This blog consists of daily news items of interest to followers of QUIDDITY as a qualitatitive method of describing the world. This approach was described by Clive Barker in "The Great and Secret Show" and analyzed in the 149 posts of the QUIDDITY blog of this writer (see link to companion blog).
Thursday, September 15, 2016
Accurate Gene Algorithms
Yes, Computing Genetic Ancestors is Super Accurate Science synopsis: Ancestral gene sequence reconstruction
benchmarked via synthetic phylogeny; results offer promise for protein
September 15, 2016 at Georgia Tech Horizons, Atlanta,
Remnants of extinct monkeys are hiding inside you, along with those of
lizards, jellyfish and other animals. Your DNA is built upon gene fragments
from primal ancestors.
Now researchers at the Georgia Institute of Technology have made it more
likely that ancestral genes, along with ancestral proteins, can be confidently
identified and reconstructed. They have benchmarked a vital tool that would
seem nearly impossible to benchmark. The newly won confidence in the tool could
also help scientists use ancient gene sequences to synthesize better proteins
to battle diseases.
For some 20 years, scientists have used algorithms to compute their way
hundreds of millions of years back into the evolutionary past. Starting with
present-day gene sequences, they perform what’s called ancestral sequence
reconstruction (ASR) to determine past mutations and figure out the genes’
That protein comes from a common ancestor humans share with rats.
Time travel substitute
But ASR algorithms have faced logical criticism. Species based on those
primal genes are long extinct, and scientists can’t travel back in time to
observe mutations that have happened since. So, how can anyone find any
physical benchmark to verify and gauge ASR?
A team of researchers led by Gaucher did it by building an evolutionary
framework out of myriad mutations. Then they benchmarked ASR algorithms against
it – no time machine required. Their results have shored up confidence that the
widely used algorithms are working as they should.
“Most of them did a very good job – 98% accurate,” Gaucher said of
contemporary algorithms’ ability to compute ancient gene sequences. Their
determination of proteins encoded by those sequences was virtually perfect.
Gaucher, research coordinator Ryan Randall and undergraduate student Caelan
Radford published their results on Thursday, September 15, 2016, in the
journal Nature Communications. Their research has been funded by the NASA
Exobiology program, E.I. du Pont de Nemours and Company (DuPont) and the
National Science Foundation.
Holographic tree branches
Ancestral sequence reconstruction is like making a family tree for genes.
The many twigs and branches at the treetop would be sequences from species
alive today. Shimmying down the tree, called a phylogeny in genetics, you would
find their common ancestors, millions of years old, in the lower branches.
There’s a caveat; none of the lower branches exist any longer. They
vanished in the extinction of the species bearing those genetic sequences.
ASR computes them back into place using algorithms based on scientific
models of evolution. It’s like replacing missing branches with holographic
Algorithm horse race
The accuracy of those evolutionary models has been a historic sticking
point. And doubts about the algorithms based on them linger in some circles
that hold on to an old, tried-and-true algorithm.
So, Gaucher and researcher coordinator Randall pitted the contemporary
model-based, or “maximum likelihood,” algorithms in a race against the generic,
or “parsimony,” algorithm.
“Parsimony follows the simplest notion of evolution, which is that very
little mutation occurs,” Randall said. The models behind contemporary “maximum
likelihood” algorithms, by contrast, are laced with filigree, data-packed
For the race, Randall made a track of sorts by putting a gene sequence that
made a single protein through multiple mutations to construct a real-life
phylogeny. She used methods that closely mimicked natural evolution, but that
were much, much faster.
Rainbow phylogeny racetrack
In cells, enzymes called polymerases aid in DNA duplication. They
work very efficiently, but their rare mistakes are the most common source of
mutations, and Randall took her lead from this.
“We used a polymerase that is error-prone to speed up mutations, and speed
up evolution,” she said.
The genes used at the starting point of the lab evolution made a protein
that fluoresced red when placed in bacteria. As significant mutations
arose, the proteins began changing color. Bacteria containing green
fluorescing proteins popped up among the red ones.
Randall divided bacteria with major mutations into new groups, creating
branches in the phylogeny, as she went. Many mutations produced new colors –
yellow, orange, blue, pink – and Randall ended up with a gene family tree in
Show me the phenotype
The colors reflected not only new gene sequences but also new phenotypes –
the actual proteins they produced, the organism’s working molecules.
“What counts is phenotype,” Gaucher said. “When you analyze DNA strictly by
itself, it ignores the context, in which that DNA is connected to phenotype,”
DNA can mutate and still encode the same amino acids, protein’s component
parts. Then the mutation has no real effect. But when mutations cause DNA to
encode different amino acids, they’re more significant.
A worthy test of ancestral sequence reconstruction algorithms must
therefore include phenotype. And Randall took this into account when she
selected mutated proteins.
“I selected for variants to purposely make it hard on the algorithms to
infer the phenotypes,” she said. The race ensued, and the algorithms got
limited information to infer the evolutionary tree’s many dozens of past
ASR a sure bet
Though the tried-and-true parsimony algorithm performed well, maximum
likelihood performed better. “Even though it got the same number of
residues (DNA sequences) wrong as parsimony, the incorrectly inferred sequences
were still more likely to encode the right phenotypes,” said undergraduate
student Caelan Radford, who analyzed the experiment’s statistics.
The margin of error was so tiny that it would not interfere in the
determination of past species.
The experiment’s outcome was not too surprising, because prior simulations
had predicted it. But the researchers wanted the scientific community to
have physical proof that feels trustier than proof from a computer. “It’s
a computer algorithm. It will do what you will tell it to do,” Gaucher
Short history of ASR
Doubts about ancestral sequence reconstruction -- and maximum likelihood
algorithms in particular -- go far back. The idea of performing ASR first
came up in 1963, but it didn’t get started until the 1990s, and back then,
researchers battled fervently over wide-ranging methods.
“People would come up with the craziest notion as to why one model was
best,” Gaucher said. “They’d say, ‘Well, if I simulate this weird mode of
evolution along these branches here, my algorithm will work better than your
The parsimony algorithm was a way of reigning in the chaos that grew out of
a lack of data in evolutionary models at the time. “When the model is
wrong, ‘maximum likelihood’ fails miserably,” Gaucher said.
But, now, a host of data and analysis give scientists a great picture of
how evolution works (and it’s not a parsimony principle): For ages, nothing
moves, then change bursts forth, then things stabilize again.
“You get this quick evolution, so lots of stuff works and lots of stuff
fails, and the stuff that works then goes on and kind of maintains its status
and doesn’t change,” Gaucher said. By confirming the high accuracy of the
algorithms, the Georgia Tech team has also corroborated the validity of current
evolutionary science they’re based on.