GeRM - Generalised RNA multivalency

Mutual homeostasis of charged proteins

Rupert Faraway, Neve Costello Heaven, Holly Digby, Oscar G Wilkins, Anob M Chakrabarti, Ira A Iosub, Lea Knez, Stefan L Ameres, Clemens Plaschka, Jernej Ule

bioRxiv (2023) https://doi.org/10.1101/2023.08.21.554177

Introduction
Installation
Testing
Quickstart
Parameters

Introduction

GeRM is a command-line tool written in R and Rcpp to calculate Generalised RNA Multivalency Scores for user-supplied sequences. The custom functions are contained within the GeRM R package.

The algorithm

GeRM is calculated from a string of consecutive overlapping nucleotide sequences of length k (k-mers).

In non-mathematical terms, the GeRM score is calculated by comparing a k-mer to all the other k-mers that surround it in a fixed window. For each of the surrounding k-mers, the sequence similarity to the central k-mer is calculated from the negative exponent of the Hamming distance, such that k-mers with identical sequences have a high score and those with unrelated sequences have a low score. The constant λ determines how quickly this similarity score decays as sequences become more dissimilar to the central k-mer. This sequence similarity score is multiplied by a distance score, which decays linearly from 1 to 0 with distance from the central k-mer. k-mers that overlap with the central k-mer are ignored. For k-mers at the edges of transcripts, where the window exceeds the end of the transcript, all positions that fall outside of the transcript are given a score of 0. The sum of all the distance-weighted sequence similarities is summed to give the GeRM score.

For more details please see the Methods section of the manuscript.

Installation

To install GeRM, first clone the repository to your local computer with

git clone https://github.com/ulelab/germ.git

Then, there are two options for installing the dependencies.

1. Conda option (recommended)

If you have Conda on your system you can create a virtual environment which installs R and all the dependencies using the provided YAML. First move into the directory into which you cloned GeRM and then run:

bash create_env.sh

You can then activate the environment using:

conda activate germs

2. R option

GeRM requires R to be installed on your system and uses some R (optparse, devtools, data.table, tidyverse, scales, ggthemes, cowplot, patchwork, logger) and Bioconductor packages (Biostrings). If you have R already installed, you can install the GeRM R package by moving to the directory into which you cloned GeRM and then run:

R -e 'devtools:install()'

3. Docker option

We will soon have a GeRM Docker container available for use.

Testing

To test the installation has worked you can run the test script. This runs three sets of GeRM test for different sequences and parameters:

bash testrun.sh

Quickstart

GeRM can be run from the command line using:

Rscript germs.R --help

This will output the help for all the parameters that can be supplied to GeRM. The minimum is to provide a FASTA file with sequences for which to calculate GeRM scores (--fasta, -f)

Parameters

Basic

--fasta or -f is used to supply the input FASTA file with the sequences for which GeRM scores will be calculated.
--k_length or -k is used to supply the k-mer length with which to assess multivalency (default: 5).
--window_size or -w is used to supply the window size for calculating multivalency (default: 123).
--smoothing_size or -s is used to supply the smoothing window size (default: 123).
--output or -o is used to supply the output TSV filename. If one is not supplied, then it is generated using the fasta filename, k-mer length, window size and smoothing window size.

Customise GeRM calculation

--lambda is used to supply the lamba value for exponential decay scaling (default: 1).
--scaling_function is used to to supply a custom scaling function.

Visualisation

--transcripts or -t is used to provide either a comma-separated list of sequence names or a text file with one sequence name per line to plot.
--plot_folder or -p is used to specify the folder in which to output the plots (default: plots).

Other

--cores or -c is used to specify the number of cores to use for parallel processing (default: 4).
--logging or -l is used to specify the level of logging (default: INFO).

ulelab/germ