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why does translation occur more frequently than transcription?
In our textbook it says that translation occurs more in a cell than transcription but I couldn't find anything that explains why it happens

Suppose you had a book that said how to make a candle mold, and you wanted some candles.  Would you make molds from the instructions as often as you would make candles from the molds?

The simple answer
Under the assumption that each mRNA molecule is translated at least once, by necessity translation will happen more often than transcription. This is because the only way to get a protein is to translate an mRNA.
In other words, as long as there are more protein molecules (translation products) than mRNA molecules in a cell, then the process of translation must have occurred more than transcription.
One estimate of the ratio of protein molecules to mRNA molecules given here is on the order of 100-1000 for E. coli. ("Under exponential growth at medium growth rate E. coli is known to contain about 3 million proteins and 3000 mRNA (BNID 100088, 100064). These constants imply that the protein to mRNA ratio is ≈1000, in line with the estimate given above. [MP note: the numbers quoted are actually closer to ~400K and ~1.4K, so this is not completely accurate; the source paper itself estimates between $10^2-10^4$ for the same ratio of protein to mRNA. Note though that the yeast cell estimates are even higher.]) So there are likely between 100 and 10,000 times as many translation as transcription events.
The speculative answer
From the perspective of "why" this is the case in terms of why cells might "choose" to have this ratio of proteins to mRNA (accepting that it must be true based on the ratio of protein to mRNA), we might say that proteins are useful and mRNA is not. Therefore, it's better to have more proteins (implied but not stated by @mgkrebbs comment). Or rather, mRNA is only useful because it gets you proteins, which are useful.
So it "makes sense" for cells to minimize the amount of energy put into generating mRNA and maximize energy put into making proteins. But that's human logic projected onto the cell, we can't know that that is the logic for why cells do what they do (or evolved to do, or whatever).

I accept that a comment is not an answer, but I would have left the poster to work things out for herself, given that she has not even finished reading the Tour.  I find your contorted additional stuff about usefulness and energy unhelpful. It’s your logic, not “ours”. And please don’t talk about logic in relation to cells. The best way to express this is “why this happens” or “why it has developed in this manner”.

@David thanks for your input.

Your answer and link is unclear. See the original article supplementary materials : a way to estimate the ratio is to synthesize DNA encoding for yellow fluorescent protein, electroporation into bacterias, synthesize many small fluorescent probes complementary to a piece of the mRNA for the YFB, add to the culture with RNase inhibitor, 9h later select some cells expressing YFP and observe to estimate how many mRNA there are and how many YFP have been synthesized.

Hi @reuns, thanks fo checking. I looked into it and edited the answer to reflect more closely what the bionumbers actually are and to add more context.

Albert Lehninger had no problem in speaking about the 'molecular logic of living organisms' or about the 'molecular logic of the living state' (Biochemistry, 2nd Edn)

@user338907 i think this kind of language is fine as long as everyone is very clear that that logic is post hoc imposed by humans onto the cell, which is what i think i was trying to do here. cells don't reason. i feel the same way about "logic circuits" and similar models for cellular behavior- it's fine as long as you know it's just our model of what's going on, it isn't literally true.

the problem with this answer is there are many forms of RNA besides mRNA. Ribozymes, tRNA, rRNA, ect, also have to be transcribed, but don't get translated.

@John- you are correct. I will have to think about addressing that, but in particular for rRNA it is an issue, as that is most RNA in the cell.

Short answer: multiple Ribosomes can stack on the same mRNA making multiple proteins on a single mRNA strand before it gets recycled. A single mRNA strand may be translated dozens of times or more.

source
source
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It would be very wasteful (and hence evolutionary unfavorable) for cells to have the process other way round (more mRNA than protein). Remember it takes a lot of energy to synthesize mRNA, so it must be used as efficiently as possible (make many protein copies from a single mRNA).

I fail to see that your answer adds anything to that from @MaximilianPress except most dubious and unnecessary remarks about energy utilization and efficiency. If your argument is correct, eukaryotic introns in pre-mRNA are a waste of energy. Let it rest with the obvious. The comment about candle moulds should have been enough for the poster and anyone else.


