我在写教程，别人在写文章

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很多人问针对RNA-seq数据如何找变异位点，尤其是肿瘤，比如TCGA计划的海量RNA-seq数据，所以我就写了教程：

  • 2017年6月:RNA-seq 检测变异之 GATK 最佳实践流程
  • 2019年11月:最新版针对RNA-seq数据的GATK找变异流程
    并且分享了代码，就是STAR aligner 2-pass的比对，衔接上 GATK的MuTect2流程找变异位点。
    最近无意中看到了2018年6月发表在PeerJ的BIOINFORMATICS AND GENOMICS的文章标题是；《Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data》的文章，我看了看跟我的教程基本上差不多额。
    同时拿TCGA数据库的GBM的转录组数据做了测试，生物信息学流程（各个步骤软件选择）如下：
  • The process first involved trimming the adapters with cutadapt (v1.10) (Martin, 2011) from fastq files,
  • removing sequences that were shorter than 36 bases after trimming,
  • and removing rRNA and tRNA sequences by aligning with BWA (v0.7.12-r1039) to a reference built with known rRNA/tRNA.
  • Filtered reads were then aligned with STAR aligner (v2.4.2a) using a 2-pass procedure (Dobin & Gingeras, 2015).
  • Before variant calling, aligned reads in BAM format were sorted, duplicate reads were flagged (MarkDuplicates, Picard v2.5.0),
  • the base scores recalibrated (BaseRecalibrator, GATK v3.6)
  • and RNA-seq reads were split into exons (SplitNCigarReads, GATK v3.6).
  • Variant calling was done with MuTect2 in tumor versus normal mode as described below.
  • Variants recovered in VCF files were then separated into RNA-seq-only, Intersection and WES-only.
  • ANNOVAR (v.2016Feb01) (Wang, Li & Hakonarson, 2010) was used to annotate variants relative to RefSeq annotations (release 73)
  • SIFT score/prediction (v2.3) (Ng & Henikoff, 2003), and FATHMM score/prediction with cancer weights (v2.3) were used to evaluate the functional impact of non-synonymous SNVs and frameshift indels.
    流程图如下：
    image-20200903223929191
    文章链接是：https://peerj.com/articles/5362/ 感兴趣也可以看看，其实如果你比较有空，而且英语比较不错，类似的文章写起来应该是不难的。
    不过，其实针对RNA-seq数据的变异，其实也有专门的软件啦，并不需要自己搭建流程，使用STAR aligner 2-pass的比对，衔接上 GATK的MuTect2流程找变异位点。只不过是用得少，不出名。
    补充阅读；
  • https://europepmc.org/article/MED/29091775
  • https://rna-seqblog.com/inconsistency-of-somatic-snvs-called-in-wes-and-rna-seq-data/

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