• Help users to populate the RNA-Seq raw data processing commands in bash with little code, i.e. it can make you life easier if you frequently dowload and align RNA-Seq data from GEO.

• Users only need to provide a collumn data, that means a table recording the design and names of the fastq files; The sample ids could directly be the RUN IDs from GEO.

devtools::install_github("ZhenWei10/RbashGEO")

• The functions of this package can help users to:

1. Download and decompress many raw fastq data from GEO easily.
2. QC, trim, and align the fastq files with popular RNA-Seq command line tools, but with well organized output in R.
3. Count the alignment results with a user provided annotation by GRanges in R, the count is conducted by SummarizeOverlap function in GenomicAlignment package, which is transplanted from the HTSeq-count in python.
4. The most significant utility of this package is to reduce the repetitive bash coding and in the mean time generate compact results for downstream analysis.

• First, we need to prepare a collumn data, such as the example below.

library(RbashGEO)
knitr::kable( Coldata_example[1:6,1:6] )

Only 2 collumns: SRR_RUN and Lib are neccessary to complete the work flow of this package, but addition collumns are valuable for their own good.

The design of the collumn table is summarized from this page on GEO.

• Then, we should download them from GEO and run fastqc for quality control:

library(RbashGEO)
mapply(function(x,y) Rnohup(WgetQC(x,y),x), Coldata_example$SRR_RUN,Coldata_example$Lib == "Paired")

• Next, we should align them with hisat2 (you should first check the fastqc reports, if they are not OK, you should use Rtrim_galore before alignment.):

library(RbashGEO)
library(dplyr)

mapply(
 function(x,y){
   Rhisat2(Fastq_file_name = x,
           Paired = y,
           parallel_num = 1,
           Fastq_directory = getwd()) %>% Rnohup(.,paste0(x,"_hisat2"))}, 
 Coldata_example$SRR_RUN,
 (Coldata_example$Lib == "Paired")
)

• Check the organized alignment reports in R all together with this command.

hisat2_report <- RbashGEO::Check_hisat2_reports()
knitr::kable(hisat2_report[1:6,-1*c(2,3,4)])

You should see a data.frame with summarized outputs of hisat2.

• Then, convert the sam into bam with some desired filters on SAM flags.

library(RbashGEO)

sapply(
  Coldata_example$SRR_RUN,
  function(x) Rnohup(
  Rsamtools_view(x,sam_end = ".sam",parallel_num = 1,flag_filter = 2820),
  x)
)

• Finally, count the reads. The reads count are stored with SummarizedExperiment object in R.

At this step, we need to provide a GRanges object for annotation, in this case we use Annotation_gr as an example.

Count_SRRs(Coldata_example$SRR_RUN,"./",Annotation_gr,"Example_human_SE")

At last, the obtained SummarizedExperiment object could be conveniently handeled with other QC, inference, and learning work flows.

If you are interested in MeRIP data, you could use meripQC;

if you are interested in the analysis of the RNA modification data, you could look m6ALogisticModel for more information.

sessionInfo()

## R version 3.4.2 (2017-09-28)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] RbashGEO_1.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.14               XVector_0.18.0            
##  [3] knitr_1.18                 magrittr_1.5              
##  [5] zlibbioc_1.24.0            GenomicRanges_1.30.1      
##  [7] BiocGenerics_0.24.0        GenomicAlignments_1.14.1  
##  [9] IRanges_2.12.0             BiocParallel_1.12.0       
## [11] lattice_0.20-35            highr_0.6                 
## [13] stringr_1.2.0              GenomeInfoDb_1.14.0       
## [15] tools_3.4.2                grid_3.4.2                
## [17] SummarizedExperiment_1.8.1 parallel_3.4.2            
## [19] Biobase_2.38.0             matrixStats_0.52.2        
## [21] htmltools_0.3.6            yaml_2.1.16               
## [23] rprojroot_1.3-2            digest_0.6.13             
## [25] Matrix_1.2-12              GenomeInfoDbData_1.0.0    
## [27] S4Vectors_0.16.0           bitops_1.0-6              
## [29] RCurl_1.95-4.10            evaluate_0.10.1           
## [31] rmarkdown_1.8              DelayedArray_0.4.1        
## [33] stringi_1.1.6              compiler_3.4.2            
## [35] Rsamtools_1.30.0           Biostrings_2.46.0         
## [37] backports_1.1.2            stats4_3.4.2