text:
signal peptides that can keep proteins at ER
I am studying an exogenous protein X that can modify specific mRNA. I would like to maintain the protein X in the rough endoplasmic reticulum. Are there any signal peptides that can maintain the protein on the cytoplasmic side of the rough endoplasmic reticulum?

I'm not an expert so take this with a grain of salt, but I'd suggest looking into the endogenous mechanisms by which proteins get anchored to the ER in the configuration that you like.
For example tail-anchoring by a hydrophobic transmembrane domain appears to be a common means by which cytosolic-side ER localization occurs. You could presumably use such a domain as a signal peptide. You might need to take some care in design, though, to ensure that cells will actually load the protein in question into the ER. That would argue for finding a specific endogenous example of the localization you want to see.
Here is a specific protein example that may be viable when expressed inside the target cell. But you may want to read up more specifically on the localization that you desire, since there are several flavors of ER membrane localization.
I assume that you want to express the protein inside your cells of interest, given that the cotranslational mechanisms for loading proteins into the cytoplasm are elaborate and possibly necessary for your application.


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No. of genes and organismal complexity?
Why do humans contain trillions of cells arranged in many complex organs, and worms contain many less cells and less organs, but we and the worm have a similar number of genes? See for example Table 1 here.
I know that the number of genes does not correlate well with organismal complexity, and the size of the genome is unrelated to the metabolic, developmental, and behavioural complexity. But I am curious WHY?

Can you reference any sources for the statements in your post?

See also a lot of questions on this site like https://biology.stackexchange.com/questions/80715/what-is-the-most-genetically-complex-organism

I would suggest looking into the field of organismal complexity.
One could turn the question on its head: why does a worm, which apparently has the raw materials to get as complex as a human, stay a worm?
One answer would be that the human developmental process is much longer and more elaborate than the worm's. We might for example have a more elaborate process of gene regulation throughout development that lets us keep developing longer- so the question is not how many genes, but what combinations of the genes we have can be turned on or off together. The number of possible combinations is very large when you have 20K genes!
More answers might have to do with the number of gene-gene or protein-protein interactions, that might underlie complexity in the way you mean. For example, from the source I linked:

Using bioinformatics, the team estimated that 650,000 protein interactions occur in humans; this number is approximately three times more than that in the roundworm and 10 times more than that in the fruit fly. Moreover, it seems that a single protein can have dozens, if not hundreds, of different interactions.

So when we say "complexity" what we might mean is number of gene-gene interactions, not the number of genes.
A fun aside, plants tend to have more transcription factors (which basically turn genes off and on and lead to interactions). Higher plants also usually develop continuously throughout their lives and can dedifferentiate their cells fairly readily. So if we're looking in this way, there might be an argument that plants are a lot more complex than humans, even while having similar numbers of genes!
Here is for example a tabulation of the number of transcription factors in various eukaryotes from this paper (apologies for not taking the time to retranscribe this image). Over time we've described/annotated more TFs in the various species of course, but even with the disproportionate effort on humans, plants still have more. For example, at last count soybean had almost 4000 TFs, whereas humans have around 1600.

That's not to say that this is the right measure of complexity. What you mean by complexity will differ depending on what you are interested in!

Thank you so much - lots of food for thought!!

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Understanding infection rate of a complex network
Suppose we have a complex network of people and  the spread of a disease is modelled as a branching process (Diekmann et al. 1998)
The rate of infection of the complex network is:
$r = \tau(n-2)-g$
where $\tau$ is the infection rate of the disease through 1 person
$n$ is the # of people in the network
and $g$ is the effective # of people / sec.
What does $g$ mean exactly?

My guess is that you actually found the equation $r = \tau (n-2) - g$ in Keeling and Eames (2005), where they cite Diekmann (1998), but use completely different notation. In their paper $g$ is the per capita recovery rate (time$^{-1}$, or 1/(infectious period)), $\tau$ is the transmission rate (time$^{-1}$ infected$^{-1}$), and $n$ is the degree of the network (i.e. the number of other people each person is connected to [edges per node] — not the total population size; unitless).
Keeling, Matt J, and Ken T.D Eames. 2005. “Networks and Epidemic Models.” Journal of the Royal Society Interface 2 (4): 295–307. https://doi.org/10.1098/rsif.2005.0051.

I didn't try particularly hard to find it, but I was not able to find the equation you include in the paper. Potentially you might need to share the exact source of the equation. After going through this (rather superficially) I think that you are mixing up the units and the definitions of your terms.
I'm not an epi person so someone with better credentials should feel free to fill in / correct.
Regardless, the paper does provide some definitions for a quantity $g$ in equations 4.2-4.5, and provides some motivation in the following statement:

We can use a branching process approximation to give a slightly more detailed description of the initial phase of an epidemic. Given ξ and given that the infective individual has k − 1 susceptible acquaintances, the probability that it will infect j acquaintances is equal to [Eqn 4.1, which is complicated]...

and then

The probability π that this process goes extinct when we start it
off with one individual can be found as the solution of the consistency condition

$\pi = g(\pi)$ (Eqn 4.3)
(see Eqn 4.4 as well)
$g$ is effectively a summation across that Eqn 4.1 with some extra decoration (including a term $z^{j}$ that they don't define very well but which I believe is the probability of not infecting any of $j$ individuals?), such that they then say

The conditions g(0) > 0 and g′(1) > 1 guarantee that 0 < π < 1. Note that for the present definition of $p_j$

$ g′(1) = (k − 1)Q = R_0$.
Where $Q$ is the overall probability of transmission, $k$ is number of individuals in a group, and $R_0$ is the familiar minimum viable reproductive rate.
I'm not going to stare at these equations any longer, but suffice it to say that $g$ appears to be a description of the now-unsusceptible (previously infected + immunized) population- when one more person gets infected, there is one less person who is susceptible (under this model, anyways).

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QTL mapping in Drosophila
I have already googled it before but yet unsuccessful in finding the whole topic with intuition. Please help.
A guide to an associated book would be greatly valued.

First you need two, hopefully homozygous, strains that differ in some measurable quantitative trait. In worms it has been done with lifespan; in mice injected with ethanol it has been done with long-sleep and short-sleep periods. The two strains need to differ in their RFLPs. For flies there are probably lots of behavioural traits one could use.
You set up a cross, and then mate individual brothers to sisters, and keep repeating this breeding pattern for multiple generations to establish recombinant inbred lines. After 10 generations they are basically homozygous, and you take the individual lines and assay them first for the trait you are scoring, and second for the molecular markers. The results of the molecular mapping let your draw the haplotype along each chromosome, so you know which parental strain contributed which portion or each chromosome.
The results of each quantitative trait scoring let you classify each recombinant inbred line into one of the two parental phenotype classes (or if it is a multi-locus trait then you may get some intermediate lines).
With those two results you will be able to correlate the absence, or presence, of molecular markers along the chromosomes, with the quantitative trait you are studying.
If it works you will be able to narrow in on a genomic region that co-segregates with the trait. If there are multiple genes involved you may be able to assign a probability or LOD score to different regions.

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Concentration gradients across membranes with different ions
I'm trying to gain an intuition for the dynamics of across neuronal membranes. The overarching idea here is they are controlled by ion concentration gradients across the membrane (which we can describe mathematically).
I'm looking for someone to delineate every present concentration gradient across neuronal membranes that are relevant here(involving $Na^+$, $Ca^{2+}$, $K^-$, and $Cl^-$).
It's unclear to me what creates a concentration gradient:

Are the concentrations of each type of ion independent from eachother, i.e. in a world without charge, diffusion across the membrane is individual to the concentration of each ion, or, is mediated by the net concentrations of mass (which makes less sense)?
Is the flow of ions across the membrane driven by net charge on each side, or the charge concentrations of each individual ion?

Hmm.. chemical gradients are individual while for the electric, we take the over all charge, or rather the potential difference. This is what we are actually doing in the Nernst equation right.. We equate the chemical potential to the electric potential difference to see, at which voltage it would balance out. So finally, chemical gradients of individual ions and net charge (implying potential). But still, lets wait for a well structured answer!

Ok thanks. that's what I was hoping it would be; otherwise Id be really confused. On that last point, I don't understand why chemical gradients work on individual ions – I'd think it's about the net chemical concentrations. What am I missing in my mental model?

I try to answer your two questions briefly:

Ion gradients are dependent on charge, but there exist independent transport mechanisms, extending diffusion. Trans-membrane transport proteins can specifically move only one sort of ions. Also symport or antiport exists, that can depend on gradients of other (e.g. non-charged molecules). In addition, the mentioned inorganic ions can form chemical bonding to organic ions for example, which neutralizes the electrical charge.
Beside an electrical potential, also chemical (concentration) potential can act as force on ios. In addition active transport, inactivation as mentioned above and other factors have an influence on the molecule flow.

You don't mention the Nernst equation at all here. Isn't that somewhat important?

