Genome Evolution by Matrix Algorithms (GEMA)


1. Shuhao Qiu, Andrew McSweeny, Samuel Choulet, Arnab Saha-Mandal, Larisa Fedorova and Alexei Fedorov. "Genome Evolution by Matrix Algorithms (GEMA): Cellular Automata Approach to Population Genetics" Genome Biol Evol 2014; 6(4):988-99

2. Shuhao Qiu and Alexei Fedorov. "Maruyama's allelic age revised by whole-genome GEMA simulations" (submitted to GENETICS)


Genome Evolution by Matrix Algorithms (GEMA) presents a computer-simulation approach and possible solutions for the decades-long controversies in population genetics, where several opposing theories (e.g. Selectionism and Neutralism) modeling intricate dynamics of mutations have not yet reached a consensus. We bring forth new software named and that perform large-scale computational simulations of genome evolution for complex organisms under intense influx of mutations existing in reality. These advanced program packages allow consideration of multiple population parameters that have not yet been examined together. We acknowledge the existence of a number of similar computational algorithms created in the past, which are described in the Appendix. GEMA belongs to the forward simulator class of these algorithms. Our long-term strategic goal is to recreate the complex dynamics of mutations in the entire human genome taking into account all parameters as close to reality as possible. This includes consideration of non-random distribution of mutations with their real frequencies, complex arrangement of genes along chromosomes, considerable inhomogeneity of nucleotide compositions in different genomic sequences, hot and cold spots for meiotic recombination events, variations in the contributions of maternal and paternal alleles on phenotypes, etc. A combination of these goals have not been attacked by previous computer algorithms, therefore, we designed our own GEMA package. Our policy is to make all computer codes freely available to public and provide support from our web pages. We appreciate a broad collaboration on GEMA project with world-wide scientific community.


Mammalian genomes are replete with millions of polymorphic sites, among which those genetic variants that are co-located on the same chromosome and exist close to one another form blocks of linked mutations known as haplotypes. The linkage disequilibrium between polymorphic sites within haplotypes is periodically disrupted due to meiotic recombination events that cause an appearance of new haplotypes. Whole ensembles of such numerous haplotypes are subjected to evolutionary pressure, where mutations influence each other and should be considered as a whole entity . a gigantic matrix, unique for each individual genome. This idea was implemented into a computational approach, named Genome Evolution by Matrix Algorithms (GEMA) to model genomic changes taking into account all mutations in a population. GEMA has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have an influence on genome evolution such as: 1) authentic arrangements of genes and functional genomics elements; 2) frequencies of various types of mutations in different nucleotide contexts; 3) non-random distribution of meiotic recombination events along chromosomes. Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness. In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding maternal and paternal chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (50-100 per individual) even when the number of mutations with deleterious effects is several times more abundant than those with beneficial effects.


Step by Step Illustrations of    GEMA_Instructions.pdf

Genome Evolution Model (GEM): Design and Application (Andrew Mcsweeny MS thesis: 2010)    Download    Download    Download    Download    Download

GEMA_r01_Java_Pseudocode.pdf    Download

GEMA.pl_Video    Download

The whole set of output files for results presented in our paper    P100.tar.gz      P50.tar.gz      P24.tar.gz      

Example of BackUp file    Example_backup.tar.gz

Example of Matrix Table    MatrixTable.txt

Video demonstration: How to create Matrix Table    MatrixTable.m4v

Example of Input Files    GEMA_Java_Input_Example.tar.gz

Introduction of GEMA    GEMA_Introduction_video.m4v

This video is the introduction to GEMA. It contains the old name of this project (MAGE). Soon it will be updated with the new name GEMA.

How to uncompress the .tar.gz file under the unix command line: tar -xvzf filename.tar.gz    

Two programs in the Paper to process the backup file from GEMA_r3:  AllelicAge_csv.txt       AllelicAge_10bin.txt

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