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3 primers to know if Homozygous, Heterozygous or WT
In this imaginary experiment, I am using T-DNA to make an insertional mutagenesis that will get randomly inserted into the DNA where it might get inserted within a gene, disrupting it.
In order to get homozygous individuals, If I working on plants, I could self-pollinate this particular plant that has had this gene disrupted making homozygous offspring.
In order to prove that, I can do a PCR using 3 primers.
My question is, why 3 primers? I would use 4 primers!
I have been researching it and trying to understand it for myself for a while, but I do not reach any positive conclusion. I think it should be pretty basic and I am missing something.
I think I need forward and reverse primers for the T-DNA, but what about the 3rd primer? If I only use these two primers, though, I will not be able to distinguish between the homozygous and heterozygous.
Thanks!

Does this imaginary experiment come frome an article or a book? Could you add a reference? Is the goal to disrupt a particular gene or to search for mutants with paticular phenotype? Do you know the sequence of the gene which has been disrupted in the parent plant before self-polination? If the gene is known one or two primers can be designed to bind the gene and not the T-DNA.

As an exercise: can you imagine any scenario under which 2 of those 4 primers could be combined into a single primer, for 3 total? (Genotyping T-DNAs is generally done with only 3, I can confirm). Also, any reference would be helpful. Is this a homework or study problem?

Maybe two different PCR tests where PCR gives positive results, one where the insert was successful, and one where no insertion happened.

This is from one of my lectures, where the professor introduced T-DNA mutations and said that I would need 3 primers with no explanation. I do not have any reference though. What I can add is the following: if I do an electrophoresis next, the WT would have a of 900 bp, the Heterozygous would have two bands, 900 and 400 bp, and the Homozygous would habve 1 band of 400 bp.

From statistical viewpoint, one needs at least three measurements to have a meaningful estimate of mean and variance . Even if you do no such estimations, these considerations are likely the reason for recommending 3 primers. If you can have more in a reasonable amount fo time and at reasonable cost, it is only better!

As I think that this is in danger of turning into an extended Socratic comments-fest without really getting anywhere, I'll just post [this link][1] which discusses T-DNA insertion confirmation. I'll note that this site is easily accessible by Google, which is a good place to look first, to help us help you!
[1]: https://bitesizebio.com/35392/genotyping-t-dna-insertion-mutants/

Thanks Maximilian! I found this website too when I changed my Google search strategy. Now I am trying to understand it properly.

@Laura can you point directly to e.g. a quote or a figure or other part of this site that is confusing you? Have you followed the links?

Thank you for reaching out again. This part confuses me: " Flanking primers [...] The chosen primer pairs span this insertion site. Furthermore, the primer pair needs to cover a region long enough that the PCR reaction will not be able to amplify the insertion mutant". The flanking primers are not  in the left and right border of the T-DNA but outside of it, in random positions of the DNA? The third one is it the T-DNA, as I understand. These random primers will be designed in a way that will give you 900bp band except if they fall where the T-DNA is?

If it is like that, it means that you will have to amplify every region of the genome in order to find the T-DNA, right?

source: Stack Exchange

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Hierarchial and shotgun sequencing vs Massively Parallel Sequencing
Why is the cloning step unnecessary prior to sequencing with massively parallel sequencing (MPS; Illumina or similar technologies), when it is necessary for hierarchical and shotgun sequencing?
I know that the cloning step is helpful for introducing universal primer binding sites for sequencing. So is it because we are using probes with adaptors on them for MPS that we don't need to clone?

Can you clarify what MPS is or link to some information that describes in more detail what you mean? I can imagine that "MPS" is "massively parallel sequencing" but that acronym is not standard terminology, as far as I know.

@MaximilianPress Oh sorry, yes I mean massively parallel sequencing.

The construction of sequencing libraries for Illumina, PacBio, ONT, or similar  instruments is a large field of methodology that encompasses a variety of different techniques. For background on Illumina libraries specifically you can see here.
In some cases, notably Illumina, indeed the library construction involves sequencing adapters either being ligated or added by PCR. There are many strategies here and I don't claim exhaustive knowledge of all of them, I would suggest doing more research here if you are interested.
For other cases, sequencing occurs by alternative mechanisms. I suggest consulting overviews of PacBio and nanopore (ONT) sequencing to get ideas of how different methods work for the same basic task of shotgun sequencing of DNA without a cloning step.