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Where to go next with clustered C elegans neuronal time series
I'm doing some independent research with the C Elegans nervous system (with the OpenWorm project) and was looking for some guidance as to where I should go next.
Right now, I'm dealing with calcium fluorescence imaging time series datasets for a subset of neurons in C Elegans. Here are my five time series clustered by similarity:

and here are correlation matrices for the neurons across the datasets:

There are obvious patterns here.
I have many vague ideas of where to go next, but I'm wondering if anyone with more expertise can provide some guidance as to what approaches I can take to:

Look at this data in the context of known C Elegans neuronal connectivity
Trying to reconstruct connectivity from functional data (develop an algorithm for this, not a simple task)
Understand why certain correlations exist
Look at computations occurring among neurons in these datasets and isolate computational building blocks
Developing a minimal network model for C Elegans.
Understanding information flow

I'm overwhelmed by all of these ideas and need some guidance/a concrete starting point.

What is your worm background? For example, you mention constructing a connectivity network (or something like that), but the synaptic connections of all of these neurons has been known since 1986, based on serial electron micrographs. It seems like the names of the neurons is the same in all five datasets, their order in the figures is changing between

I'm aware we have a connectome and I have read lots of literature analyzing the static connectome. I'm interested in looking at functional data in the context of the connectome. One approach I had in mind is trying to develop an algorithm for reconstructing connectivity from functional data with the intention of attempting to develop a theory relating the two or to just get insight relating static graph characteristics to dynamics.2)The reason the neuron names change order between figures is because neurons are clustered differently in the different datasets, leading to different orderings.

Yes, it wouldn't let me edit my typo. What is the difference between the five datasets, in other words, what are the worms doing, crawling, eating, defecating, egg-laying, etc.? How any animals are being assayed in each dataset? Have you tried making a figure for each neuron, to compare how each one behaves in all five conditions?

This data was obtained from Saul Kato for his paper "Global Brain Dynamics Embed Motor Command Sequence of C Elegans". I'm able to infer movement patterns based on his results in the paper. Nevertheless, I don't have access currently to actual behavioral data.


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How can I obtain a computer readable model of Dr. Gerhard Michal's biological pathways map?
I want to run simulations of various metabolic pathways – the project could end up becoming quite large, and having a machine readable chart would make thing a lot easier. Does anyone know if there is a machine readable file of Dr. Gerhard Michal's famous metabolic pathways chart?

Can you think of any format you would like this map to be in?

If yes, you could maybe email ROCHE directly to ask. I am not so familiar with the standard methods used in numerical simulation of metabolic pathway to know whether there is such thing as a standard format for this kind of data.

Can you give more details about the sort of simulations you want to run?

Roche's Biochemical Pathways works as a big png image and just put labels on the map. But you could try to extract data using queries like http://biochemical-pathways.com/pol/fts/query?query=Glutarate
It seems to be legal as it's not prohibited.
Escher is a good pathway visualization tool where you can save map in JSON or SVG.
Another choice is VMH (Virtual Metabolic Human) - the virtual metabolic human database provides literature-derived information on human metabolism, gut microbial metabolism, nutrition, and diseases.
Reactome has two layer map - first layer is biological interactions, second layer is pathways.


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What is a holocentric chromosome?
I was doing this question that asked: "How many centromeres does a typical chromosome have?"
I thought one and the answer was:"One, except for holocentric chromosomes."
So then what are "holocentric chromosomes"?
I assume they would be chromosomes with more than one centromere, but then why do they need more than one centromere? And where can I get some more information on them? I tried googling but did not find much info on this.

I believe that you did the search but are you sure you spelled it correctly? Autocorrect may have changed it to Heliocentric Also are you using Google in a country other than the US? When I Google holocentric chromosome, the first two entries were NIH (National Institute of Health) Articles, then Images of Holocentric Chromosome, then the third was the English language Wikipedia article on Centromeres. It is possible that Google in another country will not have the same results as these.

I got the same results as you did. I didn't click open the NIH article, which proved to be quite helpful.

No worries. I was interested in seeing if maybe you were outside of the US and that would have been why you might not have gotten the necessary results. Thanks for responding!

In most eukaryotes, the kinetochore protein complex assembles at a single locus termed the centromere to attach chromosomes to spindle microtubules. Holocentric chromosomes have the unusual property of attaching to spindle microtubules along their entire length.

source


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Models of evolution by positive versus negative selection
I am making a program for simulating natural selection. Now, I have discovered an interesting difference between positive selection and negative selection.
Positive selection occurs when the population size is limited by resources.
Take bird territories as an example. Assume that there are not enough suitable
territories for all the birds of a generation.
The fates of the individual birds are interdependent in the following way:
Every time a bird finds a territory and successfully defends it, the chances for the remaining birds are reduced.
In other words, the chances for each bird of finding a territory depends
on how successful other conspecifics have already been.
Now assume that there are two different phenotypes with different fitnesses.
A rare mutant has higher chances of finding and defending a territory than
the wild type.
The probability distribution of the mutant after selection is described by
the little-known Wallenius' noncentral hypergeometric distribution
(see Wikipedia: https://en.wikipedia.org/wiki/Wallenius'_noncentral_hypergeometric_distribution )
Now consider negative selection. This occurs when the population size is limited by predation. Assume that the birds are hunted by predators and that the predators have a limited appetite or limited time and energy for hunting, so that the number of surviving birds is fixed. The fates of the individual birds are also interdependent under negative selection: Every time one bird is caught by a predator, the chances for the remaining birds are increased. Assume that the mutant birds have a higher chance of escaping predators. The Wallenius distribution now models the number of birds killed rather than the number of birds surviving. The distribution of surviving birds under negative selection can be called the complementary Wallenius distribution.
The difference between positive and negative selection only applies when the fates of the birds are interdependent. Under positive selection, the birds are selected for survival, one by one. Under negative selection, the birds are selected for elimination, one by one.
I have simulated these distributions and discovered that positive selection has the strongest effect when the overall survival rate is high, while negative selection has the strongest effect when the overall survival rate is low.
The difference is shown in the following graph. The graph shows the expected number of surviving mutants after positive versus negative selection. The initial population has 10 mutants and 990 non-mutants. The fitness ratio is 5.

The green line indicates positive selection and the red line indicates negative selection.
The grey line indicates the situation where the fates of the individuals are independent. We can observe that both positive and negative selection have stronger effects than when fates are independent.
Independent fates means that the chance of survival of each individual is independent of what happens to the other individuals. Independent selection can happen in the following cases: (1) Unrestrained growth. (2) Feeding, when food is plentiful, but difficult to find. (3) Predation, when predators have unlimited appetite, but limited success in hunting the species in question. (4) Diseases: The ability of one animal to survive a disease has no negative effect on the other animals.
The total number of survivors is not fixed under independent selection, but determined post factum.
The difference between the effects of positive and negative selection can be explained most easily by the number of individual selection events. A selection event under positive selection is a bird finding or winning a territory. The number of such events is higher when the survival rate is high. A selection event under negative selection is a bird being caught by a predator. The number of such events is higher when the death rate is high and the survival rate is low.
Now, here comes my question:
I find it hard to believe that I could be the first one to discover the statistical difference between positive and negative selection, but I cannot find any literature describing such a difference. Can you please direct me to any literature describing the difference between positive and negative selection in terms of the mathematical/statistical effects.

I don't think that you are using the same definition of positive selection as other people. Positive selection is usually more considered to be the outcome of innovation that allows competitive advantage; the rate of new innovations is positively related to the summed population mutation rate. There are many descriptions of positive/negative selection behavior in terms of gene frequencies, but it seems that you are assuming perfect heritability of different phenotypes and seeing what happens rather than doing pop genetics.

Thank you for your comment Maximilian. You are right that the terms positive and negative selection are often used in a different meaning than here. The concepts I am adding here are not described anywhere in the literature, as far as I can find. For lack of better terms, I am calling them positive and negative viability selection. I am open for other ideas if you have a good suggestion. My models are described in this preprint: https://www.researchgate.net/publication/375029380_Statistical_models_of_different_selection_mechanisms

I skimmed this question and the preprint again and I think that the main stumbling block here has to do with vocabulary. For example your "positive viability selection" is very close to "negative frequency dependent selection", though I suppose you can make some definitional arguments for a difference. Similarly "positive frequency dependent selection" looks a lot like your "negative viability selection". I am not an evolutionary ecologist though. I added the ecology tag to your question since I think that's more appropriate.

except most predators learn and have prefrences, if an organism is easy to kill, each predation could actually increase the number of predation events. As for desease you forget most deseases are contagious. when one gets infected in can drastically increase the chances of others being infected.

I have a hard time to understand exactly what and how you are modelling your scenarios, so it is difficult to answer the question. For one; are you modelling survival rates of individuals under different scenarios, or are you modelling actual evolution across time (e.g trait or allele frequencies across time)?
However, I think (as also suggested in the comment by @maximilian) what wording and language plays a role here. For instance, your framing of territorial competition (and the predator-prey scanario) are very similar to what is used in evolutionary game theory. You should look into theory in that area for inspiration and similarities.
You should also look at density dependent selection since theory in that area also deals with similar issue that you mention. For instance “K-selection” in crowded environments with fixed resources or territories. One recent paper you might find interesting is Bartram & Masel. 2019. Density-dependent selection and the limits of relative fitness.
I’m not sure that you will find what you are looking for here, but it might provide a couple of starting points

Thank you for the reference. You ask if I am modeling survival rates. I am modeling the statistical distribution of individual selection events as a function of phenotype. How many birds of each phenotype will find a territory? How many rabbits of each phenotype will avoid predators? These are statistical distributions. The statistical distributions get complicated when the fates of the animals are interdependent. The survival rates of birds of one phenotype depends on how many birds there are of a different competing phenotype. That's what I want to model.


