CGI Virology

By Michael J. Talmo, April 19, 2024 — Published in State of the Nation

The purpose of this article is to explain in lay language exactly what modern virologists are doing when claiming they have discovered new pathogenic viruses. The methods they now use are part of a rapidly evolving field of scientific research called virus genome sequencing.

All living things have a genome, which is an organism’s genetic blueprint. It’s what makes us humans what we are and everything else what it is. Therefore, whole genome sequencing (WGS) isn’t limited to virology. As explained here and here, this technology is also used for disease prevention, as in seeing if you have genetic mutations that can impact the health of your future children; predicting what diseases a person may acquire in old age; preventing and treating cancer more effectively; determining in advance the side effects of medications on a particular person, etc. But the focus of this article is the genome sequencing of viruses. So, let’s understand what it means.

As explained here, viral genomes, which can either be DNA or RNA, are very small compared to the genomes of larger, more complex living organisms. This makes sequencing them a faster and simpler task. For example, our human genome is composed of 3 billion DNA nucleotides, or letters. But the RNA genome of a coronavirus is supposedly only about 30,000 nucleotides; the RNA genome of an influenza/flu virus is supposed to be 13,500 nucleotides; and the DNA genome of the redondovirus is supposed to be a mere 3,000 nucleotides.

As explained in this 2017 article in the peer review journal Nature, three major methods are used for viral genome sequencing: “metagenomic sequencing, PCR amplicon sequencing, and target enrichment sequencing.”

Regardless of the methods used, the CDC (Centers for Disease Control and Prevention) explained that what virologists are doing is using computers to sequence RNA and DNA found in cell cultures into what they claim is a whole viral genome. They then further claim that it proves a particular virus exists or that a new virus has been discovered. But this is done without isolating actual virus particles, which, as reported by the CDC, have always “been critical in the detection of previously unrecognized viruses” and warned that these “classical techniques” should not be abandoned. Instead, modern virologists will access databases that contain millions of artificially sequenced viral genomes and compare them. If they don’t exactly match, they just claim it’s a new variant.

To quote this 2019 report from Harvard University:

“Computational biologists use pattern-matching algorithms, mathematical models, image processing, and other techniques to summarize and derive meaning from the sequencing data.”

The University of Washington’s Paul G. Allen School of Computer Science and Engineering further states in this article:

“Computers are needed to process, analyze, and store billions of DNA bases that can be sequenced from a single DNA sample. Even the sequencing machines themselves run on computers…After DNA is sequenced, it is usually processed and analyzed by a number of computer programs through what is called the DNA data processing pipeline.”

As a result, virologists assert that “New sequencing techniques have helped us uncover a world of new viruses,” as reported by Penn Medicine News.

The article goes on to ask the crucial question:

“Overall, we are asking whether we can take unknown DNA sequences and make sense of it by identifying new viruses from the whole universe of sequences in the human virome.”

What all this means is that virus genome sequencing, along with other terms used to describe it, are just fancy words for non-specific molecular markers. It’s like claiming footprints of Bigfoot and that sonar blips of what appears to be a large animal known as the Monster in the waters of Loch Ness prove these creatures exist and that you have actually found them. Obviously, you haven’t. You need the actual creatures. You need a body. In essence, virologists are claiming that you don’t need a body. Instead, they are claiming that fragments of genetic material artificially assembled into a complete genome prove the existence and/or presence of viral pathogens. And since these viral genomes are sequenced by computers, virus genome sequencing is really computer modeling.

The problem with computer models

Computer modeling, as explained here, is not scientific evidence. Nevertheless, it has its place in scientific research. I have no problem with using computer models for predicting weather patterns like hurricanes, evaluating the performance of aircraft, fleshing out dinosaurs, and a plethora of other things. But I have a big problem with using it as a substitute for medical research or to guide public health policies, for which it has failed miserably.

A 2014 study in Issues in Science and Technology, published by the National Academy of Sciences and Arizona State University, emphatically states:

“Evidence of poor modeling practice and of negative consequences for society abounds.”

This 2022 study, published in the International Journal of Forecasting, further states:

“Failure in epidemic forecasting is an old problem. In fact, it is surprising that epidemic forecasting has retained much credibility among decision-makers, given its dubious track record…implausible, exaggerated forecasts should be abandoned. Otherwise, they may cause more harm than the virus itself…Despite these obvious failures, epidemic forecasting continued to thrive, perhaps because vastly erroneous predictions typically lacked serious consequences.”

When it came to COVID-19, there should have been very serious consequences because, as reported in the journal Science, as early as March 2020, “Entire cities and countries have been locked down based on hastily done forecasts that often haven’t been peer reviewed…Long lockdowns to slow a disease have catastrophic economic impacts and may devastate public health themselves.”

As reported in National Review, “the most influential” and “one of the most wrong,” not to mention “wildly inaccurate,” computer models was created by epidemiologist Neil Ferguson of London England’s Imperial College. Ferguson’s COVID-19 model, by his own admission, “was based on undocumented, 13-year-old computer code that was intended to be used for a feared influenza pandemic, rather than a coronavirus.”

