It’s 1994. Portable CDs were all the rave, phones had become mobile and personal computers were more
accessible. With the launch of the human genome project in 1990 and the first genetically modified
food approved for sale in early 1994, the world of genetics was quite different to what it is today.
While many key genetic discoveries had been made before and during the early 90s,
geneticists were faced with a new world of technologies to conduct their research in.
With very few available genetic software’s, Kumar, Tamura and Nei decided to develop an
analysis tool for widespread use. They developed MEGA – the Molecular Evolutionary Genetics
Analysis software for microcomputers. The aim of MEGA was simple: to provide an advanced
analysis tool with a user driven interface that could be run on personal computers.
But why was this software development so crucial? In the 90’s, methods of phylogenetic analysis and
categorisation of species, while possible, was overly complicated and time consuming.
The world of genetics needed an efficient software to conduct this analysis. This
is how MEGA filled the gap in the genetics world, with the software combining data from
multiple data bases to produce accurate and interactive phylogenetic trees.
But what are phylogenetic trees? They are tree-like diagrams representing the relationships
between species that are assumed to have evolved from a common ancestor. The tree displays these
relationships through the estimation and analysis of either nucleotide or protein sequences.
In MEGA, the trees produced are used to provide statistics on the relationships
of homologous sequences and to estimate the evolutionary divergence between them.
So, how is the algorithm designed to produce this? MEGA mainly uses distance estimation methods,
which measures the genetic differences between the molecular sequences inputted by the user.
Distance estimation methods in MEGA determine the nucleotide differences between sequences,
identifying transition or transversion base changes, unequal base frequencies,
and varying substitution rates. It then categorises these distances into
three groups: nucleotide, synonymous and non-synonymous, and amino acid distances.
Firstly, nucleotide distances determine how many nucleotide substitutions occurred at
each site to infer which sequences were more closely related. Next,
the amino acids encoded are categorised into synonymous, meaning no amino acid change occurs,
and non-synonymous, meaning that the different nucleotide encodes a new amino acid.
Finally, the number of different amino acids between sequences is determined.
MEGA’s software is designed to calculate the nucleotide frequencies at either the first,
second or third position of the codon.
Phylogenetic interference must also be considered, where the evolutionary history of related species
is re-evaluated, and the species are more accurately grouped. The maximum parsimony
method is used to account for this. This method uses the evolutionary relationships of nucleotides
at each site to determine interference and more accurately categorise species.
Finally, MEGA needed some pretty good statistical analysis tools. It uses both the
bootstrap and standard error test to determine the reliability of the phylogenetic trees produced.
The bootstrap test randomly re-samples nucleotide sites to determine the relative
frequency of the appearance of bootstrap replications to determine reliability.
The standard error test is completed by calculating the standard error of
re-estimated branch lengths and conducting a t-test to determine the confidence probability.
We’ve now reached the final product: highly accurate and
comprehensive phylogenetic trees displaying the relationships between related species,
with varying complexity. That’s MEGA-mazing if you ask me.
So now that we know how MEGA works, how do we use it? Although the algorithm
of the 1994 version looks complex, it’s actually quite simple. Let’s break it down.
Users are firstly provided with an instruction manual containing a basic tutorial. They can
input either the nucleotide or amino acid sequence of the species they wish to analyse,
with missing data points allowed. The program does all the computing,
and once the phylogenetic tree has been produced, an editor is available.
MEGA was not only advanced for its time, but it was also easily accessible to geneticists,
and has become highly recognised software.
So, what is MEGA like now? Many software updates have occurred in the 27 years since version 1. A
notable mention is the 2007 version, aka MEGA version 4. It included an improved tree editor
tool that facilitated better understanding of the factors involved while forming the phylogenetic
tree. The 2016 version, MEGA 7, was also key, containing more sophisticated phylogenomic
tools, but most notably, was optimised to analyse larger data sets than ever before.
The current version, MEGA 11, was released in 2021, and contains extensive access
to molecular analysis tools that extend beyond phylogenetic trees.
While the development of MEGA provides the opportunity for unlimited genetic discovery,
we can discuss those another day.