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Is there a human gene or allele that has dramatically changed in frequency of occurence in the past circa 500 years?
I'm a writer and engineer, not a biologist, so please forgive me on errors. I've done hours and hours of reading of biology texts and research papers trying to answer this question for myself before asking here.
Is there evidence for any allele that has dramatically changed in frequency of occurrence in the past 500 years (or in that time frame)?
I'm just trying to get an idea of whether a genome would provide any evidence of deriving from one century versus another, so to speak (i.e. I'm not really concerned with actual centuries, but rather changes over that time scale).

Update
See this very recent post on this topic from the excellent Sasha Gusev.
Original answer
I would suggest this recent review of the literature on techniques for detecting very recent selection in human populations and some of the results they've gotten.
Some of the least ambiguous results are as follows:

Markovian coalescent (ASMC) detects targets of recent positive selection by inferring pairwise coalescent times and looking for unusually high densities of coalescent events in the recent past (Fig 2C(v)) [16,17]. When applied to whole-genome sequences of approximately 3,200 individuals of European ancestry, SDS detected selection signals in the past 2,000 to 3,000 years in the major histocompatibility complex (MHC) region and at variants associated with lactose tolerance and pigmentation [15]. In comparison, application of ASMC to over 487,000 British individuals identified signals of selection in the past 1,500 years, including those detected by SDS, as well as several new candidate loci harboring genes related to immune response, tumor growth, and other phenotypes [17].
...
These new methods have confirmed strong selection on variants associated with lactase persistence, immune response, and pigmentation traits in Europeans in the past few thousand years and some signals in other populations (such as the EDAR gene in East Asians), although very few new signals have been detected. [my emphasis]

There are some more results in there. However, I would pay a lot of attention to the methodological issues and caveats that are given. It's very hard to detect this kind of thing, and the possibility of confounding is very high.
Furthermore, I am not sure that these constitute "dramatic" change in allele frequency of the kind that you seem to be interested in. For example, they have this to say about genes associated with resistance to pandemics like the Black Death (one of the most clear cut examples of selection on differential fitness).

These results suggest the selection effects of historical pandemics at individual genomic loci are relatively modest, necessitating expansive sample sizes for detection.

They are statistically detectable, implying that they are strong from a microevolutionary point of view, but it is not at all clear that they are significant from a historical point of view, or that they lead to meaningful group distinctions.
Looking at your interest in trying to "date" a genome like with carbon dating, that sounds difficult. A more answerable question might be, if you got very lucky and found an example of a distinct population group that died out, you might be able to put bounds on that person based on the existence of that group. But that's probably on the thousands of years scale (more) rather than hundreds.
In summary: recent evolution in humans is the exception and not the rule. Most of the important stuff happened prehistorically (like, out-of-Africa and before).

Thank you so much for taking the time to research and write a solid answer to my question.
I try hard to make my stories as scientifically accurate as possible.  It's a challenge sometime, as I have only basic college-level (physics, mostly) background in the sciences.


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Can DNA reveal an approximate chronological generation of a person?
Given complete nuclear and mitochondrial DNA from an individual, would it be possible to approximate in what generation they were born?
For example, imagine DNA from someone who lived circa 200 to 700 years ago somewhere in Alsace (modern France).  Could we narrow that range to say they were likely born between 400 and 500 years ago using DNA evidence?
Imagine this is an expense is no object project with a 1 to 3 year period starting in 2024 for doing the work.

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It is possible w/ population history and local admixture patterns or new haplotypes appearing in addition to mutations, but is not a defined or accepted technique and likely to be inaccurate.

Briefly, no.
There may be an argument for using similarity to previously observed archaic hominin genomes that you could impute the age by similarity to e.g. Neanderthals. But those would be very old samples.
There's maybe an argument that you could look at the level of admixture in the sample and try to guess where it happened in the human population expansion, or by relating it to pedigrees. My gut sense is that it would be very difficult to differentiate it from currently existing humans.
That hasn't stopped people from trying; I haven't read the relevant paper in detail but it seems to only work in judging relative dates of somewhat closely related people, e.g. "this skeleton probably isn't the same date as this other skeleton found in the same location". It isn't an absolute measure of date, and it requires reference to some other sample. The authors themselves say it isn't a replacement for radiocarbon dating.
But in practice no one bothers, given that ascertainment of ancient hominins is so low. It is much simpler to just radiocarbon date the same sample that you sequence, even if you are already sequencing. Radiocarbon dating seems to have pretty good resolution in that age range, so you might as well just do that.
Methylated cytosines are approximately as stable and unmethylated cytosines in ancient DNA, so they aren't a good measure.


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Deviation from Hardy Weinberg equilibrium (HWE)
I have a set of 10 genes, each gene contains around 15 SNPs. I have tested the deviation from Hardy Weinberg equilibrium (HWE) for all SNPs. All of the SNPs didn't deviate from HWE, except 5 SNPs in ABCG8, they had p<0.05. I have found different factors that can affect the HWE. My question: Why for the rest of the SNPs met the HWE, why only 5 SNPs in ABCG8 deviate largely from HWE?

Be careful with the claims based on p-values: a p-value allows to reject the null hypothesis, but not to confitm it. In other words, when $p<0.05$ one can say that HWE is broken, however $p>0.05$ does not mean that HWE holds - it could be still broken, but we cannot prove it.

I would quibble with @Vadim a bit- when you do 150 tests, you expect that $0.05 * 150 = 7.5$ SNPs to be $p < 0.05$, as under the null hypothesis, p-values from independent tests are uniformly distributed. P-values are a quite low standard of evidence except when they're very small. Having all 5 occur in one gene might be surprising, but nonetheless I would say that "rejecting" the null hypothesis based on it is a bit strong, as the observations are still pretty consistent with the null hypothesis (without seeing additional data).

@MaximilianPress I was talking about more basic difference about p-value and power, objecting the claim that the rest of the SNPs met the HWE. However I do agree with you that, given the large number of tests, even the five SNPs with $p<0.05$ are not a proof that HWE is violated.

@Vadim ok, that makes sense. I agree with your overall point about p-values used to confirm the null hypothesis, I think I was just worried about the other side of the issue, which wasn't clear. I think that you actually addressed the question quite directly and accurately! I would suggest making it into an answer, actually.

A significance test (i.e., p-value) allows us to reject the null hypothesis, but not to confirm it. In other words, when $p<0.05$ one can say that the conditions for HWE are not met, however $p>0.05$ does not mean that HWE holds - the conditions might still not be met, but we cannot prove it. Thus, by making statements such as All of the SNPs didn't deviate from HWE and the rest of the SNPs met the HWE one may be committing Type II statistical error.
As @MaximilianPress correctly pointed out, in this case one should not be making any statements about the SNPs with $p<0.05$ either, due to the high possibility of False discovery. Indeed, a p-value gives us the probability that the data would be at least as extreme as was observed, even though the null hypothesis is true. Given $N=150$ SNPs one expects about $$N\times\alpha = 150 \times 0.05= 7.5$$ statistically significant outcomes to occur by chance, even if the HWE holds. That is, one could be also committing the Type I error of rejecting the null hypothesis, when it is true. To exclude this error one needs to use a more stringent test, e.g., Bonferroni correction, i.e., testing against the significance level $N$ times smaller than the current $\alpha=0.05$:
$$
\alpha\longrightarrow\frac{\alpha}{N}=\frac{0.05}{150}\approx 0.0003
$$


text:
Alternative to discontinued 10X Genomics Chromium linked-reads sequencing platform?
Any comparable alternatives to linked read sequencing?
I read that 10X Genomics discontinued its linked-reads technologies:

Discontinuation of Linked-Reads
At 10x Genomics, we are committed to enabling your research and providing you with >the best experience to make scientific discoveries. Earlier this year, as a part of >our product life cycle management, we notified you about refinements to some of our >on-market product offerings.
As of June 30, 2020, we discontinued the sale of our Chromium Genome and Exome >product lines. We will not be releasing new versions of these products.
We thank you for your partnership and are committed to providing the highest-quality >technical and software support.
Please contact your Sales Executive, Field Application Scientist, or email our >Support Team at support@10xgenomics.com with any questions.

See here on the 10X Genomics homepage.
This source assumes the product was discontinued due to...

infringed patents held by Bio-Rad Laboratories...

But when searching for "linked-reads" and "Bio-Rad" I cannot find anything. Having worked with ddPCR I assume 10X Genomics problem was the oil emulsion but not the linked-reads approach?
Is there an alternative platform doing what 10X Genomics linked-reads was able to accomplish? I am specifically interested in its role in genome assembly/scaffolding as in the Vertebrate Genome Project (in bold):

The current pipeline (Figure 1) to meet the 3.4.2.QV40 phased metric with the fewest errors currently achievable consists of a combination of the following approaches:

60X PacBio long-reads for an initial phased contig assembly (30X/haplotype);

2. 68X coverage of 10X Genomics-linked reads for intermediate-range scaffolding and further phasing (34X/haplotype);

80X Bionano optical maps to correct scaffolding errors and for further scaffolding;

68X Hi-C linked reads for long-range scaffolding;
PacBio Jelly algorithm to fill gaps using long-reads;

10X Genomics Illumina short-reads for base-call accuracy polishing; and

Thanks for your insights!

