GCContent
├── GCCalc
│ ├── BoxAndWhiskersPlot.R
│ ├── Counter.py
│ ├── GC3.ipynb
│ ├── GCContentPlotOrthologusChromosome.R
│ ├── GCCounter.py
│ ├── GCCounter2.py
│ ├── GGPlot2ViolinPlot.R
│ ├── PlotGCRepeats.ipynb
│ ├── README.md
│ ├── SlidingWindowAnalysis.ipynb
│ ├── plot.R
│ └── plot2.R
└── RepeatGC
├── LineRegress.R
├── PlotGCContentForRepeats.ipynb
├── calculate_gc_repeats.py
├── check_for_soft_repeatmask.sh
├── download_and_extract_genomes.sh
└── species_list.txt
GCCalc/ contains scripts to analyze the base composition of genomes by analyzing gc content, ag content, gc skew, and at skew in windows, chromosomes and other features of the genome. Some scripts were based on WenchaoLin's GCcalc. The python files require pandas, and biopython.
Counter.py can be used to count the length of chromosomes in a chromosome level assembly.
Example: python Counter.py -f genome.fna -i NumberOfChromosomes
GC3 is indended to read the GC3 value, but has not been finished.
Run GCCounter with a .fna genome file as input. It will output filename.fna.csv, which contains the base composition metrics mentioned above for each read in the file, which can be plotted by modifying plot.R.
Example: python GCCounter -f genome.fna
Run GCCounter2 with a .fna genome file and a .gtf feature file as input. It will output the base composition metrics mentioned above for each feature in the .gtf file, as filename.gtf.csv, which can be plotted by modifying plot2.R.
Example: python GCCounter2 -g genome.fna -a annotations.gtf
BoxAndWhiskersPlot.R, GCContentPlotOrthologusChromosome.R, and GGPlot2ViolinPlot.R are examples of other ways to plot the results from GCCounter and GCCounter2, and their results can be seen in presentation.pdf.
SlidingWindowAnalysis.ipynb also requires numpy and matplotlib. It can be used to create sliding window analyses of reads in a .fna file, and plot in additional location markers using other files that have the coordinates and feature in a dataframe.
RepeatGC/ contains scripts created to compare gc content in repeats and non repeats across species from softmasked genomes from ncbi, or from EarlGrey, the conda library for RepeatMasker.
Command Order:
Manually edit species_list with vscode or another text editor.
download_and_extract_genomes.sh
check_for_soft_repeatmask.sh
python calculate_gc_repeats.py
Run PlotGCContentForRepeats.ipynb and/or LineRegress.R with vscode or an IDE
species_list is a text file containing the species names and gc contents. I copied and pasted the species I wanted from ncbi by selecting only those two features in the genome search bar.
Make sure to make the download_and_extract_genomes.sh and check_for_soft_repeatmask.sh scripts executable by running chmod +x download_and_extract_genomes.sh and chmod +x check_for_soft_repeatmask.sh.
download_and_extract_genomes.sh downloads and extracts the genomes from ncbi using species_list as input. If downloading issues occur, manually add the genome, or run the script again with a shortened species_list.
check_for_soft_repeatmask.sh outputs whether or not each .fna file in the directory contains softmasked elements. If the genome does not, either run a repeat masker, or take the genome out of your analysis.
caclulate_gc_repeats.py requires biopython, os, and csv libraries. It outputs gc_content_results.csv which contains the fields filename, gc in masked, unmasked, and species.
PlotGCContentForRepeats.ipynb and LineRegress.R are ways to visualize gc_content_results.csv, and my graphs can be seen in presentation.pdf
presentation.pdf is a powerpoint presentation showing results of research.
Using symgenoevolab's SyntenyFinder, the scripts were used to look for relationships between ALG ancestral genes of bilaterals and GC content for B. floridae, P. maximus, L. longissimus, C. mucedo, M. membranacea
While looking at the GC distribution of genes, an interesting observation was a bimodal distribution in L. longissimus chromosome 6.
Using the sliding window analysis, it was observed that the high GC genes, GC content between 0.5 to 0.6, were concentrated in specific areas.
Lewin, T. D., Liao, I. J., Chen, M., Bishop, J. D. D., Holland, P. W. H., & Luo, Y. (2024). Fusion, fission, and scrambling of the bilaterian genome in Bryozoa. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2024.02.15.580425
In each species, the repeat GC seemed to be more extreme, having higher GC in higher GC regions and vice versa, in each window.
This held true across all species investigated if we took the GC content of the repeatmasked regions in the genome, and the GC content of the non-repeatmasked regions.
