Matrix Algorithms for Genome Evolution (MAGE)

Introduction       

Matrix Algorithms for Genome Evolution (MAGE) presents novel computer-simulation approach and solution for decades-long controversies in population genetics, where several opposing theories (e.g. Selectionism and Neutralism) modeling intricate dynamics of mutations have not reached a consensus yet. We bring forth new software named MAGE.pl and MAGE.java that perform large-scale computational simulations of genome evolution of 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. As a consequence of a highly mosaic structure of mammalian genome gametes (formed from meiotic recombinations of parental chromosomes), our results demonstrate that population fitness is preserved under the intense influx of mutations. This applies even when deleterious mutations significantly outnumber beneficial ones.

Abstract       

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 closely linked mutations known as haplotypes. The linkage within haplotypes is constantly disrupted due to meiotic recombination events. 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 Matrix Algorithms for Genome Evolution (MAGE) to model genomic changes taking into account all mutations in a population. MAGE has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have influence on genome evolution such as: 1) authentic arrangements of genes and functional genomic 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 MAGE 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 parental chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.

Resources:       

Step by Step Illustrations of MAGE.pl    MAGE_Instructions.pdf

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

MAGE_r1.pl    Download

MAGE_r2.pl    Download

MAGE_r01.java    Download

MAGE_r01_Java_Pseudocode.pdf    Download

MAGE.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 MAGE.java Input Files    MAGE_Java_Input_Example.tar.gz

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