5/10/2023
To install the private package, please use R version 4.3 (or higher), then install as follows:
# install.packages("devtools")
library(devtools)
# Bioconductor needs to be activated when automatically installing dependencies.
options(repos = BiocManager::repositories())
devtools::install_github("trichelab/bamSliceR")
library(bamSliceR)
If you will be analyzing any data that is controlled access, you will need to download a GDC token and store it in a file in your home directory. For more instructions see GenomicDataCommons Token and Data portal authentication tokens
echo "my_downloaded_auth_code" > ~/.gdc_tokenTo query the available BAM files on GDC, three pieces of information are needed: 1) The project ID. 2) Experiment Strategy (ex. “RAN-Seq”, “WGS”, etc. ) 3) Alignment workflow.
These three pieces of information can be inspected by availableProjectId(), availableExpStrategy() and availableWorkFlow() correspondingly to locate the BAM files on GDC portal. Here, we showed the example using TARGET-AML cohort with 2,281 subjects including 3,225 RNA-seq BAM files. #### Check ids for all the projects on GDC portal
library(bamSliceR)
availableProjectId()## [1] "TCGA-BRCA" "CPTAC-3"
## [3] "TCGA-STAD" "TCGA-LUAD"
## [5] "EXCEPTIONAL_RESPONDERS-ER" "CGCI-HTMCP-LC"
## [7] "CPTAC-2" "CMI-MBC"
## [9] "TARGET-ALL-P3" "TARGET-ALL-P2"
## [11] "OHSU-CNL" "REBC-THYR"
## [13] "TARGET-ALL-P1" "MMRF-COMMPASS"
## [15] "TARGET-CCSK" "ORGANOID-PANCREATIC"
## [17] "NCICCR-DLBCL" "TARGET-NBL"
## [19] "TCGA-CHOL" "TARGET-OS"
## [21] "TARGET-AML" "TARGET-RT"
## [23] "TARGET-WT" "TCGA-SARC"
## [25] "TCGA-PCPG" "TCGA-COAD"
## [27] "TCGA-ACC" "WCDT-MCRPC"
## [29] "TCGA-UCEC" "MP2PRT-ALL"
## [31] "TCGA-MESO" "TCGA-KIRP"
## [33] "TCGA-KIRC" "TCGA-GBM"
## [35] "CGCI-HTMCP-CC" "CMI-ASC"
## [37] "CGCI-HTMCP-DLBCL" "BEATAML1.0-CRENOLANIB"
## [39] "CDDP_EAGLE-1" "APOLLO-LUAD"
## [41] "CMI-MPC" "FM-AD"
## [43] "MATCH-Z1D" "MATCH-Y"
## [45] "MATCH-N" "MATCH-Q"
## [47] "MP2PRT-WT" "TCGA-DLBC"
## [49] "TCGA-LAML" "TCGA-KICH"
## [51] "TCGA-THYM" "VAREPOP-APOLLO"
## [53] "TCGA-UCS" "TCGA-SKCM"
## [55] "TRIO-CRU" "TCGA-HNSC"
## [57] "TCGA-PAAD" "TCGA-TGCT"
## [59] "TCGA-CESC" "TCGA-ESCA"
## [61] "TCGA-THCA" "TCGA-LGG"
## [63] "TCGA-LIHC" "TCGA-PRAD"
## [65] "TCGA-READ" "MATCH-I"
## [67] "MATCH-W" "MATCH-B"
## [69] "MATCH-H" "TCGA-OV"
## [71] "TCGA-UVM" "MATCH-Z1A"
## [73] "MATCH-U" "BEATAML1.0-COHORT"
## [75] "TCGA-BLCA" "TCGA-LUSC"
## [77] "CGCI-BLGSP" "HCMI-CMDC"
## [79] "CTSP-DLBCL1"
availableExpStrategy("TARGET-AML")## [1] "Genotyping Array" "Methylation Array" "RNA-Seq"
## [4] "WGS" "WXS" "miRNA-Seq"
availableWorkFlow(projectId = "TARGET-AML", es = "RNA-Seq")## doc_count key
## 1 6450 Arriba
## 2 6450 STAR - Counts
## 3 6450 STAR-Fusion
## 4 3225 STAR 2-Pass Chimeric
## 5 3225 STAR 2-Pass Genome
## 6 3225 STAR 2-Pass Transcriptome
After knowing the keyword of project, experimental strategy and workflow, we can collect information of BAMs we are interested in.
