{"id":37635405,"url":"https://github.com/ulelab/germ","last_synced_at":"2026-01-16T11:05:26.227Z","repository":{"id":191599484,"uuid":"504106059","full_name":"ulelab/germ","owner":"ulelab","description":"An algorithm for scoring Generalised RNA Multivalency","archived":false,"fork":false,"pushed_at":"2023-09-14T13:59:09.000Z","size":2953,"stargazers_count":1,"open_issues_count":4,"forks_count":0,"subscribers_count":1,"default_branch":"dev","last_synced_at":"2023-09-15T05:29:02.378Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ulelab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null}},"created_at":"2022-06-16T10:11:06.000Z","updated_at":"2023-08-30T18:55:34.000Z","dependencies_parsed_at":"2023-08-30T16:27:10.850Z","dependency_job_id":null,"html_url":"https://github.com/ulelab/germ","commit_stats":null,"previous_names":["ulelab/germ"],"tags_count":1,"template":null,"template_full_name":null,"purl":"pkg:github/ulelab/germ","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ulelab%2Fgerm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ulelab%2Fgerm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ulelab%2Fgerm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ulelab%2Fgerm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ulelab","download_url":"https://codeload.github.com/ulelab/germ/tar.gz/refs/heads/dev","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ulelab%2Fgerm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478113,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T06:30:42.265Z","status":"ssl_error","status_checked_at":"2026-01-16T06:30:16.248Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2026-01-16T11:05:25.521Z","updated_at":"2026-01-16T11:05:26.220Z","avatar_url":"https://github.com/ulelab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GeRM - Generalised RNA multivalency\n\n**[Mutual homeostasis of charged proteins](https://doi.org/10.1101/2023.08.21.554177)**\n\nRupert Faraway, Neve Costello Heaven, Holly Digby, Oscar G Wilkins, Anob M Chakrabarti, Ira A Iosub, Lea Knez, Stefan L Ameres, Clemens Plaschka, Jernej Ule\n\nbioRxiv (2023) https://doi.org/10.1101/2023.08.21.554177\n\n## Table of contents\n\n1. [Introduction](#introduction)\n2. [Installation](#installation)\n3. [Testing](#testing)\n4. [Quickstart](#quickstart)\n5. [Parameters](#parameters)\n\n## Introduction\n\nGeRM is a command-line tool written in R and Rcpp to calculate **Ge**neralised **R**NA **M**ultivalency Scores for user-supplied sequences. The custom functions are contained within the GeRM R package.\n\n### The algorithm\n\nGeRM is calculated from a string of consecutive overlapping nucleotide sequences of length\n_k_ (_k_-mers).\n\nIn 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.\n\nFor more details please see the _Methods_ section of the manuscript.\n\n## Installation\n\nTo install GeRM, first clone the repository to your local computer with\n```\ngit clone https://github.com/ulelab/germ.git\n```\n\nThen, there are two options for installing the dependencies.\n\n### 1. Conda option (recommended)\n\nIf 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:\n\n```\nbash create_env.sh\n```\n\nYou can then activate the environment using:\n```\nconda activate germs\n```\n\n### 2. R option\n\nGeRM 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:\n\n```\nR -e 'devtools:install()'\n```\n\n### 3. Docker option\n\nWe will soon have a GeRM Docker container available for use.\n\n## Testing\n\nTo test the installation has worked you can run the test script. This runs three sets of GeRM test for different sequences and parameters:\n\n```\nbash testrun.sh\n```\n\n## Quickstart\n\nGeRM can be run from the command line using:\n\n```\nRscript germs.R --help\n```\n\nThis 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`)\n\n## Parameters\n\n### Basic\n\n* `--fasta` or `-f` is used to supply the input FASTA file with the sequences for which GeRM scores will be calculated.\n\n* `--k_length` or `-k` is used to supply the _k_-mer length with which to assess multivalency (default: 5).\n\n* `--window_size` or `-w` is used to supply the window size for calculating multivalency (default: 123).\n\n* `--smoothing_size` or `-s` is used to supply the smoothing window size (default: 123).\n\n* `--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.\n\n### Customise GeRM calculation\n\n* `--lambda` is used to supply the lamba value for exponential decay scaling (default: 1).\n\n* `--scaling_function` is used to to supply a custom scaling function.\n\n### Visualisation\n\n* `--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.\n\n* `--plot_folder` or `-p` is used to specify the folder in which to output the plots (default: plots).\n\n### Other\n\n* `--cores` or `-c` is used to specify the number of cores to use for parallel processing (default: 4).\n\n* `--logging` or `-l` is used to specify the level of logging (default: INFO).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fulelab%2Fgerm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fulelab%2Fgerm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fulelab%2Fgerm/lists"}