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De novo mutation selectivity
I was recently reading about genetic diseases and came to understand that many of them are caused by de novo mutations in the prezygotic or early embryonic stages of the life cycle of the organism. Most chromosomal aneuplodies resulting in well known genetic diseases such as the downs syndrome caused by trisomy of chromosome 21 are caused due to de novo mutations.
However the thing that I didn't clearly understand was the reason for some de novo defects being more prevalent than others. For example the trisomy of chromosome 21 causing downs syndrome is relatively more common than the trisomy of the other chromosomes. Why are some aneuploidies such as trisomy 21 replicated independently in many different individuals (are more prone to occur) than others?

Questions for clarification: Is trisomy 21 actually more common than other aneuploidies? Or, are other trisomys or monosomies more likely to result in a non-viable pregnancy or spontaneous miscarriage? My suspicion is the latter, but I haven't done the research to back it up yet.

Edit: I am only addressing aneuploidies as those are the examples given. Other classes of mutation have distinct mechanisms, so I would split those out into another question if you are interested.
Chromosomal abnormalities occur more frequently when it is hard for cells to do meiosis and mitosis properly with a chromosome, or when the chromosome is more prone to "break" for some other reason.
The cell relies on certain architectural features (centromeres) to segregate chromosomes properly during mitosis. When segregation goes wrong, then you find aneuploidy much of the time. Chromosomes have other features such as telomeres and satellite DNA that can lead to chromosome fusion errors by different mechanisms. For example, yeast cells that have aberrant telomeres lead to high rates of aneuploidy.
Different chromosomes of the same genome have some variation in structure, repeat content, and other features that may predispose to aneuploidy formation or otherwise. These can include the quality and functionality of features such as centromeres and telomeres; these can vary even for the same chromosome across the lifespan of an organism.
For example in yeast, people have observed quite dramatic differences in the occurrence of spontaneous aneuploidy between different chromosomes (see figure 1). Yeast cells are much more robust to chromosomal variation than humans as they do not have a complex developmental cycle. However, aneuploidy of chromosomes with certain genes does seem to be selected against in that paper. There also appear to be dependencies in which the aneuploidy tolerance of one chromosome depends on the karyotype state of some other chromosome. "In other words, CIN [chromosomal instability] does not appear to be caused by some general property of being aneuploid per se but rather by determinants associated with specific aneuploid karyotypes."

Figure 1.
Experiments in the muntjac (a mammal) seem to show that the different chromosomes segregate more or less effectively based on centromere size (Figure 2).

Figure 2.
Human chromosomes have quite different rates of aneuploidy formation (see Table 1), so yes there is a bias. This is most markedly a bias in which acrocentric chromosomes and the shorter chromosomes have a somewhat elevated rate of aneuploidy (Table 2, same paper). So having an acrocentric architecture may make a chromosome more difficult to segregate.
When you ask about trisomy 21, note that chromosome 21 is acrocentric in humans. Thus, aneuploidies occur for chr21 at a somewhat higher rate.
However, commenter @MikeyC has a good point that there is a bit of ascertainment bias in how we find aneuploidies. Many aneuploidies are embryonic lethal and spontaneously abort; chr21 trisomy on the other hand yields living babies with noticeable features of their aneuploidy, so we know a lot about it and see it a lot. This seems to agree with the yeast data.
So there are multiple contributing factors. There is some variation in how chromosomes give rise to aneuploidy, but we also need to consider the means by which we observe aneuploidies in evaluating those molecular mechanisms.


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Correlation between polygenic scores and reproductive success
I was looking through this article in the journal Behavior Genetics. The researchers has investigated 400 000 people in the UK Biobank and calculated polygenic scores for 33 traits. Then they investigated how much offspring the 400 000 people had generated and coupled the traits to fecundity (number of children).
They found that people with a genetic composition that suggested that they had a high risk of (for instance) ADHD, obesity, and becoming smokers in general had better reproductive success.
I am having some problem understanding how big the effect is. If you look at the main results in figure 2, you get a list of polygenic traits with some measure of association, but how large a difference in reproductive success does the measure indicate?
If the number they get for "ADHD" is slightly above 0.03 does this mean that people with a very high genetic probability of having ADHD statistically have 3% more children than those with virtually zero risk of getting ADHD?
Question:
What is the interpretation of figure 2 in this paper? How many more children are people prone to various polygenic traits such as ADHD, high BMI etc. statistically supposed have according to the research?
Is says under the figure: "Each point represents a single bivariate regression of RLRS on a polygenic score. P value threshold is 0.05, Bonferroni-corrected for multiple comparisons."

