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[mRNA-seq] 转录组入门笔记(2)

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发表于 2017-7-30 19:31:15 | 显示全部楼层 |阅读模式


一、读文献
  • 以发表在nature communication上的文章为例:"AKAP95 regulates splicing through scaffolding RNAs and RNA processing factors"。

本文主要发现AKAP95蛋白积极调控前体mRNA的选择性剪切。mRNA的选择性剪切非常重要,5'和3'的剪切位点都有剪切体来识别,剪切信号由顺式调控元件调节,而这些调控元件又由许多反式作用因子调节,包括SR及相关蛋白,核不均一核糖核蛋白(hnRNPs)及其他组织特异性RNA结合蛋白。hnRNPs是人体普遍表达的一类RNA结合蛋白,通过特异的结构与pre-mRNA结合,形成pre-mRNPs复合体,参与mRNA转运、代谢、剪切及表达等过程的调节;hnRNPs家族种类繁多,已经发现有20多种蛋白,依次从A命名到U。AKAP95是AKAPs家族唯一位于核内的成员,此家族主要调节细胞信号。AKAP95已经发现的功能包括参与染色质压缩,有丝分裂等。(作者为什么要研究这个蛋白?怎么把它与前体mRNA的选择性剪切联系起来的?)

作者在2013年发表了一篇文章:’Regulation of transcription by the MLL2 complex and MLL complex-associated AKAP95. Nat. Struct. Mol. Biol. 20, 1156–1163 (2013)‘,发现AKAP95可以调节组蛋白甲基化和基因表达。参考AKAP95的功能,其调控基因表达的机制是不清楚的,本文作者就对此进行深入研究。

  • AKAP95 binds to RNA processing factors at its N-terminus. 之前作者的研究发现AKAP95的N端对促进基因表达是必要的,因此做了pull-down实验,发现带N尾巴的片段拉下来的大部分是hnRNP。免疫印迹实验发现AKAP95 与 Pol II 相关。
  • AKAP95 directly regulates splicing of a minigene pre-mRNA. 构建体外报告系统,敲高或敲低AKAP95基因表达直接影响剪切效率。
  • AKAP95 directly regulates alternative splicing of FAM126A. AKAP95 knockdown 影响 FAM126A外显子的生成,免疫组化发现FH-AKAP95 bound to three major sites in the region around the AKAP95-regulated exon 11 of FAM126A。最后These results thus establish a direct and joint regulation of FAM126A exon 11 inclusion by the interacting AKAP95 and hnRNP F proteins.
  • AKAP95 binds to intronic regions of cellular pre-mRNAs. 作者做了anti-AKAP95 RIP assays,然后测序,看看结合位点在哪。(这里就是我们要的数据了)
二、测序及分析

作者将RNA提取出来,反转录成cDNA并构建文库,上机测序:paired end 2* 50bp sequencing runs to align the cDNA sequences to the reference genome。获得的数据:35–55 million of paired 51bp reads for each sample。

生物学分析步骤。用TopHat (v2.0.13)比对到GRCh37/hg19,低质量序列去掉(MQ>30),用Picard-tools计算插入平均片段大小,用HTSeq (v0.6.0)生成read count tables,用DESeq (v3.0)进行deferential expression (DE) analysis,DEXSeq (v3.1)进行Deferential exon 分析。

作者对control and KD都做了两个生物学重复。control samples(sample GSM1095127 in GSE44976),miR#8 and miR#12 knockdown samples。The read per million normalized BigWig files were generated using BEDTools (v2.17.0) and bedGraphToBigWig tool, Gene ontology analysis was performed at DAVID (http://david.ncifcrf.gov/)

由于没做过,不知道名词什么意思,就不翻译了。对于 RIP-Seq, mapped files were passed to Model-based Analysis of ChIP-Seq (MACS) (v1.4.2 20120305) for peak calling and HOMER (vv3.12, 6-8-2012) (http://homer.salk.edu/homer/)57 was used for Motif finding. Bedgraph files generated by MACS were then normalized based on read per million and converted to BigWig files using bedGraphToBigWig. The input RNA-seq files were not subject to MACS. The genomic regions for annotation (intergenic regions, exons, UTRs, distal and proximal introns) were extracted from the GTF file used above. Peak annotations and coverage calculations for these genomic regions were performed using Bedtools and SAMtools (v0.1.18)58, respectively. Profile plots were generated by ngs.plot (v2.47)

The RIP-seq an RNA-seq data have been deposited in the Gene Expression Omnibus database, with accession code GSE81916

三、下载数据

批量下载数据:python方法(参考http://www.biotrainee.com/forum. ... p;tid=1829#lastpost

#!/bin/python3import refrom urllib.request import urlopen #urllib.request是python3自带的库(python3.x版本特有),我们用它来请求网页,并获取网页源码。import osdef main(geo):    # find the FTP address from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GEO    response = urlopen("https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={}".format(geo))    pattern = re.compile("<a href=\"(.*?)\">\(ftp\)</a>")    # use wget from shell to download SRA data    ftp_address = re.search(pattern,response.read().decode('utf-8')).group(1)    os.system(' wget -nd -r 1 -A *.sra ' + ftp_address)if __name__ == '__main__':    from sys import argv    main(argv[1])

由于不会编程,本人直接从ftp用wget命令下载,速度还可以(ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByExp/sra/SRX/SRX180/SRX1801306/SRR3589962/SRR3589962.sra)





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