Last updated: 2025-02-27

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Knit directory: locust-comparative-genomics/

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Work environment

Environment

  • Grace Cluster at Texas A&M University, College Station, Texas, USA
  • Local Mac: macOS Sonoma Version 14.1
  • R version 4.3.1

Directories

Path Purpose
/scratch/group/songlab/maeva/refgenomes/locust-complete Reference genome, annotation and index
/scratch/group/songlab/maeva/headthor-locusts-rna Working repository
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/workflow Location of the Snakemake pipeline
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/reads Raw FASTQ.GZ files
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/list Metadata and list files
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/kaiju Kaiju database and output
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/trimming Trimmed FASTQ files
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/alignment/STAR Output from STAR
/scratch/group/songlab/maeva/headthor-locusts-rna/{species}/data/analysis/DESeq2 R analysis (DGE)

Our Snakemake pipeline

We use a Snakemake pipeline for each species. Therefore, it is essential to verify that each required software is installed and accessible. This can be done through various methods: 1) locally (e.g., cloned from GitHub or installed with admin access), 2) as a HPRC cluster module on the Grace cluster at Texas A&M University or 3) via a conda environment.

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

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

### SET DIRECTORY PATHS FOR REFERENCE AND OUTPUT DATA

REFdir = "/scratch/group/songlab/maeva/refgenomes"
OUTdir = "/scratch/group/songlab/maeva/headthor-locusts-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.

Quick notes for necessary modules loading:

  • to launch a quick interactive bash session
srun --ntasks 1 --cpus-per-task 8 --mem 30G --time 01:00:00 --pty bash
  • 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/12.2.0 STAR/2.7.10b

Public workflow used

mbarkdull: Comparative Genomics Analysis

We are extremely grateful for scientists that made their hard-worked pipelines and codes available to streamline to processing of annotated genomes to selection analysis. We used and modified the amazingly curated pipeline mbarkdull/FormicidaeMolecularEvolution: v1.0.0-beta made by Megan Barkadull, Cornell for the paper on ant worker reproduction and caste polymorphism.
Usage: Longest isoform, Orthofinder, HyPhy, InterProScan annotation

R packages neccessary

How to launch R in HPRC:

ml GCC/12.2.0  OpenMPI/4.1.4 R_tamu/4.3.1
export R_LIBS=$SCRATCH/R_LIBS_USER/

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 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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

time zone: Asia/Tokyo
tzcode source: internal

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

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
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[13] git2r_0.35.0      htmltools_0.5.8.1 httpuv_1.6.15     ps_1.9.0         
[17] sass_0.4.9        rmarkdown_2.29    jquerylib_0.1.4   tibble_3.2.1     
[21] evaluate_1.0.3    fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4  
[25] whisker_0.4.1     stringr_1.5.1     compiler_4.4.2    fs_1.6.5         
[29] pkgconfig_2.0.3   Rcpp_1.0.14       rstudioapi_0.17.1 later_1.4.1      
[33] digest_0.6.37     R6_2.6.1          pillar_1.10.1     callr_3.7.6      
[37] magrittr_2.0.3    bslib_0.9.0       tools_4.4.2       cachem_1.1.0     
[41] getPass_0.2-4