RLRS is defined as respondent i’s number of children, divided by the mean number of children of people born in the same year

They fit a regression:

RLRSi=α+β * PGSi+εi

In this equation, RLRSi is the normalized number of children by subject i. α, the intercept, is probably something around 1 but not necessarily exactly 1, but would account for bias in the sample in the study vs. the reference population they used to normalize number of children.
"β" is the key variable they are estimating with the equation. Note that β is multiplied by PGSi, so β is not in units of RLRS but in units of "RLRS per PGS". If PGS was, say, height in cm, you would interpret β as "change in RLRS per cm of height".
However, in Figure 2 they only state they are plotting "effect size". They do not, however, report what effect size they actually use in the paper. This is bad. Effect size is not one thing, but rather a family of approaches meant to quantify the relative size of an effect, usually relative to variability. However, unstandardized regression coefficients (i.e., "β") are also used as effect sizes.
Peaking at the code for the paper, I think this is probably what they are reporting: raw β coefficients that they then label "effect size", but I didn't go through the trouble to actually download and run the code and verify I'm reading their output correctly. It's quite a jumble.
However, they do mention:

The “selection effect”, β, reflects the strength of natural selection within the sample. In fact, since polygenic scores are normalized, β is the expected polygenic score among children of the sample (Beauchamp 2016).Footnote1

and Footnote1 is:

The selection effect  β  equals Cov(RLRS, PGS)/Var(PGS). Since PGS are normalized to variance 1 and mean 0, this reduces to  Cov(RLRS,PGS)=E(RLRS×PGS)−E(RLRS)E(PGS)=E(RLRS×PGS) . This is the polygenic score weighted by relative lifetime reproductive success, which is the average polygenic score in the next generation (Robertson 1966).

Those conclusions seem consistent with the idea that these are indeed the unstandardized regression coefficients. It also seems like these are better numbers to use to interpret the results of the paper, rather than converting to number of children.
So, in summary, it is not trivial to convert these to "number of expected children", but if you knew the PGS for a given individual, you would multiply that by these numbers and add to α to get the RLRS. If α is exactly 1 and the PGS for an individual is 1, then yes, β=0.03 would mean their RLRS is 1.03 which would be 3% more children than the reference, not considering any of the other possible weighting or confounding factors.


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Why does Falconer's formula for broad sense heritability subtract correlation values instead of r^2 values?
Falconer's formula for broad sense heritability is 2*(r_monozygotic - r_dizygotic).
Mayhew and Meyer (2017) states:
"Subtracting the DZ phenotypic correlation from the MZ phenotypic correlation provides an estimate of the variance contributed by genetics when 50% of DNA is shared."
If we are seeking an estimate of the additional variance explained by an additional 50% of shared genes, shouldn't we be subtracting r^2 values rather than r values because r^2 is the proportion of variance explained?
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635617/

Each of these two estimates (DZ correlation and MZ correlation) express a numerical relationship (Pearson correlation) between individuals. Each of these could independently be squared to provide r^2 values, if that were one's goal. And the final quantity (heritability, h^2) that Falconer's formula provides is analogous to a r^2. But that does not mean that all of the intermediate steps can  be easily treated in terms of r^2.
I'll defer to wikipedia and other resources for more details, but in simple terms, variance (in r space) is easy to decompose into contributing factors (e.g. MZ correlation vs. DZ correlation), but r^2 is hard to decompose into contributing factors. See for example the general definition of r^2 given by wikipedia:

When you are computing R^2, you don't sum (or subtract) variances explained by different factors. You compute residuals (differences like MZ vs DZ), square them, and sum them, and then say all the variance that isn't in that residual quantity must come from the model (e.g. is variance explained by the model).
I think one issue here is that people use terms like "variance explained" in slightly informal ways. It applies to both r^2 and the final heritability estimate, but not every intermediate step of the computation of those estimates can be neatly phrased in those terms.
For more background here, I can recommend the wiki page for heritability.

