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VenusPrincess

Origin of target DNA sequences for Covid-19 PCR tests

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From Wikipedia:

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Polymerase chain reaction (PCR) is a method widely used to rapidly make millions to billions of copies of a specific DNA sample, allowing scientists to take a very small sample of DNA and amplify it to a large enough amount to study in detail.

 

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Viral DNA can be detected by PCR. The primers used must be specific to the targeted sequences in the DNA of a virus, and PCR can be used for diagnostic analyses or DNA sequencing of the viral genome.

 

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PCR employs two main reagents – primers (which are short single strand DNA fragments known as oligonucleotides that are a complementary sequence to the target DNA region) and a DNA polymerase.

 

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One major limitation of PCR is that prior information about the target sequence is necessary in order to generate the primers that will allow its selective amplification.[36] This means that, typically, PCR users must know the precise sequence(s) upstream of the target region on each of the two single-stranded templates in order to ensure that the DNA polymerase properly binds to the primer-template hybrids and subsequently generates the entire target region during DNA synthesis.

My question is regarding the origin of the target sequences for Covid-19 PCR tests. Can anyone point me towards a primary source?

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I've been rabbit holing through articles for the past hour and I've gone down the following path; I started with Optimization of primer sets and detection protocols for SARS-CoV-2 of coronavirus disease 2019 (COVID-19) using PCR and real-time PCR which states:

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A detailed description of how the SARS-CoV-2 viral RNA was prepared is provided in a separate report.

In that report, The architecture of SARS-CoV-2 transcriptome, it states:

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SARS-CoV-2 is a betacoronavirus responsible for the COVID-19 pandemic. Although the SARS-CoV-2 genome was reported recently, its transcriptomic architecture is unknown.

Searching for articles on the SARS-CoV-2 genome led me to Complete Genome Sequence of a 2019 Novel Coronavirus (SARS-CoV-2) Strain Isolated in Nepal which states:

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The specimen tested positive for SARS-CoV-2 by real-time reverse transcriptase PCR (rRT-PCR) developed in the University of Hong Kong (5).

(5) refers refers to the article Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia, which states:

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Two monoplex real-time RT-PCR assays targeting the ORF1b and N gene regions of 2019-nCoV were designed based on the first publicly available sequence in Genbank (Accession number: MN908947)

The first publicly available sequence in Genbank is connected to the article A new coronavirus associated with human respiratory disease in China, which states:

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To investigate the possible aetiological agents associated with this disease, we collected bronchoalveolar lavage fluid (BALF) and performed deep meta-transcriptomic sequencing. The clinical specimen was handled in a biosafety level 3 laboratory at Shanghai Public Health Clinical Center. Total RNA was extracted from 200 μl of BALF and a meta-transcriptomic library was constructed for pair-end (150-bp reads) sequencing using an Illumina MiniSeq as previously described[4],6–8.

[4] refers to the article Redefining the invertebrate RNA virosphere Sequence assembly and RNA virus discovery, which states:

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For each library, sequencing reads were quality trimmed and assembled de novo using the Trinity program[23] with default parameter settings. No filtering of host/bacterial reads was  performed before the assembly.

[23] refers to Full-length transcriptome assembly from RNA-Seq data without a reference genome, which states:

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Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

Successful mapping-first methods were developed in the past year4, but substantially less progress was made to date in developing effective assembly-first approaches. As the number of reads grows, it is increas-ingly difficult to determine which reads should be joined into contigu-ous sequence contigs. An elegant computational solution is provided by the de Bruijn graph7,8, the basis for several whole-genome assembly programs9–11. In this graph, a node is defined by a sequence of a fixed length of k nucleotides (‘k-mer’, with k considerably shorter than the read length), and nodes are connected by edges, if they perfectly overlap by k – 1 nucleotides, and the sequence data support this connection. This compact representation allows for enumerating all possible solutions by which linear sequences can be reconstructed given overlaps of k – 1.

In contrast to de novo assembly of a genome, where few large connected sequence graphs can represent connectivities among reads across entire chromosomes, in assembling transcriptome data we expect to encounter numerous individual disconnected graphs, each representing the transcriptional com-plexity at nonoverlapping loci. Accordingly, Trinity partitions the sequence data into these many individual graphs, and then processes each graph independently to extract full-length isoforms and tease apart transcripts derived from paralogous genes.

 

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I think the first full publication was Lu et al. (Lancet 395:10224 2020). 

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On 10/18/2020 at 8:11 PM, CharonY said:

I think the first full publication was Lu et al. (Lancet 395:10224 2020). 

Thanks. After doing extensive research I have come to the conclusion that there is no hard evidence that the putative COVID-19 virus exists.

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37 minutes ago, VenusPrincess said:

After doing extensive research I have come to the conclusion that there is no hard evidence that the putative COVID-19 virus exists.

What do you mean?  There is no virus?  The covid-19 virus has been misidentified?

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55 minutes ago, VenusPrincess said:

Thanks. After doing extensive research I have come to the conclusion that there is no hard evidence that the putative COVID-19 virus exists.

I hope you mean to say that there is no clear evidence regarding the origin of the virus (before it jumped to humans).  The conclusion that it does not exist at all is of course ridiculous.

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Extensive research? Really? It took you one hour to track down a paper where the authors sound like they're hedging the bets, or formulating disclaimers, which OTOH is common practice in the peer-reviewed world.

So the viral RNA could not be efficiently assembled from certain techniques based on eukaryotic splicing. So what?

What does that lead to, in the way of a conclusion? I can't build the Taj Mahal. Does that prove that there's no hard evidence that it exists?

They're describing an assembly problem, not a signature problem.

Even procaryotes have signatures of just 6 to 14-bases. It is well known that viruses have an even stronger adaptive pressure to be terse. I would expect re-assembly to be hard with eukaryotic techniques.

Can you lead us to a paper that talks about signatures, instead of assembly?

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What are the RT-PCR test results ("standards) with respect to Ct in various countries? (E.g., USA such "high" cases but China, India not so)

And does the Ct variation among different nations reflect what we see on the Johns Hopkins COVID-19 site?

 

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13 hours ago, invasive-feces said:

What are the RT-PCR test results ("standards) with respect to Ct in various countries? (E.g., USA such "high" cases but China, India not so)

And does the Ct variation among different nations reflect what we see on the Johns Hopkins COVID-19 site?

I have not watched the video, but Ct values do not differ between countries. They are the result of the qPCR. What you might mean is where the cut-off for a negative test is. I suspect there may be different recommendations but most use a cut-off around 35. There is not a huge variation and since typically the runs are further validated there is little reason to assume that difference between countries are due to different testing. In fact, there is good evidence that there is a much stronger correlation with measures taken (or not).

There are some folks who want to use it more diagnostically. E.g. using Ct values (which correspond to different concentration of isolated viral RNA) to determine viral load. However, that is not trivial as each step starting from sample collection introduces significant variation. Moreover, you would need to use internal standards to make sure that the values can be compared. That takes time as leaves room for error, which is why it is not often done.

Specifically, China had one of the most stringent responses, including isolating every positive case. While one might question the precise numbers, there are no indication for major outbreaks and in some cases they started testing the whole city when outbreaks were detected. These measures are known to massively reduce infections. The US we know that they have a scattered response and the President actively encourage folks in engaging in dangerous activities. And so the number rise, as expected.

India has actually a lot of infections and deaths (second only to the US). But they have a massive population and relative to the population they have tested much less than the US. I am not familiar enough with the responses in India to comment on their policy. But the numbers and trend definitely do not look great. I think there was a heavily criticized lockdown which has slowed cases a bit, but again, I would need to do more reading on India.

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