What is the specific goal/need that you have that linked-reads satisfied? Is it related to your previous question here? https://biology.stackexchange.com/questions/101425/is-mpseq-more-cost-effective-than-fish-when-looking-for-chromosomal-abberations

This is a question about bioinformatics software, so you will have a better chance of getting an answer on SE Bioinformatics.

@MaximilianPress no I have been reading about the pipeline of the Vertebrate Genome Project. They have been using 10X genomics' linked-reads. I wonder how  the pipeline might change in the future. https://www.rockefeller.edu/research/vertebrate-genomes-project/technology-pipeline-and-policies/
I thought someone might know. Or immediately point out a reason why it might have become obsolete given other technologies on the market.

First, I was just expressing an opinion on the SE group most likely to provide an answer to a valid question. I may or may not be right. As far as ddPCR my guess is that this list might be better as it’s not particularly Bioinformatics, but you’d be expected to research the topic first. Don’t worry too much, if people think you have posted to the wrong list the Moderators can move or help you move your question.

Maximillian provided some good information, but I thought I'd add some practical considerations:

All of the long-read sequencing platforms provide quite contiguous assemblies (scaffold N50s up to 40Mb or so), but those are definitely not chromosome-scale. No matter which platform you use, you will still need a "bigger-scale" method to get to the end. This can be Hi-C, strand-seq or optical mapping.

PacBio Hifi reads are great in that they are high-accuracy and allow high contiguity, but, at least in my experience, you will pay 2-4 times as much to get a comparable result to what you could get with 10X linked reads.

Nanopore can give you a very contiguous assembly with 20-30X coverage (ie about a PromethION flowcell's worth, roughly equivalent in cost to a 10X run on Illumina), but as Maximillian noted, requires polishing with short reads to get the base error corrected. (Note: Nanopore's new Q20+ sequencing is looking promising for this, but costs about twice as much per Gb. It's still cheaper than PacBio though.)

So with that said, it's extremely tragic that the cheapest method for making a high-accuracy, one-step-from-chromosome-scale assembly was killed as collateral damage in a patent war.

And then to answer your question:

MGI have been prototyping a method called stLFR that was supposed to be a replacement, but that seems to still be a work in progress, and also may have been shelved during the pandemic as they shifted towards supporting COVID sequencing.
TellSeq is another possible option, although I can't comment yet on how well it works in practice.

Update:
Answer was transferred to Kieran O'Neill's response, which I think is appropriate. I am not completely convinced that these technologies are necessary, but I am also not as familiar with them.
Original answer
The VGP used 10X linked-reads for 2 purposes, as I have updated your question to reflect:

intermediate-range scaffolding
further phasing

I would argue that both of these purposes have been to some extent rendered unnecessary by advances in technology. VGP protocols seem to be frozen at this point to ensure comparability I guess, but they are now somewhat outdated. For example, the VGP also calls for PacBio CLR technology, which is at this point not preferred for genome assembly. Instead, most would use PacBio HiFi reads, for which an extremely strong suite of tools such as HiFiAsm already exists. HiFiAsm and HiFi tools in general do an excellent job of phasing.
The other component is Hi-C-seq, which provides both intermediate-range and long-range scaffolding information to generate chromosome-scale scaffolds from contigs. The only reason to use intermediate range scaffolding is to simplify the Hi-C scaffolding problem, which is unnecessary with a higher quality of assembly such as that provided by HiFi. Hi-C can also be used for long-range phasing, and is at this point built into HiFiAsm, uniting the two data types.
A similar argument can be made that BioNano is no longer necessary.
In fact, HiFiAsm can use only these two data types (HiFi and Hi-C) in concert to create effectively chromosome-scale diploid human genome assemblies. In my experience it still requires some manual curation to get to full chromosome scale, but the results are still very impressive.
I would say that, at this point, steps 2, 3, and possibly 5 of the VGP are no longer necessary, and step 1 should be replaced by HiFi.
A somewhat lower-cost workflow might be as follows:

60X Nanopore long read sequencing and assembly. For a recent tool see here.
polishing nanopore assembly with nanopore reads using e.g. medaka, possibly diploid phasing.
100X Hi-C sequencing for scaffolding assembly using Salsa2 or RagTag.
Possibly polishing with high-coverage Illumina shotgun.

Additionally, there are a number of service providers such as DNANexus, Phase Genomics, DoveTail Genomics, etc. that will do some or all of these steps for a price. While I was an employee at Phase Genomics, I contributed to a guide for performing genome assemblies that is now almost as outdated as VGP but may still have useful information.
Full disclosure: former employee of Phase Genomics, hold some equity in the company still.


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How are gene and DNA linked in this file?
So I am preparing some documents about Sar-Cov2 and I wanted to illustrate the "definition" of the virus with the DNA sequence. I found this sequence in GenBank.
Now I think I need  an explanation about what this sequence described exatly. From y research, I understood:

The first part of the text listed genes that had been identified in Sar-Cov2. Each gene would be responsible of a "feature" of the Sars-Cov2.
The second part, named "Origin" in the text, is the complete DNA sequence of the virus.

Now my question is: how is one gene from the first linked to the second part? Is there some sort of "stamp" that allows to say: "gene X starts at fifth letter in DNA sequence and ends at tenth"?
If yes, is the information in the text from GenBank (link above)?
Example of the pieces of the file:
First part
FEATURES             Location/Qualifiers
     source          1..29903
                     /organism="Severe acute respiratory syndrome coronavirus
                     2"
                     /mol_type="genomic RNA"
                     /isolate="Wuhan-Hu-1"
                     /host="Homo sapiens"
                     /db_xref="taxon:2697049"
                     /country="China"
                     /collection_date="Dec-2019"
     5'UTR           1..265
     gene            266..21555

[...]

Second part
ORIGIN
        1 attaaaggtt tataccttcc caggtaacaa accaaccaac tttcgatctc ttgtagatct
       61 gttctctaaa cgaactttaa aatctgtgtg gctgtcactc ggctgcatgc ttagtgcact
      121 cacgcagtat aattaataac taattactgt cgttgacagg acacgagtaa ctcgtctatc
      181 ttctgcaggc tgcttacggt ttcgtccgtg ttgcagccga tcatcagcac atctaggttt
      241 cgtccgggtg tgaccgaaag gtaagatgga gagccttgtc cctggtttca acgagaaaac
      301 acacgtccaa ctcagtttgc ctgttttaca ggttcgcgac gtgctcgtac gtggctttgg
[...]

Your question is, in my opinion, unsuited for this site for several reasons. First it is not self-contained — it requires one to read a document on an external site to understand. Second it is not sufficiently focused — it is, in effect asking for a complete explanation of a datafile format. Third, one could argue that this is not a problem in biology per se (although this is more debatable than my other objections). However, fourth, and most important, you show no evidence of trying to solve the problem yourself by using the extensive documentation on the NCBI website.

For the first reason: the linked document is a dna sequence. I cannot "say it in my own words" and reproducing it in the post is useless and decrease readability. Second: no, it is focused on the link between gene and DNA sequence. Third: yes, this could be an objection. Fourth: I went into some of this documentation, not all. I did not find something that lighten me about how gene are linked to dna sequence

I think it is not unreasonable to expect you to reproduce examples of the file in question. You obviously have identified the different pieces of the structure of the GenBank file. It would be simple enough to place that at the bottom of your question, if you don't like having it in the middle of the question.

In a GenBank format file, the first line of each feature entry gives the location of the feature in the sequence.  Consider, for example, the second feature in the linked file:

 5'UTR           1..265

This means that the 5' untranslated region of the viral genome is from bases 1 through 265.
Bases start counting with 1, so down below in the sequence after the ORIGIN, marker, this corresponds with the bases:
        1 attaaaggtt tataccttcc caggtaacaa accaaccaac tttcgatctc ttgtagatct
       61 gttctctaaa cgaactttaa aatctgtgtg gctgtcactc ggctgcatgc ttagtgcact
      121 cacgcagtat aattaataac taattactgt cgttgacagg acacgagtaa ctcgtctatc
      181 ttctgcaggc tgcttacggt ttcgtccgtg ttgcagccga tcatcagcac atctaggttt
      241 cgtccgggtg tgaccgaaag gtaag

Note also that "feature" means more than just genes, as in the case of this 5'UTR. Any identifiable region of interest in a sequence may be marked with a feature. Take a browse through the Sequence Ontology to see what other sorts of things are likely to be marked as features.

Thank you for your answer
How strong is the association made between the region of interest and the feature ? Is it made with a test (like supprres the zone and see how the feature evolved?)

@totalMongot You'll have to look at the article that produced the sequence to see how they identified features. Most of these features come from sequence analysis.


text:
renal portal system
I am a high school student.
In my textbook it is written that Mammals don't have renal portal system because they have four chambered heart. I understand that the four chambered heart keeps the oxygenated and deoxygenated blood separate but what does it have anything to do with renal portal system?