file_meta = getGDCBAMs(projectId = "TARGET-AML", es = "RNA-Seq", workflow = "STAR 2-Pass Genome")
head(file_meta)## id sample
## 1 d06f1bce-79a1-42b0-8a80-c1ef0c43b8f5 TARGET-20-PAWWWM-03A-01R
## 2 e33ae4d5-3320-4627-95fc-993685ee1f60 TARGET-20-PAYIET-09A-01R
## 3 a2a40017-1ff6-4180-aac1-202d5c42fdf8 TARGET-00-RO02327-14A-01R
## 4 97feca17-6b0c-43c3-af9d-1f1a290029f5 TARGET-20-PAVLJH-09A-01R
## 5 2dc9b0ea-5896-4799-a07f-6034c9bd1c09 TARGET-20-PAVLJH-04A-01R
## 6 5f4b77a1-5e74-4224-9e32-751a1f83de7b TARGET-20-PAVPLM-09A-01R
## file_name
## 1 89a164db-c6c0-4614-9090-dd8d86734b66.rna_seq.genomic.gdc_realn.bam
## 2 d01cb0b7-5ebb-4873-aa9b-ebfb01bd2563.rna_seq.genomic.gdc_realn.bam
## 3 7a623fca-9c97-4582-b7af-ed821bf3a52b.rna_seq.genomic.gdc_realn.bam
## 4 d24423b0-0027-489b-8182-e16d6ca63683.rna_seq.genomic.gdc_realn.bam
## 5 6bf0ba4d-e99b-4fb7-b808-f9a48f4fe671.rna_seq.genomic.gdc_realn.bam
## 6 3e61af27-749c-4ca5-b9b6-9e68aee8a037.rna_seq.genomic.gdc_realn.bam
## case_id sample_type
## 1 TARGET-20-PAWWWM Primary Blood Derived Cancer - Peripheral Blood
## 2 TARGET-20-PAYIET Primary Blood Derived Cancer - Bone Marrow
## 3 TARGET-00-RO02327 Bone Marrow Normal
## 4 TARGET-20-PAVLJH Primary Blood Derived Cancer - Bone Marrow
## 5 TARGET-20-PAVLJH Recurrent Blood Derived Cancer - Bone Marrow
## 6 TARGET-20-PAVPLM Primary Blood Derived Cancer - Bone Marrow
## experimental_strategy workflow
## 1 RNA-Seq STAR 2-Pass Genome
## 2 RNA-Seq STAR 2-Pass Genome
## 3 RNA-Seq STAR 2-Pass Genome
## 4 RNA-Seq STAR 2-Pass Genome
## 5 RNA-Seq STAR 2-Pass Genome
## 6 RNA-Seq STAR 2-Pass Genome
## downloaded_file_name
## 1 TARGET-20-PAWWWM-03A-01R_TARGET-20-PAWWWM_89a164db-c6c0-4614-9090-dd8d86734b66.rna_seq.genomic.gdc_realn.bam
## 2 TARGET-20-PAYIET-09A-01R_TARGET-20-PAYIET_d01cb0b7-5ebb-4873-aa9b-ebfb01bd2563.rna_seq.genomic.gdc_realn.bam
## 3 TARGET-00-RO02327-14A-01R_TARGET-00-RO02327_7a623fca-9c97-4582-b7af-ed821bf3a52b.rna_seq.genomic.gdc_realn.bam
## 4 TARGET-20-PAVLJH-09A-01R_TARGET-20-PAVLJH_d24423b0-0027-489b-8182-e16d6ca63683.rna_seq.genomic.gdc_realn.bam
## 5 TARGET-20-PAVLJH-04A-01R_TARGET-20-PAVLJH_6bf0ba4d-e99b-4fb7-b808-f9a48f4fe671.rna_seq.genomic.gdc_realn.bam
## 6 TARGET-20-PAVPLM-09A-01R_TARGET-20-PAVPLM_3e61af27-749c-4ca5-b9b6-9e68aee8a037.rna_seq.genomic.gdc_realn.bam
## UPC_ID
## 1 PAWWWM
## 2 PAYIET
## 3 RO02327
## 4 PAVLJH
## 5 PAVLJH
## 6 PAVPLM
BAM slicing API from GDC portal accept genomic ranges specifying as vector of character() e.g., c(“chr”, “chr1:10000”). Here we provide a function to get the required input format given the gene names.
