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.
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.
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.
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.
Over the last year, you probably saw news articles about how the Wuhan researchers collected bat poops from the
remote caves of Yunnan province and found novel viruses. One of those viruses matched closely with the SARS-Cov2
(or that is what they claimed) giving us some idea about the origin of the pandemic. We will talk about this
origin question in a later post. This one is about a much easier way to discover new viruses that you can safely
try at home. A group of bioinformaticians discovered two novel coronaviruses without leaving their sofa or
kissing the posterior of any bat or other dirty animal.
In the previous post, we covered the basics of genetic analysis. The tools discussed there will go a long way to help you follow various scientific discussions involving SARS-CoV-2 genetic data. Today we will quickly review that post, and then look into different “strains” of SARS-CoV-2 coronavirus.
Over the last few weeks, I received many questions related to genetics of the new coronavirus. Some of them are about genome-based tracking of this virus by the nexttrain team. Others are on claims about two strains of the virus (“L” and “R”), whether the virus is mutating rapidly into more deadly form, how the tests are made, how scientist know that it came from bat or pangolin and finally whether it is a bioweapon.
Prashant Pandey, Rob Patro and collaborators published a number of excellent papers on a new kind of “compound” hashing scheme. The original paper discussing the idea is available at “A General-Purpose Counting Filter: Making Every Bit Count”, but they published other papers linking their idea to bioinformatics. We wrote about Mantis last year in this blog.
While teaching R to biologists, a common complaint I hear is that “there are too many functions”. Therefore, I decided to take a minimalist approach and not teach students new functions unless those are absolutely necessary. Using existing functions for new tasks has two benefits - (i) it keeps the brain clutter-free from too many function names, (ii) it gives students more practice on the existing functions thus reinforcing their knowledge.