Looking at CRISPR sequences in Metagenomic Datasets of Sea Stars affected by Sea Star Wasting Disease and Healthy Individuals
TO-DO
- Read up on viral reference genomes or cogs
- Read up on sea star wasting disease
- Read up on finding CRISPR sequences in metagenomic data
- Read up on sea star bacteria and biology
- Find and install software for finding CRISPR sequences in metagenomic datasets
- Trial run on one sick and one healthy dataset
- Build reference tree
- create flow chart of process
- make power point slide for pitch
- if initial test successful, apply for a grant. NSF?
- Make list of what sort of tests and visualizations will be down. eg circo plot, ipath, cytoscape etc
Background info:
- CRISPR is part of bacterial immune system response to viruses
- short repeating sequences with viral DNA from previous invasions incorporated between each repeat. used to recognize invading viral DNA
- Sea star wasting disease: sea stars die for unknown reason. correlated with a denso virus, cured with antibiotic
- sea star immune system tightly connected with microbiome. if microbiome is disrupted, sea star becomes sick
Hypothesis: A bacteriophage is disrupting the microbiome. Either creating a highly virulent strain or killing one species
Expected Outcomes: A history of what virus have infected the microbiome is recorded in the CRISPR seuqneces. If a virus is disrupting the microbiome, then a significant difference in crispr content should be present between healthy and sick metagenomic datasets. If not, then there should be no difference and the shift in the microbiome could be from the bacteria taking advantage of the dying sea star.
Python Libraries
- xml
- xlsxwriter