For more details about the package or to cite, please visit https://www.biorxiv.org/content/10.1101/2023.05.07.539760v1.

MiSiPi.RNA

Characterization of small RNA pathways

Installation and Basic Usage

You can find the full documentation and examples .

In order to install MiSiPi.RNA, you must first install devtools and BiocManager:

install.packages("devtools")

if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
  
devtools::install_github("stupornova33/MiSiPi.RNA")

library(MiSiPi.RNA)

RNAfold

In order for this package to work, you must also have RNAfold from the ViennaRNA package installed. You will need the path to the RNAfold executable. See https://www.tbi.univie.ac.at/RNA/ for installation.

Optional dependencies

For converting the .ps output files from the miRNA module to .png, install ImageMagick and ghostscript, then run

ps2png(path_to_magick_exe, file_dir)

where path_to_magick_exe is the full path to the binary executable and file_dir is the folder containing the .ps files. This will also be the output folder.

Input

The input for any of MiSiPi.RNA's main functions is an object created by theset_vars() function. Running set_vars will always be the first step in using this package. Below is a description of each of the parameters that will be passed to set_vars(). These should be changed based on your needs.

• roi - A bed file listing your regions of interest
• bam_file - A BAM file of aligned reads. Index file must also be present
• genome - A genome fasta file. Chromosome names must match the bed file
• min_read_count - This filters out loci with low mapping reads. Defaults to 1
• plot_output - (TRUE or FALSE) If TRUE, MiSiPi.RNA will output plots as pdfs
• path_to_RNAfold - Full path to RNAfold executable
• path_to_RNAplot - Full path to RNAplot executable
• pi_pal - Palette option for the generated piRNA heatmap (see below)
• si_pal - Palette option for the generated siRNA heatmap (see below)
• annotate_region - (TRUE or FALSE) Plots annotated gene features below the hairpin arc plot which is useful for characterizing cisNAT loci
• weight_reads - Determines if read counts will be weighted. ("Top", "locus_norm", or "None")
• gtf_file - Full path to a 9 column GTF file. Required only if annotate_region is TRUE
• write_fastas - (TRUE or FALSE) If TRUE, MiSiPi.RNA will write read pairs from functions to a file. Default is FALSE
• out_type - ("pdf" or "png") Specifies the output type. Default is "pdf"

vars <- set_vars(roi = "path/to/bed",
                bam_file = "path/to/bam", 
                genome = "path/to/genome",
                min_read_count = 1,
                plot_output = TRUE, 
                path_to_RNAfold = "path/to/ViennaRNA/RNAfold.exe",
                path_to_RNAplot = "path/to/ViennaRNA/RNAplot.exe",
                pi_pal = "BlYel",
                si_pal = "RdYlBl",
                annotate_region = TRUE,
                weight_reads = "None",
                gtf_file = "path/to/gtf",
                write_fastas = FALSE,
                out_type = "pdf")

Palettes:

Palette options are:

• "RdYlBl"
• "BlYel"
• "yelOrRed"
• "MagYel"
• "Greens"

Provide the vars object to the function of your choice and all lines contained in BED file will be run:

miRNA_function(vars)


piRNA_function(vars)


siRNA_function(vars)

To run the above functions all at once:

This outputs a table with metrics and statistics which can be used for summarization or machine learning. See the documentation for more details regarding values in table.

misipi_rna(vars)

To make a plot summary and sortable table of results:

# ml_plots is for users who have already run machine learning 
make_html_summary("full/path/to/run_all/", type = (one of "siRNA", "piRNA" or "miRNA"), ml_plots = FALSE)

Use built-in machine learning model to characterize loci:

See full documentation for more details.

#give the path to the directory that contains the folder and the name of the table
ml_probability("full/path/to/table/directory/", "table_ml.txt")