Ferguson’s COVID-19 computer model predicted, in the worst-case scenario, that without an immediate lockdown, over 500,000 deaths would occur in the UK and over two million deaths in the USA, which was totally wrong. As of April 2024, Worldometer reported only 232,112 deaths in the UK and 1,219,487 deaths in the USA. It doesn’t matter if Ferguson denied calling for lockdowns; both countries locked down because of his incorrect computer model. Of course, lockdown apologists will argue that it was because of the restrictions or NPIs (non-pharmaceutical interventions) that the worst-case scenarios didn’t occur. This peer-reviewed 2021 study and this 2023 article in New York Magazine emphatically document that the lockdowns were a failure and had little or no impact on case numbers and deaths.

It’s important to understand that computer models can be used for things that are real and for things that aren’t real. For example, experts in this field could construct a model using CGIs (computer-generated images) for how long it would take Godzilla to melt Manhattan with his atomic breath if he came ashore in New York City. Or a model could be done predicting who would win if Batman fought Captain America. None of these characters exist. But the computer doesn’t know or care about that.

Take fight sims as another example, which you can find all over YouTube. This is where they match up boxers from different eras and predict who would have won. Muhammad Ali (1942-2016) and Mike Tyson are used the most because they are two of the best-known boxers. But because Ali died, Tyson is used the most. They even had a CGI fight sim where he took on Rocky Balboa. The computer predicted that Rocky would win. Iron Mike is a real person. But Rocky Balboa is a fictitious character created and brilliantly played by actor Sylvester Stallone. But this is irrelevant to the computer.

Simply stated, computers are stupid. Even though they can process data faster than us, they will accept the assumptions and biases of the people who program them. So, if programmers assume that a particular pathogenic virus is real, the computer will follow that incorrect reality.

Genome sequencing is no different

As stated in this 2017 study published in the journal Nature, “Sequencing viral nucleic acids, whether from cultures or directly from clinical specimens is complicated by the presence of contaminating host DNA.” (including contamination from other organisms, as explained here) “By contrast, most bacterial sequencing is currently carried out on clinical isolates that are cultured; thus, sample preparation is comparatively straightforward… It is also important to remember that the detection of viral nucleic acid does not necessarily identify the cause of illness.”

In other words, as explained on page 702 of this 1993 study published in Nature/Biotechnology, a virus must be isolated or purified to establish its existence. Meaning, the viral particles, which have a genome that can be DNA or RNA encased in a protein coating called a capsid and sometimes an additional outer envelope called a lipid bilayer, must be separated from everything else that’s in a cell culture. The viral isolates or pure culture must then be photographed under an electron microscope. Its proteins and genome can then be identified or characterized. Once its existence has been established, it can then be determined if the virus causes a particular disease by applying either Koch’s postulates or their modification, known as River’s postulates.

Isolation of actual viral particles is essential because this 2022 study, published in the journal Current Opinion in Virology, emphatically states:

“Contamination, or false discovery, of non-viral sequences is a feature of all virus prediction software and should not be ignored…In most cases, the time, expertise, and/or computational resources are not available to manually validate all recovered viruses.”

The study further states:

“All software tools that predict viruses from metagenomes can make mistakes.”

“The reliance of most software tools on reference databases is a source of bias.”

“Metagenomes are puzzles: an unfinished puzzle is still just pieces.”

To put it in simple terms, virus genome sequencing is like taking bones from hundreds of different human skeletons that are the same size, assembling them into one skeleton, and claiming that it was once a living human being. The individual bones were from real human beings, but the composite skeleton is a model, a representation of a human being. It looks like the remains of a real human being, but it isn’t. The parts are real, but the model isn’t. In the same way, the genetic material virologists are finding is real, but the viruses they claim to have discovered aren’t.

As I see it, the logic of modern virologists goes something like this: Zero means nothing. But if you have a whole bunch of zeros, you must have something—especially if they are fancy, high-tech zeros that can be sequenced with the fastest, flashiest computers of all time. Nevertheless, the fact remains: a zero is still a zero, and no matter how many of them you have, they still equal nothing.

Conclusions

In my honest opinion, modern virology has become a religion. Claiming that nonspecific phenomena prove viruses exist is a faith-based assumption. It’s the equivalent of saying you know God exists because all that is exists. “Just look around,” believers will argue. “You don’t need to physically see God; you don’t need evidence. The creation we behold proves God exists.” Of course, they don’t consider the fact that the creation we behold can really just be existence and that there is another explanation for how everything got here. And just because they don’t know or understand other explanations for how everything got here doesn’t prove God exists.

In other words, faith is not a reliable path to truth. If it were, everyone would have the same religious beliefs. Since everyone doesn’t have the same religious beliefs and never has throughout recorded history, faith isn’t a reliable way to find the truth about anything. And let’s face another obvious fact: religion belongs in a church, not in a science lab.