Excellent question!  The book is right, but ... well, biology has a hard time with the word because.  We read causes and effects into evolutionary designs to make them easier to remember, much as the ancients linked everything to the four humours.  There is an answer to because, somewhere in a very long period of geological history, but take all assertions (even mine!) with a grain of salt.  I will, of course, use this word quite often below, with great and misplaced confidence.
I would disagree with the statement that we don't need a renal portal system.  There are unfortunately many cases of acute kidney injury that result from something as straightforward as blood loss.  The kidney needs to shut down the blood flowing in (constrict the afferent arterioles) because filtrate production would further reduce the volume of the blood.  However, the kidney itself depends on the blood coming out (efferent arteriole; peritubular capillaries; vasa recta) to sustain its own needs.  It often dies in the attempt, and without kidney dialysis or a lucky transplant, so would the person.  The renal portal system would have prevented that by allowing blood to reach the peritubular capillaries (and presumably the vasa recta once it evolved) by non-leaky vessels.  The portal system means that blood is at less-than-arterial pressure, comparable to blood after it has been put through the glomeruli.  The system is cunningly wrought so that valves in the veins can force blood into the portal system only when the normal arterial input is reduced: (Akester, 1967)

How did we lose the system?  I have no idea.  Sometimes we lose things like the enzyme to make vitamin C apparently by pure chance - at some point in time, our ancestors didn't need it for a while, and key genetic information was corrupted because natural selection didn't fix it.  Other times, there is a small overhead in metabolic cost or the potential for other disease.  Maybe the excellent adaptations of mammals for water recovery with a long loop of Henle were incompatible with the structure.
What is clear is that we can do without it, normally, unlike many other species that frequently shut down some or all of their glomeruli by constricting afferent arterioles.  This is related to the heart, since the blood goes directly from aorta to renal artery instead of passing through the capillaries of the lung first, and therefore has a consistent high pressure (Keogh, 2021).  But it is also related to the issue of why we aren't shutting down urine production.  Mammals have a consistently high metabolism, requiring constant internal heating.  This goes beyond mere sugar consumption - our active bodies are continually generating urea, uric acid, creatinine, and other wastes that contain nitrogen that need to be flushed out of the blood.  So we pretty much do want to keep the glomeruli going all the time, and we have a powerful four-chambered heart that does that effectively while powering the rest of our lifestyle.  By contrast, reptiles are known for sudden bursts of action and long periods of inactivity.  Their cold-blooded physiology has much less need for food, and I think they don't need to excrete waste so consistently.
I should apologize for not keeping the answer simple, but understand this is a question that would stump good college anatomy and physiology students!  I've started linking some terms above - feel free to ask for clarifications on anything.

Sorry,but I am a high school student ,and only understood only half of what u have written, can u please give me a possible satisfactory explanation of how Four chambered heart helps us in this? It is due to four chambered heart that's for sure because in every biology textbook this is the reason given but I don't know why? My guess is that hear keeps deoxygenated are oxygenated blood separate and oxygenated blood also has less impurities than the deoxygenated, thereby in mammals comparatively less impure blood is supplied to tissues, whereas in fishes and amphibians mixed blood is supplied

In a 2-chambered heart, blood starts off going through the gills.  After that, the blood moves through the rest of the body.  So the pressure of the blood is LOWER when it enters the kidneys to be filtered than it would be if the heart were 4-chambered with the same pressure inside.  So when the organism is trying to increase its blood pressure, it will want to constrict the blood flow being filtered in the kidney, but that would drop the blood pressure moving the blood through the kidney tissue even more than it does in mammals.  Unless there is a backup plan.

Wastes in the mixed blood are not likely to be the explanation.  Though it is not the best thing for them, people on dialysis can go days between sessions.  So the body doesn't need to remove this waste right as it is formed before it mixes with other blood.

but even in the organisms having three chambered heart like amphibians renal portal system is present and well developed? why? they too can provide direct blood to kidneys as 4 chambered heart is not necessary for this


text:
Is it rare for a virus to be similar to its host?
Halomonas bacteriophage vB_HmeY_H4907 discovered recently in the Mariana trench is apparently highly homologous to its host:

To our best knowledge, it is the deepest isolated siphovirus from the ocean. Its 40,452 bp linear dsDNA genome has 57.64% GC content and 55 open reading frames, and it is highly homologous to its host.

Naïvely, this seems to suggests to be an evidence in favor of a particular hypothesis of the origin of viruses - as runaway genetic elements.
Questions:

Is it common for a viral genome to be homologous to its host (or another cellular organism)?
Is such homology an evidence in favor of a particular hypothesis of the origin of viruses - as runaway genetic elements?

References:
Identification and genomic analysis of temperate Halomonas bacteriophage vB_HmeY_H4907 from the surface sediment of the Mariana Trench at a depth of 8,900 m
For laymen interpretation see, e.g., Virus from depths of the Mariana Trench uncovered or Deepest Virus Ever Detected Unearthed by Scientists in the Mariana Trench

Funny to see something "unearthed" in the ocean... Language can be wonderful at times.

Please read the paper and find out in what specific way the authors of the paper think the virus is “homologous” to the host (and indeed tell us what the host is). Then modify your question accordingly. By itself the word “homologous” is unclear.

@David I think the second sentence in the quote, where the first half of the sentence is talking about the genome, indicates sequence homology. I'm not sure any other homology would make sense in context. Sure, I think this could be clarified, but the authors are making a relatively qualitative statement here anyways.

@BryanKrause DNA, RNA, encoded protein? For how many genes? The question does not say and is therefore unclear. I have asked the poster to clarify and think that perfectly reasonable. Too many questions (and answers) are of little use here because their subject is not clearly defined. This can be because the poster assumes everyone knows what he is talking about, but often is because he is not clear about the problem. In this case going back to sort this out is an important learning experience. If the poster is not prepared to engage with us we should not engage with him.

Phage similarity to host
Briefly, yes. Generally speaking phage have relatively similar compositional features (e.g. k-mer frequency, see linked paper) and also direct homology (large regions of sequence similarity) to their host genome, compared to a null model. Methods leveraging this have been used (with some success) to predict hosts.
Support for hypotheses of origin
I think that there are multiple possible mechanisms for how similarity arises:

Gene conversion between phage-host
accidental packaging of host genome
phage integration as endovirus (possibly as a "dead" copy) and neofunctionalization.

I think that these mechanisms don't directly contradict your hypothesis. In fact, the presence of auxiliary metabolic genes in phage might argue in favor of it.
But at the end of the day, the phage and the host are just swapping DNA at a nonzero rate, and you'd expect them to be more similar than otherwise.

“Compositional features”? The question is not clear on what it means by homology, and your answer does not say what compositional features you are talking about. Size? An RNA genome surrounded by a protein coat? A genome that does not encode ribosomal components?

@David "composition" is usually used to refer to nucleotide, GC, or k-mer frequencies in the context of DNA sequences. "Homology" is generally understood in genomics to refer to regions of sequence similarity. (As described in the linked paper.) I have updated the answer to reflect this.

As I implied in the original version of my request to the OP, to the man with a hammer, everything looks like a nail. Composition means composition. In the context of a genome it could mean the number and functional distribution of genes, whether they were ss, ds, RNA or DNA, their base composition etc. And your answer ignores the vast differences between viruses in the same organism: simple RNA phages like Qbeta, v. large T5. Even human herpes viruses show vastly different GC contents, and, of course they are quite different from polio and from SV40.

@David certainly- variation exists. The question, as I understand it, is whether that variation shows any association to the host, or whether it is agnostic to the host. If you are frustrated with genomics terminology at large, and the discipline's attempt to reduce everything to sequence data, then I will happily agree that it is annoying.

I am not frustrated with genomics terminology, I resent imprecise terminology and lack of context that make statements ambiguous and scientific discussion pointless. I know about viruses, I know about relationships between viruses and hosts, viruses and viruses and genes within viruses. I have now read the paper, and have, I think, managed to fathom what the authors mean in their sloppy usage of the term "homologous". As their first language is not English they are only partly to blame for this — the journal editors should take their share. If I chose to answer this question I will explain.


text:
Why are people unable to develop lasting immunity against Norovirus?
Infection with many viruses will result in decades-long if not lifetime immunity, for example chicken pox.
Because of the large number of viruses responsible for the common cold, lifelong immunity to the common cold isn't possible; we will still be susceptible to those we haven't encountered before. But we do develop immunity to those we've been exposed to.
Influenza virus mutates quickly enough that there will nearly always be strains which will be new to us. Immunity to HIV is difficult because of the particular cells the virus attacks (as well as mutations).
However, Norovirus, while there are many strains, is particularly problematic because immunity to a strain lasts less than a year, making reinfection with the same strain repeatedly possible.
Is the reason Norovirus infection doesn't confer longer-lasting immunity known?

Your first question! :) interesting read +1, but I'm not able to answer it I'm afraid. And congratz with your moderatorship @Parents!

@AliceD - I was researching this, and thought, "Wait! I know where I can ask!" ')  Also, thanks. It's a great site.

