RibosePreferenceAnalysis

Preference analysis for ribose-seq data

This script is used for preference analysis of rNMP incorporation data generated from ribose-seq protocol and Ribose-Map software. The neighbor of incorporated rNMP keeps certain preference in a specific species. This is because the surrounding dNMPs could affect the probability of rNMP misincorporation by replicative polymerases. This software is designed to reveal this preference.

Citation

Xu, P., & Storici, F. (2021). RibosePreferenceAnalysis: Analyzing the preference of rNMPs embedded in genomic DNA. Software Impacts, 10, 100149. https://doi.org/10.1016/J.SIMPA.2021.100149

Dependency

Except Python3 standard libraries, the following packages are needed to run the scripts:

• Matplotlib

• Seaborn

• Scipy

Usage

1. Use count_background.py to calculate background frequencies for the reference genome (FASTA file).

   ./count_background.py <ref genome> -o <background frequency>

   Dinucleotide background frequency is calculated by default. Available parameters are:

   1. -d D Distance between dinucleotide pair, default = 1
   2. -s Count only one strand
   3. --mono Count mono nucleotide instead
   4. --trinuc Count trinucleotide instead

2. Use get_chrom.py to get background frequency of mitochondrial and nuclear DNA seperately

   ./get_chrom.py <background frequency> -s <chrM name> -o <chrM_frequency>
   ./get_chrom.py <background frequency> -s <chrM name> -v -o <nuc_frequency>

   Dinucleotide background frequency is calculated by default. Available parameters are:

   1. -s S [S ...] Chromosome to be selected, default = chrM
   2. -a Append to original file
   3. --name NAME Name for the output line, default = input file name

3. Count different patterns of rNMP incorporation from BED file generated by Ribose-Map using count_rNMP.py.

   ./count_rNMP.py <ref genome> <BED> -o <rNMP incorporation raw>

   By default only dinucleotide frequency is counted. Available parameters:

   1. -f Use fourth column of bed file as frequency. Otherwise each row of BED file is considered as a rNMP
   2. -m Also count mononucleotide frequency
   3. -d Also count dinucleotide frequency
   4. --dist DIST [DIST ...] Distance between rNMP and its dNMP neighbor
   5. -t Also count trinucleotide frequency

4. Get the data of desired chromosome.

   ./get_chrom.py <infile1> <infile2> ... -s <chromosome1> <chromsome2> ... -o <file desired>

   Available parameters:

   1. -v Select non-matching chromosomes
   2. -a Append to original file
   3. --name NAME Name for the output line, default = input file name

5. Normalization

   ./normalize.py <file desired> <chrM or nuclear frequency> -o <normalized file>

   Available parameters:

   1. --group_len {0,4,16} Number of rows of which the sum is 1. If 0 is selected, the sum of all rows will be 1. default = 0.
   2. --name NAME Name of chromosome in background frequency used for normalization, default = saccer

6. Rename and sort libraries if needed

   ./resort.py <normalized file> <order file> -o <sorted normalized file>

   Available parameters:

   1. -d D Connector of library informations, default = '-'
   2. -c C Column number of library name, default=1

7. Draw preference heatmaps.

   ./draw_heatmap.py <normalized file> -o <output figure name>

   Heatmap of rNMP incorporation normalized frequency. Available parameters:

   1. -b B Select background file. If a file is selected, the background percentage is added to labels.
   2. --background_chrom BACKGROUND_CHROM Chromosome name of background file, default = chrM
   3. --no_annot Hide percentage annotation in each cell.
   4. --palette Palette Define Seaborn color palette for heatmap.

License

This software is under GNU GPL v3.0 license

Contact

If you have any question, please contact me at pxu64@gatech.edu.