Bioinformatics

In the earlier posts of this series (here, here, here and here), we covered the mathematical and biological aspects of evo and evo2. One important topic that we have not covered yet is how the models were trained.

In this article, I will argue that Multi Parameter Statistics, or even better, Massively Parameterized Statistics (MPS) better describes the application of AI models in biology and medicine. Also, I will introduce you to a new preprint on DNA sequence modeling that claims to match evo.

In the last three posts of this series (here, here and here), we covered the mathematical aspect of evo and evo2. Let us now discuss the biological findings from these models. It will take multiple posts to go over these topics.

In the first two posts of this series (here and here), we covered the AI-related mathematical concepts applied to evo and evo2. Before moving on to the biological side, here is one last post on the model.

In the first post of this series, we covered the basic technical terms of the evo and evo2 papers. We also mentioned the key technological innovation that made their work possible. That led to the question - if they were using fast fourier transform (FFT), were they using convolutional neural network (CNN)? The answer is no. The computer science work done by the Stanford group is quite groundbreaking. Let me go over that in detail.

Two recent papers applying AI-related large language models on DNA sequences are gaining a lot of attentions and a bit of controversy. The first paper titled Sequence Modeling and Design from Molecular to Genome Scale with Evo wrote -

Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods.

What are the rules of the genomes? What patterns do the genome sequences follow? What biochemical and evolutionary mechanisms are behind these patterns? Are newly published genomes and pangenomes displaying many exceptions to the rules, or do they all confirm the expected patterns?

In NGS experiments, when the researchers encounter issues with genome assembly or analysis, they go back to the raw data composed of sequencing reads. In a latest preprint submitted to zenodo, Steven C. Quay did exactly that for a seminal paper and concluded - “The alternative conclusion is that this sample was not a fecal specimen but was contrived. The data cannot, however, distinguish between a non-fecal specimen that came from true field work on the one hand and a specimen created de novo in the laboratory on the other hand.” This is no simple matter, because the entire world had been running like headless chicken for the last two years relying on the genome assembly submitted in the paper.

In early 2020, Prashant Pradhan and collaborators posted a preprint titled “Uncanny similarity of unique inserts in the 2019-nCoV spike protein to HIV-1 gp120 and Gag” in biorxiv. Based on the released emails from NIH under FOIA, we now know that this article and its coverage in zerohedge upset Fauci so much that he immediately convened an urgent meeting of virologists and several health bureaucrats from US, UK and Europe. All details of this meeting had been redacted, but the virologists present in the meeting fast-tracked a Nature Medicine paper claiming the virus definitely came from animals even though they described it as lab-engineered in their private emails. This paper was then used for over one year to censor all counter-arguments. Especially, biorxiv retracted the preprint due to intense pressure and thus destroyed its reputation as a preprint server for good.

An interesting paper came out recently in J. Phys. Chem. Lett.. Based on their analysis of over 2.2 million SARS-CoV-2 genomes, the authors observed -

US establishment biologists are so tone-deaf that they gave Trevor Bedford both Howard Hughes and MacArthur awards. These same people also scream at the top of their lungs - “Trust the experts”. Here is what I got by trusting “experts” like Trevor Bedford.

Eighteen months after publishing the infamous letter organized by Peter Daszak, disgraced British rag The Lancet made a U-turn. Now it is making “an appeal for an objective, open, and transparent scientific debate about the origin of SARS-CoV-2”. Talk about shutting the barn door after the horse has reached the other continent.

Yesterday, an explosive set of leaked documents on the origin of SARS-CoV-2 virus got released by DRASTIC. People following the topic are describining them as “worse than the Chernobyl in the biology field”. In my opinion, this release changed the entire understanding of the origin of the pandemic and exposed a group of people as extremely wicked, shockingly evil and vile (sorry to borrow the movie name). Let me explain why.

The scientists looking for the true origin of the Wuhan flu are excited about three major developments over the last few weeks. Let me list them here.

A few months back, Eran Elhaik privately shared a preprint of his article on indiscriminate use of PCA in population genetics. I thought it would challenge many accepted discoveries in the field. The paper is currently available at biorxiv as “Why most Principal Component Analyses (PCA) in population genetic studies are wrong”.

In an earlier post, I wrote about five open problems in bioinformatics. In the next several posts, I will select each of them and discuss in some detail. The current post is on the shotgun development biology experiments and related challenges.

In twitter, a number of researchers are discussing about the open problems in bioinformatics. Therefore, I wanted to share a set of unsolved problems I am curious about. Please tweet your suggestions in reply to this tweet, and I will add them below with your name.

I like to make our readers be aware of the Chinese publications from where the claim that the virus came from bat originated. The key sequence to understand the origin of Covid is RaTG13, which you can download from here. You can also download the raw data files from NCBI SRA (SRX7724752 and SRX8357956).

Over the last eighteen months, biologists funded by the NIH participated in a massive coverup of the origin of the covid virus. Now that they are exposed, these people are acting rather strangely, reminding me of cockroaches running away from the shining light. We like to provide our readers with a detailed overview so that they can get entertained by the actions of these lowly creatures. I have not been so amused ever since Dan Graur vanquised the ENCODE team in 2013 (check “On the Immortality of Television Sets: “Function” in the Human Genome According to the Evolution-Free Gospel of ENCODE”), but ENCODE, to its credit, did not get anyone killed (apart from science itself).

The origin of SARS coronavirus causing the pandemic is still a mystery due to paucity of early data. This is puzzling because Wuhan, where the pandemic started, is equipped with world-class virology labs. A recent finding by Jesse Bloom, a virologist from Fred Hutch, suggests that we are likely being deliberately misled. By checking the internet caches, he recovered an entire set of early measurements deleted from the NCBI SRA database in March 2020, possibly based on an order from the Chinese government. Incorporating these early measurements point to a progenitor of SARS-Cov2 different from the commonly accepted one.