rNMP Hotpsots

To obtain rNMP hotspots(locations of highly abundant rNMPs) using a percentage or poisson pvalue threshold and map on respective reference genomes.

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Table of Contents

1. Installation
• Getting the code
• Creating the enviroment with required dependencies
• Additional Dependencies
2. Usage
• Defining variables
• Normalization of bed files for coverage (optional)
• Generating matrix
• Getting common hotspots for each genotype using different thresholds and visualization
• GGseqlogo plots (MEME plots)
• Additional visualizations
3. Contributing
4. License
5. Contact
6. Citations

Installation

Getting the code

The development version from GitHub with: sh git clone https://github.com/DKundnani/rNMP_hotspots.git

Creating the enviroment with required dependencies

sh conda env create --name rNMPhotspots_env --file /rNMP_hotspots/yml/r_env.yml

Additional Dependencies

• Input files (bed) containing single nucleotide locations, mainly for rNMP data. (another single nucleotide data can also be experimented on!)
• Reference genome files (.fa and .fai) of the organism being used(Also used to generate bed files)
• BAM files (optional) from DNA-seq pipelines See https://github.com/DKundnani/Omics-pipelines

Usage

Defining variables

bash lib=path/to/AGS/ribo-DNA-order #First col Library name, 3rd col basename of bam files from DNA-se pipeline, bed=path/to/AGS/bed dna=path/to/AGS/DNAseq/aligned normbed=path/to/AGS/norm_counts script=path/to/AGS/rNMP_hotspots/scripts genome=path/to/reference/sacCer2/sacCer2-nucl.fa.fai #size file of the genome

Normalization of bed files for coverage (optional)

```bash conda activate rNMPhotspots_env #activating enviroment mkdir $normbed #Creating output directory for sample in $(seq 1 6 | xargs -I aa echo Yaa); do samtools depth -a ${sample}.bam > ${sample}.cov & done #Get coverage file from bam files

while read -r line; do FS=$(echo $line | tr " " "\t" | cut -f1) sam=$(echo $line| sed 's/\r$//' | awk '{print $3}') Rscript $script/count_norm.R -r $bed/*.bed -c ${dna}/${sam}.cov -g $genome -o $normbed ; done < $lib > $normbed/norm.log

```

Generating matrix

```bash mkdir $normbed/hotspots #place files into the normbed folder cd $normbed

getting files per genotype

filelist=$(cut -f3 files | uniq | tr "\n" "\t") for f in $filelist; do grep $f files > ${f}_files; done

thresh=2 #Minimum 2 libraries in each subtype used as threshold for f in $filelist; do $script/dfmatrix.R -f ${f}files -a -t $thresh -c 8 -s -o ${f}files${thresh}commonEF.tsv & done #files contain library information per genotype to be grouped for finding hotspots ```

Getting common hotspots for each genotype using different thresholds and visualization

```bash mv tsv ./hotspots/ cd hotspots top=25 #Getting top 25 hotspots for f in $filelist; do Rscript $script/plothotspots.R -m ${f}files*all -c -g $genome -r BSgenome.Scerevisiae.UCSC.sacCer2 -t $top -v -o . & done

Getting top fraction of hotspots

for thresh in 0.05 0.02 0.01 ; do for f in $filelist; do Rscript $script/plothotspots.R -m ${f}files*all -c -g $genome -r BSgenome.Scerevisiae.UCSC.sacCer2 -t $thresh -o . & done done

```

GGseqlogo plots (MEME plots)

bash for thresh in 0.05 0.02 0.01 ; do for f in $filelist; do Rscript $script/meme.R -f ${f}_files*${thresh}*top* -c 9 & done #ggseqlogo plots done

Additional visualizations

See stacked barplots for composition in RPA-wrapper

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Deepali L. Kundnani - deepali.kundnani@gmail.com

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Citations

Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!+ * Light-strand bias and enriched zones of embedded ribonucleotides are associated with DNA replication and transcription in the human-mitochondrial genome. Penghao Xu, Taehwan Yang, Deepali L Kundnani, Mo Sun, Stefania Marsili, Alli L Gombolay, Youngkyu Jeon, Gary Newnam, Sathya Balachander, Veronica Bazzani, Umberto Baccarani, Vivian S Park, Sijia Tao, Adriana Lori, Raymond F Schinazi, Baek Kim, Zachary F Pursell, Gianluca Tell, Carlo Vascotto, Francesca Storici Acids Research 2023;, gkad1204, https://doi.org/10.1093/nar/gkad1204 * Distinct features of ribonucleotides within genomic DNA in Aicardi-Goutières syndrome (AGS)-ortholog mutants of Saccharomyces cerevisiae Deepali L. Kundnani, Taehwan Yang, Alli L. Gombolay, Kuntal Mukherjee, Gary Newnam, Chance Meers, Zeel H. Mehta, Celine Mouawad, Francesca Storici bioRxiv 2023.10.02.560505; doi:https://doi.org/10.1101/2023.10.02.560505 * Kundnani, D. (2024). rNMP_hotspots:2.0.0 (2.0.0). Zenodo. https://doi.org/10.5281/zenodo.8152090

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