target_genes_data = system.file("data", "gene_names.rds", package = "bamSliceR")
target_genes = readRDS(target_genes_data)
target_genes## IDH1 IDH2 TET1 TET2 ASXL1 ASXL2 DNMT3A RUNX1
## "IDH1" "IDH2" "TET1" "TET2" "ASXL1" "ASXL2" "DNMT3A" "RUNX1"
## HIST1H3A HIST1H3B HIST1H3C HIST1H3D HIST1H3E HIST1H3F HIST1H3G HIST1H3H
## "H3C1" "H3C2" "H3C3" "H3C4" "H3C6" "H3C7" "H3C8" "H3C10"
## HIST1H3I HIST1H3J H3F3A
## "H3C11" "H3C12" "H3-3A"
Get either GRanges or vector of character() for exons of the genes.
#Get GRanges for exons of all genes above
target_ranges_gr = getGenesCoordinates(target_genes, ret = "GRanges")
head(target_ranges_gr)## GRanges object with 6 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ASXL1 20 32358280-32439369 +
## ASXL2 2 25733703-25878537 -
## DNMT3A 2 25227805-25342640 -
## H3-3A 1 226061801-226072069 +
## H3C1 6 26020401-26021008 +
## H3C10 6 27810001-27811350 +
## -------
## seqinfo: 8 sequences from an unspecified genome; no seqlengths
#Get the vector of character() instead.
target_ranges_chars = getGenesCoordinates(target_genes, ret ="DF")
head(target_ranges_chars)## [1] "chr20:32358280-32439369" "chr2:25733703-25878537"
## [3] "chr2:25227805-25342640" "chr1:226061801-226072069"
## [5] "chr6:26020401-26021008" "chr6:27810001-27811350"
#Download 3225 RNA-Seq sliced BAMs files with reads within regions defined by "target_ranges_chars" from TARGET-AML.
downloadSlicedBAMs(file_df = file_meta, regions = target_ranges_chars, dir = "BAM_FILES")We first also need to specify the regions as a GRanges object.
head(target_ranges_gr)## GRanges object with 6 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ASXL1 20 32358280-32439369 +
## ASXL2 2 25733703-25878537 -
## DNMT3A 2 25227805-25342640 -
## H3-3A 1 226061801-226072069 +
## H3C1 6 26020401-26021008 +
## H3C10 6 27810001-27811350 +
## -------
## seqinfo: 8 sequences from an unspecified genome; no seqlengths
Then we need make the character() vector including all the names of downloaded BAM files.
cd DIR_BAM_FILES
ls | grep bam$ > bamfilesIn the directory of the downloaded BAM files. And then scan the ‘bamfiles’ in R.
bamfiles = scan("bamfiles", "character")Last thing we need is to specify the directory of gmapGenome object created before. (see here about how to create gmapGenome object.)
gmapGenome_dir = "/path/to/your/gmapGenome"We can then start to tally the reads of BAMs files:
tallied_reads = tallyReads(bamfiles = bamfiles, gmapGenome_dir = gmapGenome_dir, grs = target_ranges,
BPPARAM = MulticoreParam(workers = 4 , stop.on.error = TRUE), parallelOnRanges = TRUE,
parallelOnRangesBPPARAM = MulticoreParam(workers = 4) )For more information about how to efficiently run tallyReads() on HPC on large cohorts, please see the instruction in here.
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