Precursors of Sea Star Wasting: Immune and Microbial Disruption During Initial Disease Outbreak in Southeast Alaska

This repository contains code and data processing scripts for the study:

If you use this code in your research, please cite:
Precursors of Sea Star Wasting: Immune and Microbial Disruption During Initial Disease Outbreak in Southeast Alaska (DOI: TBA).

The analysis compares wild-sampled individuals from outbreak-free sites (“Naive”) to those from sites where wasting disease is present but individuals appear visually healthy (“Exposed”). Scripts include:

• Data cleaning

• Statistical analyses

• Figure generation

Related Repositories and Data

• Dryad DOI: https://doi.org/10.5061/dryad.t1g1jwtfh

• Manuscript DOI: TBA

Folders and Code Overview

Cleaning_Mapping_Reads/

Cleaning and mapping paired-end reads and transcript quantification

• star_loop.sh – Bash script to run gene alignment and transcript quantification on paired FASTA files to a reference.

• star_counts_matrix.R – R script to combine outputs from STAR gene mapping into a single counts matrix.

DEG/

Differential gene expression analysis and plotting

• Annotations/ – Annotation of mapped transcripts:

  • diamond.sh – Bash script for running DIAMOND blastX.

  • subset_fasta.py – Python script to subset FASTA files for differentially expressed genes.

• DESeq2_pycnoRNA_STAR_NE.R – Differential expression analysis of transcript counts.

• heatmaps_by_catagory/ – Heatmaps of differential gene expression.

16S_Counts/

Microbial classification and quantification

• Qiime2_pipeline_Silva.txt – QIIME2 pipeline for microbial classification.

• pycno_samples_NE.txt – Sample metadata.

• level-7.csv – Counts matrix of classified microbial taxa per sample.

• NE_ANCOM-BC-Lev7.R – Analysis of differentially abundant microbes.

• deseqPCA_microbes.R – PCA analysis of microbial composition.

• RelAbundancePlot.R – Microbial abundance visualizations.

GO/

Gene ontology enrichment analysis

• TOP_GO.R – Gene ontology analysis of differentially expressed genes.

• topgo_wgcna_mods.R – Gene ontology analysis of genes clustered in WGCNA modules “red” and “purple”.

Gene_Microbe_WGCNA/

Weighted gene-microbe correlation network analysis and plotting

• WGCNA.R – WGCNA analysis of gene expression and microbial abundance counts.

• WGCNA_expression_abund_plots_nf.R – Abundance and expression plots of module membership.

• new.netolot.R – Network visualization of WGCNA modules.