3大数据库超2万RNA-seq数据重新统一处理

各种大型计划产出的RNA-seq数据资源已经非常丰富了，但是大家都想把多个数据库联合起来分析，就不得不面对批次效应这个问题，所以UCSC团队就使用统一的流程把这些数据重新处理了，在亚马逊云上，一个样本花费1.3美元。

发表在：Nature Biotechnology publication: https://doi.org/10.1038/nbt.3772

3大数据库是：

1. The Cancer Genome Atlas (TCGA)
2. Genotype-Tissue Expression (GTEx)
3. Therapeutically Applicable Research To Generate Effective Treatments (TARGET)

而且还提供网页工具供查询使用：

Differential gene and isoform expression of FOXM1 transcription factor in TCGA vs. GTEx

使用的数据处理流程

如下图： CutAdapt was used for adapter trimming, STAR was used for alignment, and RSEM and Kallisto were used as quantifiers.

img

流程介绍

如果你对RNA-seq数据处理流程有意外，直接去看我长达74个小时全套生物信息学入门视频：生信技能树视频课程学习路径，这么好的视频还免费！

参考基因组选择

• STAR, RSEM, and Kallisto indexes were all built with the same reference genome. HG38 (no alt analysis) with overlapping genes from the PAR locus removed (chrY:10,000-2,781,479 and chrY:56,887,902-57,217,415).
  • ftp://ftp.ncbi.nlm.nih.gov/genomes/archive/old_genbank/Eukaryotes/vertebrates_mammals/Homo_sapiens/GRCh38/seqs_for_alignment_pipelines

注释文件的选择

• RSEM: Gencode V23 comprehensive annotation (CHR)
  • http://www.gencodegenes.org/releases/23.html first row
• Kallisto: Gencode V23 comprehensive annotation (ALL)
  • http://www.gencodegenes.org/releases/23.html second row

软件参数的选择

• STAR
  • sudo docker run -v $(pwd):/data quay.io/ucsc_cgl/star --runThreadN 32 --runMode genomeGenerate --genomeDir /data/genomeDir --genomeFastaFiles hg38.fa --sjdbGTFfile gencode.v23.annotation.gtf
• Kallisto
  • sudo docker run -v $(pwd):/data quay.io/ucsc_cgl/kallisto index -i hg38.gencodeV23.transcripts.idx transcriptome_hg38_gencodev23.fasta
  • Kallisto index that was used during the recompute is available here.
• RSEM
  • sudo docker run -v $(pwd):/data --entrypoint=rsem-prepare-reference jvivian/rsem -p 4 --gtf gencode.v23.annotation.gtf hg38.fa hg38

可以看到，上面的3大要素， 就是我五年前在 生信菜鸟团博客写教程的基本规律。

Raw data

Nature Publication Supplementary Note 7 – Data Availability

Submitter sample ID to Xena sample ID mapping

TCGA mapping

GTEx mapping

TARGET mapping

最后公布的可供下载的数据集

• GTEX (11 datasets)
• TARGET Pan-Cancer (PANCAN) (12 datasets)
• TCGA and TARGET Pan-Cancer (PANCAN) (4 datasets)
• TCGA Pan-Cancer (PANCAN) (10 datasets)
• TCGA TARGET GTEx (13 datasets)

其中TCGA TARGET GTEx 3大数据库) (共有 13 datasets)

cohort: TCGA TARGET GTEx

表达矩阵样本量很可观

• RSEM expected_count

  (n=19,109)

  UCSC Toil RNAseq Recompute

• RSEM expected_count (DESeq2 standardized)

  (n=19,039)

  UCSC Toil RNAseq Recompute

  RSEM expected_count output normalized using DESeq2

• RSEM fpkm

  (n=19,131)

  UCSC Toil RNAseq Recompute

• RSEM norm_count

  (n=19,120)

  UCSC Toil RNAseq Recompute

  TCGA TARGET GTEx gene expression by UCSC TOIL RNA-seq recompute

• RSEM tpm

  (n=19,131)

  UCSC Toil RNAseq Recompute

phenotype

• TCGA GTEX main categories

  (n=17,221)

  UCSC Toil RNAseq Recompute

• TCGA survival data

  (n=10,496)

  UCSC Toil RNAseq Recompute

• TCGA TARGET GTEX selected phenotypes

  (n=19,131)

  UCSC Toil RNAseq Recompute

somatic mutation (SNP and INDEL)

• TCGA somatic mutations (Pan-cancer Atlas MC3 public version)

  (n=8,463)

  UCSC Toil RNAseq Recompute

transcript expression RNAseq

• RSEM expected_count

  (n=19,109)

  UCSC Toil RNAseq Recompute

  TCGA TARGET GTEx transcript expression by RSEM using UCSC TOIL RNA-seq recompute

• RSEM fpkm

  (n=19,129)

  UCSC Toil RNAseq Recompute

  TCGA TARGET GTEx transcript expression by RSEM using UCSC TOIL RNA-seq recompute

• RSEM isoform percentage

  (n=19,131)

  UCSC Toil RNAseq Recompute

  TCGA TARGET GTEx transcript expression by RSEM using UCSC TOIL RNA-seq recompute

• RSEM tpm

  (n=19,131)

  UCSC Toil RNAseq Recompute

  TCGA TARGET GTEx transcript expression by RSEM using UCSC TOIL RNA-seq recompute