I cannot answer this with anything but a guess, still: Some norovirus immunity seems to be caused by non-functional fucosyltransferase, which is (to keep it short) related to the ABH antigen. (see http://en.wikipedia.org/wiki/Norovirus) If (something similar to) a normal human antigen can confer immunity to norovirus, maybe the "real" norovirus antigen is so close to autoimmunity that our immune system filters a lot of them out.

I am soooo going you have to come back to this when I have time. I'm currently struggling with the scope I want to include (mucosal immunity, IgG vs IgM, HRV serotypes, noro infectious dose...). If I want to dive deep into the biology here would it be better to repost the question there Q/A style, or can juat do it here?

So the direct mechanisms of norovirus immunity aren't well understood - the shortage of straightforward animal models, and the extreme difficulty in culturing the virus make mechanistic studies really rather hard.
A modeling study pegged the duration of immunity at 4.1 to 8.7 years when talking about symptomatic disease, which is much higher than many of the estimate's you've encountered, and isn't actually all that fast. The older estimates were built off human challenge studies, which involved massive doses of norovirus, rather than the normal exposure you'd have. Now that is for a single strain, but immune cross-reactivity for strains isn't well understood. There's also several efforts to develop norovirus vaccines, which suggests some duration of immunity is at least possible. It's also possible that a proportion of the population (~20%) is simply immune due to the absence of the FUT2 gene, which acts as a receptor for the virus in the intestines.
In addition, there's definitely some antigenic drift, where mutations in the primary antigens negate previous immunity to the disease (the same mechanism that causes you to eventually lose immunity to a particular strain of influenza).
This paper is a pretty brief review of all these factors, written by some norovirus experts.

Thank you! I was getting some impressions in my reading last night and touched on some of the topics, for example lack of cell cultures that will grow the virus, something that seems to be getting better) but was getting nowhere fast with "why". Thanks for the link!!

+1 I should point out, that my comment that I want to come back with an answer to this question is not because of a disagreement with anything here. Having worked on a culturing system for norovirus, I just have a lot of opinions on the matter. There are no italics extreme enough to to be apllied to the extreme difficulty in culturing, but I really appreciate that note :)

@AtlLED Nothing bad ever happened by having a subject matter expert provide an answer with more detail :)


text:
Can anyone identify this “fruit”?
Just found this “fruit” in my driveway, probably dropped by a bird.  I opened to see what kind of seed was inside.  Does anyone know what this is and is it edible?
I live in the San Fernando Valley of Southern California, USA.

Could you include a photograph with something other than a coin in your local currency so those of us not familiar with US coins can get an idea of the size? Also, I take it all of those seeds came from that one fruit, right?

Unfortunately, I no longer have the fruit in order to take another picture.  It was about an inch long and had about a dozen seeds.


text:
Non Canonical Splicing and Alternative Splicing
I am struggling to understand concept of non-canonical splicing. I couldn't find satisfying answer to how non-canonical splicing work, what are the mechanism of it, how does it distinguish from canonical splicing? or is alternative splicing the same term for non-canonical splicing?

Can you post an example of how people are using the term that you find confusing? Different people might use it in different ways. Is this from a textbook? It would be helpful to get a link or some kind of citation. For example, this paper discusses it in detail. Is there something that you find confusing there?

Here's a recent review by Consuelo Pitolli  (I'm not specifically recommending it - it's just a recent relevant hit from PubMed, very readable)  Non-canonical splicing breaks rules, so you can do interesting things: make circular RNAs, do trans-splicing ("RNA-viral sex"), etc.  Alternate splicing just means there is some way to change what splice product is made.  Sorry I don't have time at the moment  to make a full answer.


text:
CTCs stability before and after cultivation, what to look for?
I want to cultivate CTCs filtered out from the patients with... say metastatic breast cancer. I found the papers where people use 3D scaffolds to cultivate MCF7 cells and after the cultivation cells show the increased invasiveness. So during the cultivation cells somehow change. Perhaps the surface markers are involved, perhaps something else.
When dealing with the patients I must be sure to get the same cells after cultivation that were in the patient, or very close to the initial ones. I want to build a model for cultivation: to use say the same MCF7 to develop the cultivating protocol and then to test it on real patients. It`s easy to build the input and output control on the MCF7, but what changes should I look for?

These kinds of questions confuse me. It's fine to ask, and we're happy to help if we can, please don't get me wrong. Unless research has changed drastically since I was doing it, you should be doing this in a lab where there should be written protocols for everything you do, like, everything. If you're in a relatively new field and you're developing a protocol, you should be consulting experts in the field. There are tons of literature on breast cancer research. What are your circumstances?

What are CTCs? Please do not use this type of abbreviation in the title of a question, and define it on first use in the main body.

Great question. CTC = circulating tumor cell. It's a biomarker for denoting metastatic cancer. There could be a cell biology expert in around.

@anongoodnurse, Im in "relatively new field" situation. Ive looked through quite a lot of papers on ... circulating tumor cells ... cultivation, the main idea of them is: "we`ve cultivated something, something has grown and we conduct our experiments on this "something"". We have markers and probably gene changes in cells that go to different fields of body. F.e. here (yup, the article is 10 y.o. bu still) they tried to grow breast-cancer-to-bone metastasis. 2 cycles of "metastasing" MCF7 into the bone and they have more aggressive cells...

And if we face the same case in the patient... Cells f.e. went from breast to bone, from bone to liver... And we have a coctail of different cancer cells in the blood... And how to look at them?

@Hedgehog - Sorry I can't help. It's been decades since I did research (carcinogenesis/skin cancer); if I were in your place, I'd be contacting the main author of that paper and asking questions, and to ask if they knew of others who were working with same. There is a site in this network, Academia.SE, which might be able to guide you better than this one. Have a look at the site to see if your question (about how to find/contact others in your field) would fit. I hope you get some good ideas sometime soon.


text:
Does it make sense to calculate the phospho protein (serine) : phospho protein (threonine) of the same protein in western blot?
I want to indirectly measure the nuclear accumulation of DARPP protein through phosphorylation. Their nuclear transport is controlled by phosphorylation of Threonine 34 and Serine 97 residues.
S97 is then dephosphorylated while T34 is phosphorylated as it moves into the nucleus.
So I want to determine cell localization indirectly through the ratio of Serine 97 and Threonine 34.
I know that the ratio of phosphoprotein to total protein is often estimated by immunoblot analysis. I have antibodies specific to each of these phospho-proteoforms of DARPP.
Does it make sense to calculate the phospho protein (serine residue) : phospho protein (threonine residue) of the same protein? Has anyone ever done this kind of analysis? I cannot find the reference anywhere. Thanks!

What exactly are you trying to find out? It makes no sense at all if you don't tell us.

Are the serines and threonines variants at the same position? are they at different positions? How are you imagining distinguishing between them? Do you have p-T and p-S specific antibodies?

@David I wanted to indirectly measure the nuclear accumulation of DARPP protein through phosphorylation. Their nuclear transport is controlled by phosphorylation of Threonine 34 and Serine 97 residues. When S97 is dephosphorylated and T34 is only phosphorylated, it moves into the nucleus. So I wanted to find out indirectly through the ratio of Serine 97 and Threonine 34.

@Maximilian Press They're at different position. One is T34, and the other is S97. And yes, I have specific phospho- antibodies for both.

editing your question to add this crucial information. Some of it doesn't make much sense to me so please edit my edits as needed.

@MaximilianPress Thanks for adding the information! Nobody knows the answer tho..

@oklu most people probably looked at it when first posted and evaluated it as too vague, and never clicked on it again. Someone likely knows the answer but you may need to wait until more people look at it. In the meantime you could try to do google searches on your own.


text:
Has artificial selection / selective breeding ever been targeted at intelligence?
Has it ever been attempted to breed a species, for example dogs, to maximize some aspect of intelligence? I find it particularly interesting to imagine one could selectively breed dogs to be able to talk or develop far more advanced communication structures.
Is there anything we know that could help us predict how many generations of breeding this might take?
Has this idea even been abstracted or generalized to a general technique of “targeted breeding”, where, in a highly methodical and systematic, scientific way, someone pinpoints some specific, even measurable trait, and then with as much automation as possible, tries to artificially breed many generations of an organism to continually maximize that trait, as fast as possible?

when you researced this what did you find?

I'll briefly note that when applied to a human context (which is the most common for this question), this kind of breeding is called "eugenics" and usually frowned upon. Assuming that you are interested in breeding dogs, I'll point you in the direction of the breeder's equation. But I'll also point you to the wiki page on IQ heritability (in humans), which has a number of important caveats. IDK if they apply equally to dogs.

Hi! Many dogs have been bred to be more alert and bark a lot rather than be bulkier and more aggressive, precisely using a systematic breeding method, maybe not as precise as scientific research. Would that count? This question is hard to answer without a common reference framework for intelligence.

@MaximilianPress - It must pertain to dogs as well. I bred Border Collies, and while I didn't employ a scientific method, some were more "intelligent" than others, and one of them was absolutely outstanding, demonstrating easy familiarity with syntax and concepts.

your last paragraph more or less exactly describes the process of artificial selection, which is being done all the time in agricultural - selecting animals or plants for increased crop yields, etc etc.

Has it ever been attempted to breed a species, for example dogs, to maximize some aspect of intelligence?

