RNA-Seq数据的内含子保留分析

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前面我们的明码标价之普通转录组上游分析，受到了各大热心粉丝的吐槽，觉得太简单了我们居然还好意思收费。后面我就就加上了稍微有一点难度的《可变剪切》，不过仍然是阻挡不了粉丝无穷无尽的需求，后台有人发给我一个RNA-Seq数据的内含子保留分析需求。

我看了看粉丝发过来的文章，发表于 January 2021, 在CELL杂志的文章《Spliceosome-targeted therapies trigger an antiviral immune response in triple-negative breast cancer》，链接是：https://doi.org/10.1016/j.cell.2020.12.031

这个文章数据比较多：

SUM159 SD6 RNA-Seq #GSE163414
LM2 SD6 RNA-Seq #GSE163411
SUM159 Cytoplasmic RNA-Seq #GSE163232
SUM159 J2 dsRIPseq #GSE163188
Syngeneic model RNA-Seq #GSE163181

可以看到，主要是RNA-Seq数据啦，有两个是普通的细胞系处理前后的表达量差异情况探索，所以出图如下：

表达量差异情况探索标准图表

这个已经是超级简单了， 我们的明码标价之转录组常规测序服务（仅需799每个样品） 和 明码标价之普通转录组上游分析 就是对这样的 RNA-Seq拿到了表达量矩阵，然后下游分析也是平淡无奇，仅收费800，代码呢，我也多次分享了，基本上看我六年前的表达芯片的公共数据库挖掘系列推文即可；

这样的分析流程基本上绝大部分粉丝已经是无需委托我们啦，所以粉丝发给我的是 RNA-Seq数据的内含子保留分析需求，步骤如下：

  • Hisat2-aligned reads were filtered for proper-paired reads (-f 2 flag in SAMtools).
  • Intron annotations were parsed from UCSC RefSeq gene annotation files and were filtered to exclude features that overlap genomic loci on the same strand.
  • Reads mapping to introns were counted using Pysam.
  • For each intron feature, we defined the following two read classes:
    • (1) ‘‘intronic’’ reads mapping at least 6 bases contiguously within the intron
    • (2) ‘‘spanning’’ reads with ends mapping to the flanking exons.
  • The intron retention (IR) score was then computed as the ratio of the RPKM-normalized ‘‘intronic’’ read density over the RPKM-normalized ‘‘spanning’’ read den- sity.
  • In order to compare commonly expressed IR events across samples, introns with < 10 spanning RPKM in any sample were excluded from all analyses.

对我们有ngs组学数据分析经验的人来说，其实并不难，无非就是安装几个软件，使用几个包。但对于没有学过编程的纯生物学研究者来说基本上不可能完成，也没有这样的网页工具。

但是呢，这个流程又确实是过于个性化，哪怕对我们来说很简单，也其实是耗费时间和精力需要研发调试的。

首先你需要有RNA-seq的fastq文件

如果是TCGA数据库，步骤如下：

  • Intron retention analysis was performed on BRCA TCGA RNA sequencing datasets (Koboldt et al., 2012).
  • TCGA fastq reads were mapped using the STAR aligner (v2.3.1) (Dobin et al., 2013) onto the hg19/GRCh37 reference genome as previously described (Hsu et al., 2015).
  • Level of intron retention (IR level) within each sample was calculated as the number of introns with IR scores > 0.01, as defined previously.
  • ‘‘High’’ and ‘‘Low’’ IR were defined as having an IR level outside one standard deviation of the mean.
  • RSEM normalized gene expression data from TCGA was obtained from the Broad GDAC Firehose.

一般来说，大家是很难下载TCGA数据库原始fastq文件，这个权限审核比较严厉，不过咱们数据挖掘呢完全没有毕业一直盯着TCGA数据库啊，自己领域的普通RNA-seq肯定也是不少。如果是认真搞科研，你一定会自行调研和阅读文献，找到合适的数据集。

数据挖掘的核心就是通过分类把基因数量搞少

部分粉丝看到这里，可能无法理解RNA-Seq数据的内含子保留分析的意义是什么？

其实就是多了一个维度的指标，来把你的样本分类，分类后就可以找差异。同样的我们可以看这个示例文章，感觉每个样品的IR指标，把病人分成IR高低两个组别，然后走普通的ssGSEA分析，生存分析。

image-20210320093952534

这一套组合拳，大家是不是很眼熟啊？

如果你也想做自己的的RNA-Seq数据的内含子保留分析，赶快联系我们吧，同样的，我们的分析仍然是明码标价，单个RNA-Seq数据的内含子保留分析收费仅需800元，因为是纯粹的基于Linux平台的各种软件脚本，所以提供你全套数据和脚本但是无法保证你能运行成功，因为你不一定有自己的服务器。

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