genovis 1.0.1

GENOVIS: Population genomics visualization toolkit

GENOVIS Developers: Siavash Salek Ardestani & Elmira Mohandesan Contact: [e-mail] Released in 2025, GENOVIS is a visualization toolkit for population genomic analyses. It supports the generation of: Manhattan plots, three-dimensional PCA, SNP density maps, admixture plots, runs of homozygosity (ROH) intervals, and relationship matrices.

MIT License for GENOVIS

Copyright (c) 2025 Siavash Salek Ardestani, Elmira Mohandesan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

• v1.0.1 (2025-11-21) – Fixed a bug in the mapden module:
  • Force chromosome IDs to be read as strings to avoid issues with mixed/leading-zero chromosome labels: df=pd.read_csv(args.m,names=cols,sep=r'\s+') changed to df=pd.read_csv(args.m,names=cols,sep=r'\s+',dtype={'Chr': str}) genome_index=pd.read_csv(args.i,sep=r'\s+',header=None,names=['Chr','Size']) changed to genome_index=pd.read_csv(args.i,sep=r'\s+',header=None,names=['Chr','Size'],dtype={'Chr': str})
  • Sort chromosome IDs based on the order of chromosome IDs in index file: chromosomes = set(df["Chr"].unique()) changed to chrs_in_map = set(df["Chr"].unique()) chromosomes = [c for c in genome_index["Chr"] if c in chrs_in_map]