Yes, perhaps unintentionally, but certainly to serve a purpose: to better herd sheep.
Border Collies (BC) are widely considered to be among the most intelligent of dog breeds. For (at least) hundreds of years, they have been selectively bred to herd sheep, playing on a dog's natural prey drive.
By listening for and obeying different whistles/verbal commands for different actions, a working BC at minimum learns whether or not to approach the flock, find the sheep, gather them into a group, move them towards the shepherd or not, towards the right, towards the left, leave the flock to go back for strays, speed up, slow down, walk, stop, lie down, bark, and come back (you're done.) There are also other commands, but those few are the minimum. (I've seen a BC cut 3 sheep by name out of a flock of ?50-60 sheep and herd them into a separate enclosure. They were not marked.)
By selecting, for generations, the most responsive dogs to breed, BCs have become finely attuned to look to humans for commands of all kinds, and to obey them.
While they may not ever learn to talk (not only vocal cords, but the entire mouth including most of the tongue is needed for speech), they can still communicate. A BC named Chaser was trained to learn the names of over a thousand toys, many of them being similar (e.g. red ball, fuzzy ball, big elephant, little elephant, white elephant, etc.). The trainer, a psychologist, stopped at 1022 objects because of space and he was kind of bored at that point. The dog could find a photograph of the object by name, and could find on the first try without help an unknown toy in a group of familiar toys when the owner made up a new name, understanding the concept of unfamiliar. He taught her verbs, adverbs, adjectives, and syntax. She could follow a string of commands successfully.
Word buttons are now being used to study canine cognition, the most well known example of these dogs being Bunny the Sheepadoodle. This video explains Bunny's progress and ability to communicate using abstract ideas, like "ouch" and "stranger", and emotions. This is as close to talking as we are likely to get with dogs.
Sadly, Bunny often indicated that she was sad, very sad ("Big Sad") and worried ("Concerned") and has asked for medications for relief. Scout's honor. (How 'human' is that?) Personally, I can't understand someone doubting the capacity for intelligence (and emotions) in dogs after seeing these dogs in action.
If you can stand another anecdote, read on. Border Collies are "soft dogs", meaning they don't like hard corrections, e.g. they react negatively at being yelled at. My grandfather, a farmer who had a small herd of dairy cows, could tell his Border Collie which cows he wanted her to round up and bring in at milking time, and she would do so. In the summer, cows are milked twice a day. One summer evening she missed a cow, which is kind of a big deal, as missing even one milking can decrease milk production for days. Uncharacteristically annoyed, my grandfather angrily yelled at her to go back out and get said cow, which she did. She then disappeared for 24 hours, making my grandfather go fetch his own damned cows from the fields. Did she disappear because her feelings were hurt, or to teach him a lesson? I don't know, but my grandfather never yelled at her again.

The Wikipedia article on "Selective breeding", which I went to while composing a comment on your question, has this sentence:

A Soviet attempt to breed lab rats with higher intelligence led to cases of neurosis severe enough to make the animals incapable of any problem solving unless drugs like phenazepam were used.[14]

The reference given is in Russian (see here), but even with Google Translate it's hard to get much out of it.
A Google search on "'artificial selection' learning evolution" brings up a lot, but nothing that I can find specifically on selection for intelligence: there are experiments on artificial selection for brain size in guppies, reversal learning (cognitive flexibility) in birds (Parus major), learning rates in parasitic wasps ...


text:
Is there a DNA analogue to ribozymes?
If not, is it impossible for DNA to have enzymatic activity?

Not very common, and not found so far in nature, but they exist and are called deoxyribozymes.
Additional information:
Deoxyribozymes are the equivalent of ribozymes in the DNA world and can function as catalysts for different biochemical reactions, such as DNA cleavage. While DNAzymes (short name) were synthesized in a laboratory context (In-vitro) and proved to be active, no observations were made of DNA molecules having an enzymatic activity In-vivo which is of course not a definitive proof they don't exist in nature.
The latter point is a major difference compared to ribozymes which were proved to be active and functional in living cells.

How did I miss the analogous name? :P

@TheObscureQuestion Thought the same when I saw you question :D. By the way excellent question, I actually knew about them because I looked that up recently while asking myself the exact same question.

Can you at least give a short summary of the Wikipedia article? Dropping a link (and this is more or less it) is usually not appreciated.

@Karolas I thought about doing that but they are just exactly the same as ribozymes but DNA-based instead of RNA-based. Anyway you are right I will add some more information.


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How to predict the top microRNAs that bind to 3`UTR of a specific gene
I would like to know whether someone could tell me
1) some of the online programs that will predict the top microRNAs that bind to 3`UTR of a specific gene (for eg-GAPDH).
I am relatively new to the field.
thanks

You'll find TargetScan quite useful. You can pick from a number of species and simply enter a gene name. It will then get the 3' UTR, show its conservation across species (binding sites in a conserved area are more likely to be active), find miRNAs that can bind to the 3'UTR, and then split those up by whether they bind to highly or poorly conserved regions. It also categorizes the hits according to various properties, like proximity to the open reading frame, in case you want to perform any sort of filtering by that.
Edit: BTW, you may want to take the target miRNAs and look at their expression in your tissue of interest. That's a good way to further narrow down their relevance.


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Why do we feel headache after ice cold drink?
Whenever we drink ice cold drinks, after a couple of seconds, suddenly we feel intense pain rising from upper nose to forehead. What is reason behind this pain after too cold drink and why it's felt in that particular part of head?

The cold drink causes the blood in blood vessels of gums to freeze. This stimulates nerve endings and causes a sensation in brain.


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Why do some armadillos roll into a ball while others don't?
My question is pretty simple that why do some species of armadillos (like three-banded) roll into a ball, while others don't?

Para 4

@Tytoalba Thanks! and Yes I know that, perhaps I haven't been able to word my question properly. I am more interested to know about why they were evolved this way (to have less plates, etc.)


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Edema and hydrostatic pressure
I'm currently studying Robbins basic pathology, and I'm confused about a specific statement:
It states in the book that when hydrostatic pressure is low due to a lack of albumin synthesis, it leads to a net movement of fluid into the interstitial spaces.
However to me that doesn't make any sense: isn't the hydrostatic pressure the thing that facilitates the net movement of particles across the capillaries, so if there is a lack of it, shouldn't the particles not move across the membrane?
Any help appreciated

Citing the edition, chapter and page number, plus the context in which the statement is made in the book, would be helpful.

it was chapter 4, page 80-81

You might consider cross-posting this to StackExchange Medical Sciences, as human physiology is more of a focus there.


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Heart rate sensor electrodes to be used for blink detection (simple EOG)
I have a cheap ECG/HRV/heart rate sensor module for Arduino, bought from Aliexpress. It has 3 electrodes: RA, LA, RL, which I guess mean right arm, left arm, right leg. The module outputs one single time series data, like one referenced electrode of an EEG.
I would like to use this cheap module to record blinks (one coarse channel is enough for this), with its electrodes placed around one eye. But I am stuck with this problem:
What I have found so far on the internet about EOG electrode placement says that the ground electrode should be placed somewhere on the forehead or near the ear/neck. But which of these 3 electrodes should be deemed as ground? What arrangement of the 3 electrodes would you advise? (What are the equivalents of these 3 electrodes in a commercial EOG sensor?)
I appreciate any helpful comment. Thanks in advance!

Can you share any documentation for your sensor, manuals or instructions? Why doesn't that manual answer your questions?

I agree with @MaximilianPress -- It depends on how the device is wired, whether there is a fixed ground electrode etc.

As it is the case of almost all Aliexpress-marketed no-brand Arduino modules, there is zero documentation, except how to wire it up with Arduino. And no matter how much I googled, all applications only involve HRV/ECG sensor wiring of the module, even though is could be capable of acting as an EOG sensor I think.

Despite initial difficulties, after inspecting the module really closely, I could successfully identify the (only) microchip on it, which is an Analog Devices IC, with all necessary functionality embedded in one piece. Name is AD8232:
https://www.analog.com/media/en/technical-documentation/data-sheets/ad8232.pdf
It seems that pin 4 and 5 are connected with a resistor. Pin 5 goes to "RL" pin of the module board. Pin 3 goes to "RA", which at the same time IC docs refer to as IN-, and pin 2 goes to "LA" which the docs refer to as IN+. So I suppose the mystery is solved, but I can not try and check the setup in action in near future
(So far no matter in what configuration I wired up the module with my eyes, it could not work, but emitted a rhythmic noise. That is why I asked the question originally. But I found out that the source was the problematic power supply, not the electrode arrangements I tried. Now I need to get a proper power supply to try the module then.)


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How do genetic variants of a gene moderate an association, leading to counterintuitive result?
I have run moderation analysis to study the interaction between SNP (dominant model) of a gene (A) with DNA methylation on cognition measures. For this, I have calculated the mean DNA methylation levels at 3 gene loci: Upstream of TSS (transcriptional start site), promoter, and gene body.
The protein from this gene improves cognition so we expect that increasing serum protein leads to better cognition outcomes. Prior research suggests that methylation in the gene body generally results in increased gene expression.
The results of the analysis showed that: in the carriers of genotype TT, increasing methylation rate in the gene body results in less recognition ability while in the holders of the C allele, higher methylation in the gene body is associated with improved recognition (2 different directions).
I know that different alleles can impact methylation rate but here the direction is completely opposite. How can I interpret the result?

