IAGS: Inferring Ancestor Genome Structure under a wide range of evolutionary scenarios

The number of novel species with high quality genomes are rapidly accumulating, signaling the start of a golden age for the study of genome structure evolution. Here, we develop IAGS, a generalized novel computational framework to infer ancestral genome structure for a variety of evolutionary scenarios. IAGS provides four basic models to solve simple single-copy (GMP and GGHP) and complex multi-copy ancestor problems (Multi-copy GMP and GGHP) with blocks / endpoints matching optimization (self-BMO and EMO) strategies and their combinations to decode complex evolutionary history in a bottom-up manner.

The previous can be found at IAG, which was only designed for three Papaver species.

Dependencies

Python 3.6

Packages	Version used in Research
numpy	1.19.2
pandas	1.1.5
matplotlib	3.3.4

Gurobi solver 9.1.2 with Academic License.

conda install -c gurobi gurobi 

Development environment: Windows 10
Development tool: Pycharm

Usage

Detailed instruction at docs UserGuide.pdf
Example usages in scenarios

Introductions

docs

User guide

Prepare input file

Please refer processDrimm to generate the input file.

dataSturcture

Basic data structure for IAGS.

inferringAncestorGenomeStructure

Containing the source code of four formulations, including GMP, GGHP, BMO and EMO.

models

Containing the source code of four basic models for IAGS, including GMP, GGHP, Multi-copy GMP and Multi-copy GGHP.

util

Including utils for downstream analysis.

inputdata

Four real datasets used in our research, including block sequences for three Brassica, nine Yeast, five Gramineae and three Papaver species. The dataset source as following.

Species	URL	Block
Brassica rapa (field mustard)	https://www.ncbi.nlm.nih.gov/assembly/GCF_000309985.2/	https://www.nature.com/articles/s41477-020-0735-y#Sec19
Brassica nigra	http://cruciferseq.ca	https://www.nature.com/articles/s41477-020-0735-y#Sec19
Brassica oleracea (wild cabbage)	https://www.ncbi.nlm.nih.gov/assembly/GCA_900416815.2	https://www.nature.com/articles/s41477-020-0735-y#Sec19
Eremothecium gossypii	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Lachancea kluyveri	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Kluyveromyces lactis	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Zygosaccharomyces rouxii	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Lachancea thermotolerans	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Lachancea waltii	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Naumovozyma castellii	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Kazachstania naganishii	http://gryc.inra.fr/index.php?page=download	Orthofinder and Drimm-Synteny
Saccharomyces cerevisiae	https://www.ncbi.nlm.nih.gov/assembly/GCF_000146045.2/	Orthofinder and Drimm-Synteny
Gordon, et al. pre-WGD Ancestor(Version 7 Aug2012)	http://ygob.ucd.ie/	Orthofinder and Drimm-Synteny
Zea mays	https://www.ncbi.nlm.nih.gov/assembly/GCF_902167145.1	Orthofinder and Drimm-Synteny
Sorghum bicolor	https://www.ncbi.nlm.nih.gov/assembly/GCF_000003195.3	Orthofinder and Drimm-Synteny
Oryza sativa	https://www.ncbi.nlm.nih.gov/assembly/GCF_001433935.1/#/st	Orthofinder and Drimm-Synteny
Brachypodium distachyon	https://www.ncbi.nlm.nih.gov/assembly/GCF_000005505.3	Orthofinder and Drimm-Synteny
Thinopyrum elongatum	https://bigd.big.ac.cn/gwh/Assembly/965/show	Orthofinder and Drimm-Synteny
Papaver rhoeas	https://xjtu-omics.github.io/Papaver-Genomics/	https://github.com/xjtu-omics/IAG/tree/master/inputFiles
Papaver somniferum	https://xjtu-omics.github.io/Papaver-Genomics/	https://github.com/xjtu-omics/IAG/tree/master/inputFiles
Papaver setigerum	https://xjtu-omics.github.io/Papaver-Genomics/	https://github.com/xjtu-omics/IAG/tree/master/inputFiles

scenarios

Pipline and example usages for four real datasets.

simulations

Including Non-CRBs and CRBs simulations.

Contact

If you have any questions, please feel free to contact: [email protected], [email protected], [email protected]

Reference

Please cite the following paper when you use IAGS in your work

Shenghan Gao, Xiaofei Yang, Jianyong Sun, Xixi Zhao, Bo Wang, Kai Ye, IAGS: Inferring Ancestor Genome Structure under a Wide Range of Evolutionary Scenarios, Molecular Biology and Evolution, Volume 39, Issue 3, March 2022, msac041, https://doi.org/10.1093/molbev/msac041