Last updated: 2022-11-01

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Knit directory: locust-phase-transition-RNAseq/

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Work environment

Environment

Grace Cluster at Texas A&M University, College Station, Texas, USA
Local Mac: macOS Monterey Version 12.6
R version 4.2.1

Directories

Path	Purpose
`/scratch/user/XXXX/locust-phase`	Working repository
`/scratch/user/XXXX/locust-phase/workflow`	Location of the Snakemake pipeline
`/scratch/user/XXXX/refgenomes/locust-complete`	Reference genome, annotation and index
`/scratch/user/XXXX/locust-phase/data/reads`	Raw FASTQ.GZ files
`/scratch/user/XXXX/locust-phase/data/kaiju`	Kaiju database and output
`/scratch/user/XXXX/locust-phase/data/trimming`	Trimmed FASTQ files
`/scratch/user/XXXX/locust-phase/data/alignment/STAR`	Output from STAR
`/scratch/user/XXXX/locust-phase/data/alignment/STAR_SALMON`	Output from STAR-Salmon
`/scratch/user/XXXX/locust-phase/data/alignment/STAR_RSEM`	Output from STAR-Salmon
`/scratch/user/XXXX/locust-phase/data/analysis`	R analysis (DGE)

Snakemake pipeline

We use a Snakemake pipeline for each species. Therefore, it is essential to verify that each software is installed 1) locally or via a conda environment or 2) as a module or cloned via Github on the Grace cluster at Texas A&M University is essential.

We built our Snakemake pipeline by launching small individual jobs to tailor each cluster parameter for the best memory and time efficiency.

To ease the indexing of our file and folder, we generate some shared parameters which will be helpful in the future: 1) reference genome directory path REFdir, 2) output directory path OUTdir and 3) a list LOCUSTS containing sample base name referred as locust.

### SET DIRECTORY PATHS FOR REFERENCE AND OUTPUT DATA

REFdir = "/scratch/user/maeva-techer/refgenomes"
OUTdir = "/scratch/user/maeva-techer/locust-rna/data"

### SAMPLES LIST AND OTHER PARAMETERS

LOCUSTS, = glob_wildcards(OUTdir + "/reads/{locust}_1.fastq.gz")
print(LOCUSTS)

Softwares and database

Links to each software to add in the future

Use a conda environment

add what to install on the Macpro tower e.g., we install a conda environment called rna-seq

Use modules on Grace cluster

On Grace, each module may requires some dependencies, which is why we need to ensure they will be loaded together. For this we use the function module spider [targeted software] w/o the version.

Today we will need the following software:

to launch the Snakemake pipeline

module load GCC/11.2.0 OpenMPI/4.1.1 snakemake/6.10.0 Biopython/1.79

to perform adapter trimming and QC

module load Trimmomatic/0.39-Java-11
module load FastQC/0.11.9-Java-11

to perform short read mapping

module load GCC/11.2.0 STAR/2.7.9a

R packages neccessary

List of R packages used

CRAN
data.table: https://cran.r-project.org/package=data.table
dplyr: https://cran.r-project.org/package=dplyr
reshape2: https://cran.r-project.org/package=reshape2
ggplot2: https://cran.r-project.org/package=ggplot2
ggrepel: https://cran.r-project.org/package=ggrepel

Bioconductor
DESeq2: https://bioconductor.org/packages/DESeq2/
apeglm: https://bioconductor.org/packages/apeglm/
EnhancedVolcano: https://bioconductor.org/packages/EnhancedVolcano/
edgeR: https://www.bioconductor.org/packages/edgeR/

sessionInfo()

R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9       compiler_4.2.1   pillar_1.8.1     bslib_0.4.0     
 [5] later_1.3.0      git2r_0.30.1     jquerylib_0.1.4  tools_4.2.1     
 [9] getPass_0.2-2    digest_0.6.30    jsonlite_1.8.3   evaluate_0.17   
[13] tibble_3.1.8     lifecycle_1.0.3  pkgconfig_2.0.3  rlang_1.0.6     
[17] cli_3.4.1        rstudioapi_0.14  yaml_2.3.6       xfun_0.34       
[21] fastmap_1.1.0    httr_1.4.4       stringr_1.4.1    knitr_1.40      
[25] fs_1.5.2         vctrs_0.5.0      sass_0.4.2       rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.7.0   fansi_1.0.3     
[33] rmarkdown_2.17   callr_3.7.2      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.1         promises_1.2.0.1 htmltools_0.5.3  httpuv_1.6.6    
[41] utf8_1.2.2       stringi_1.7.8    cachem_1.0.6

Preparations needed for launching the analysis

Maeva Techer

2022-11-01