The addition of a methyl group (groups) can activate or repress genes, do you have a reference that the expected result in this case is activation?

No, I do not have any data about the gene expression or serum protein level.

Can you refer to any data in general talking about gene body methylation's effect on gene expression? My feeling was that the effect was somewhat ambiguous and context dependent, but I'm not an expert. Especially in the absence of actual gene expression level for your gene of interest, it's hard to draw conclusions. Additionally, "the protein from this gene improves cognition..." sounds like a pretty squishy result. Cognition is a hard phenotype to measure usually. How large are the effects +/- error of the protein on cognition? What are the effect sizes / errors of alleles on cognition?

Thank you all. Why do you judge me? I am talking about the data I have obtained by analysis. They are not my personal idea! Still, I have not shown them to my supervisors. I wanted to improve my information before that meeting.

Fair enough. I apologise. We worry about presenting information to people that might harm anyone, it is a concern on many sites on the network. For example [worldbuilding.se] often gets questions about making explosives or poisons which creates concern. I'll delete those comments.


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Biology (DNA electrophoresis with agarose)X
I'm researching a polymorphism using restriction fragment length polymorphism (RFLP) and gel electrophoresis.
After RFLP, I should see fragments at 141bp and 111bp, but I can not see in 2% agarose, what is your suggestion, dear friends?
Here is my gel recipe:

TBE: 0.5% 75ml
agarose: 1.5gr
Voltage: 65
Time: 1 hour

Unfortunately, I do not have the ability to change the methods.

Why can't you change the conditions? Anyways, fragments of 141 and 111 bp should separate in 1,5% agarose. Can you show an image of your gel?`

How are you visualizing the bands? Ethidium bromide or SYBR green or something? What does "1hr" mean, did you run it for 1 hour? Did you load a ladder to confirm that the band didn't run off.

When you say you "can not see" the bands, do you mean they are totally absent? Or is only one band seen?


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Why is tautonymy forbidden in botany?
From Merriam Webster:

tautonym: a taxonomic binomial in which the generic name and specific epithet are alike and which is common in zoology especially to designate a typical form but is forbidden to botany under the International Code of Botanical Nomenclature

(My emphasis)
From basic research on Google I couldn't find a reason as to why this is.
Q: Why is tautonymy permitted in zoological nomenclature but forbidden in botanical nomenclature?

this should be helpful. https://www.jstor.org/stable/1219048

Thanks John, I will look through this. How do you know this is the meeting where they set this rule?

I'm not 100% sure I searched tautonym in the taxon database and it was the latest article discussing them, this one may be more relevent https://onlinelibrary.wiley.com/doi/10.2307/1218008 and Volume19, Issue5 October 1970 Pages 787-788 may be even more relevent but I don't have access to a hard copy. thats why I dropped it as a comment not a na answer.

Short of someone from either one of those committees being on here and seeing the question the answer is "Because we said so".
The respective committees are the authorities on the subject and get to make their own rules, even if they aren't consistent with committees covering similar aspects in other areas of biology.
For those interested, the committees have their rules available online and the ones relevant to the question are 32.1c and 23.4 from the International Code of Nomenclature for Algae, Fungi, and Plants and 23.3.7 (under Chapter 6, Article 23, section 23.3; no direct link possible) for the International Commission on Zoological Nomenclature.

I suspect they said so because they're trying to avoid the way that tautonyms are used to identify the "prototypical" species of a genus, given that there isn't really a fair way to choose a prototype so it'll always be based on some human bias. But I agree it is likely difficult to find any source that explains this rather than just stating the rule.

@BryanKrause quite likely true. In botany at least, type is often indicated by "vulgaris" meaning "the common one" rather than a tautonym, and vulgaris was used in several examples, but I didn't put 2 and 2 together. However, usually a type is the first identified species, which I guess is typically the most common one, but not always, so vulgaris might not be appropriate in all cases

Thanks bob and Bryan, this is helpful. Do the taxonomy commission meetings have public transcripts?

@imrobert No idea; you could try searching their websites.


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Interpretation of narrow-sense heritability over one (using R/S = h^2)
Here is my data:
Mean height score of the total parental population: 5.2
Mean height score of selected parents (those chosen for breeding due to their higher height): 6.4
Mean height score of the offspring (resulting from the selected parents): 7.8
Selection differential (S) for height:
S = Mean height score of selected parents - Mean height score of the total parental population
S = 6.4 - 5.2 = 1.2
Response to selection (R):
R = 7.8 - 5.2 = 2.6
h^2 = R / S
h^2 = 2.6 / 1.2 = 2.167
I confirmed that the data are not wrong. What is the interpretation of this h^2 value?
I am looking for explanations for why h^2 would be above one.

Simpler explanations: 1) noisy phenotyping. 2) (my bet) environmental effects in the selected population, unless you somehow can exclude this via a common garden with parents and offspring raised side by side at the same time under the same conditions. Overall this smells like an experiment where something went wrong. "Confirmed the data are not wrong" not sure what this means, you mean you went back and recalculated?

Check your assumptions.
It looks like you're using a formula that assumes linear, additive variance. Your data suggest a system that does not follow these assumptions, so the equations are not valid for the situation you describe.
See also https://en.m.wikipedia.org/wiki/All_models_are_wrong - much of the difficulty in applying simple mathematical formulae to real world data is in deciding/recognizing when your model is sufficiently wrong that it's no longer useful.

What could explain that? Dominance? I thought of that, but I assumed that would decrease h^2, not increase it

@BigMistake I would start by assuming the trait is simply nonlinear. No reason that things have to be linear in the real world just because it's sometimes convenient to model.

Another possibility would be non-independence of the environment. If there's a systematic environment change that's going to affect population parameters outside of the heritability.

Actually, I think dominance could elevate h^2 above 1. The regressed slope would be above 1

I know these things can bias h^2 estimates, but I'm specifically wondering about what can result in them being above one, not just generally inaccurate

Both reasons I suggested could have this result. It's not that surprising: when assumptions are violated an equation is no longer valid.

How would they increase h^2?

They don't, they make the equation you are using to calculate it invalid.

Exactly. I am looking for why h^2 is calculated to be too high, not why the equation is invalid

Because you're using an invalid equation to calculate it because the assumptions that make the equation hold are not met - that's why it's too high. R is a bigger response than is possible under the model that makes that equation hold.

Assumptions not being met ≠ reason why a particular direction of bias is observed. Assumptions being met = reasons why it is wrong, but ≠ reasons why it is wrong in a specific direction.

R is large. That's it. In another situation with violated assumptions it could be small instead. The direction isn't important what is important is that it's not meaningful. If you tried to calculate how much money you have in the bank by multiplying what you deposit by what you spend you get the wrong answer because that equation is wrong. It's not important to explain why the result is too big or too small, it's just not meaningful to use that equation in that situation.

From this paper discussing the pitfalls of using the breeder's equation:

The breeder's equation provides a useful framework for conceptualizing the process of adaptive evolution by natural selection: selection causes phenotypic changes in a population, and genetic variation transmits these changes to future generations. This is not wrong, but given the assumptions we have discussed, it may generally be very inappropriate to apply this framework as a predictive tool in nature. There is consequently a problem with the enthusiastic way in which evolutionary biologists, ourselves included, have transferred this model from being a tool to conceptualise trait evolution and analyse artificial selection experiments (a context in which the inherent assumptions can be more readily approximated) to a predictive model in the field. Estimates of selection based on selection gradients are undoubtedly a conceptual step forward, but they do not escape reliance on the assumption that all traits and environmental factors that jointly influence studied traits and fitness have been identified, meaningfully measured and adequately modelled.

So if this is indeed an artificial selection experiment and you have actually designed the experiment to control for all possible sources of e.g. environmental variation, then it may be appropriate to use the breeder's equation model. However, we don't know anything about the source of your data, so it's difficult to evaluate whether your data are from an adequately controlled e.g. common garden style experiment.
Moreover:

No model can be expected to provide accurate predictions if its assumptions are seriously violated. The simple approaches we discuss here for predicting evolutionary change actually make many simplifying assumptions (e.g. constant population demography, discrete generations, constant environmental conditions; Merilä et al., 2001a), and we are certainly not the first to highlight the potential for predictions to fail when when these models are applied to natural systems (Hadfield, 2008; Price et al., 1988; van Tienderen & de Jong, 1994) or to call for caution in this regard.

They are clearly focusing on natural populations, which I don't think you are. But on the off chance that your "selected" parental population is not selected explicitly on your height measurement, then that is probably the reason why your estimates are wonky.
If you are in fact doing an artificial selection experiment, your estimates are probably wonky because some of those bolded factors were not controlled for.
For example, if these are bean plants, the parental and offspring generations may have been grown in different years with different climatic conditions. Even in a laboratory environment with well controlled conditions, unless the parents and offspring are grown at literally the exact same time side by side (which presents obvious logistical challenges) with positional rotation and constant care, it can be hard to fully reject the hypothesis of some unknown environmental factor impacting your data.