hg19 genome and known ERCC transcripts using TopHat [56] (v.2.0.4), while constraining mapped reads to be within reference hg19 or ERCC transcripts. Additional QC parameters were obtained to assess whether reads were appropriately mapped. Bamtools [57] was used to the number of mapped reads, including junction spanning reads. The Picard Tools (http://picard.sourceforge.net) RnaSeqMetrics function was used to compute the number of bases assigned to various classes of RNA, according to the hg19 refFlat file available as a UCSC Genome Table. For each sample, Cufflinks [21] (v.2.0.2) was used to quantify ERCC Spike-In and hg19 transcripts based on reads that mapped to the provided hg19 and ERCC reference files. For three samples that contained ERCC Spike-Ins, we created dose response curves (i.e. plots of ERCC transcript FPKM vs. ERCC transcript molecules) following the manufacturer's protocol [58]. Ideally, the slope and R2 would equal 1.0. For our samples (Dex.2, Control.4, Dex.4), the slope (R2) values were 0.90 (0.90), 0.92 (0.84), 0.82 (0.86), respectively. Raw read plots were created by displaying bigwig files for each sample in the UCSC Genome Browser.

Differential expression of genes and transcripts in samples treated with DEX vs. untreated samples was obtained using Cuffdiff [21] (v.2.0.2) with the quantified transcripts computed by Cufflinks (v.2.0.2), while applying bias correction. The CummeRbund [59] R package (v.0.1.3) was used to measure significance of differentially expressed genes and create plots of the results. As a positive control of gene expression, the FPKM values for four housekeeping genes (i.e., B2M, GABARAP, GAPDH, RPL19) were obtained. Each had high FPKM values that did not differ significantly by treatment status [Figure S11]. The NIH Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform gene functional annotation clustering using Homo Sapiens as background, and default options and annotation categories (Disease: OMIM_DISEASE; Functional Categories: COG_ONTOLOGY, SP_PIR_KEYWORDS, UP_SEQ_FEATURE; Gene_Ontology: GOTERM_BP_FAT, GOTERM_CC_FAT, GOTERM_MF_FAT; Pathway: BBID, BIOCARTA, KEGG_PATHWAY; Protein_Domains: INTERPRO, PIR_SUPERFAMILY, SMART) [28]. The RNA-Seq data is available at the Gene Expression Omnibus Web site (http://www.ncbi.nlm.nih.gov/geo/) under accession GSE52778.