I'm suggesting subtracting r^2 values from each other. Not subtracting an r from an r^2. i.e. 2*(r^2_mz - r^2_dz)

@Ashish ok I misread your question. But the point about the decomposability of r vs. r^2 stands. I'll edit

It is quite common to fit sequential linear regression models where you add a predictor with each model and report the difference in r^2 with each added IV. That difference represents the additional variance explained by the addition of the IV. Similarly, here you're adding 50% of the genome as a predictor. The difference in r^2 has a statistical meaning. Difference in correlations does not correspond to any clear statistical property as far as I can tell. My question is looking for a mathematical explanation of the substantive meaning of a difference in correlations.

@Ashish yes this is a common approach when performing regression analysis, one commonly evaluates models using this approach (though AIC or BIC are probably preferred for penalizing parameters). The r^2 difference is heuristically interesting for evaluating model fit in that case. It is however a different approach from what heritability estimation is trying to do, which is to try to estimate specific quantities of interest within a directly interpretable mathematical framework.


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Need help interpreting Western blot data
So the paper I am reading (here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094582/) uses densitometric units to quantify the western blots.
The authors mentioned that the unphosphorylated proteins were used as loading controls, and their quantities are represented by the densitometric graphs for each corresponding figure, but protein activity is measured by their phosphorylated counterparts which I believe is represented by the densitometric graph on the right of each corresponding figure.
Questions:

Did I interpret that correctly?
What exactly are loading controls?
I am trying to use these data for parameter estimation purposes. Is it possible to find the basal mass quantity of a particular protein?

There are several questions here, note that we usually ask for a single focused question.

Answers to each question:

Approximately, yes. In this case, phosphorylated Akt (p-Akt) is supposed to be the active form of the enzyme that goes around the cell doing its job, whereas non-phosphorylated Akt doesn't have activity that they are interested in. The authors do show that targets of Akt show characteristics of Akt activity when Akt is phosphorylated (Fig 2B, 2C).

Note that densitometry is just a way of measuring the intensity of some bands on immunoblots (westerns). In this case, it is normalized by the loading control (non-phospho Akt). So you could interpret the densitometry plots as showing the proportion of Akt that is phosphorylated. It is a little confusing because they subsequently normalize them to a specific timepoint. From the methods:

The results were quantitated by densitometric analysis (ImageQuant, Molecular Dynamics and PDSI, GE Healthcare). The ratio of phosphoprotein to its respective internal control was normalized to the control level at 0.5 h, arbitrarily set to 1.

Loading controls indicate how much protein of interest is present. For example, 1% of 100mg of protein is the same as 10% of 10mg protein (each is 1 mg). However the interpretation of 1% vs. 10% may be quite large. So if what you are interested in is a percentage or proportion, you need to have a loading control to allow unbiased comparison or normalization.

I don't really know what "basal mass quantity" means, but you could look into the molecular weight of proteins (usually measured in kilodaltons). For example the molecular weight of Akt is ~55kda. You can compute this from the protein sequence by adding up the weights of all the amino acids.


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Primers spaning exon-exon junctions
I am trying to choose which of two primers is most appropriate to use in qPCR, based on if the primer spans an exon-exon junction or not, where the primer that does span an exon-exon junction is considered the most appropriate.
What exactly is an exon-exon junction? When searching online I still am not sure if there is supposed to be an intron between the exons or not. I believed it to be two exons without an intron between them, however a classmate said "primer one spans over an intron, so it is the most appropriate to use".
Can someone please make this clearer for me. Thank you!

See similar questions elsewhere.
Looking into this paper on primer design, they write this:

It is often necessary to choose the primers from the exon—exon junctions (E-E-jns), the regions of mRNA which include nucleotide sequences from the 5’ border of one exon and 3’ border of the neighboring exon. Selecting at least one primer that represents one such E-E-jn of a particular transcript is important for expression profiling of alternatively spliced variants. The E-E-jn specific oligonucleotides could also be used as probes in microarray experiments addressing alternative splicing and the follow up confirmations by reverse transcription—polymerase chain reaction (RT—PCR) (e.g. Johnson et al., 2003). This type of ‘junction primers’ could be used to overcome the problem of genomic DNA in mRNA samples used in RT—PCR reactions.

In other words, a "junction primer" will be able to work off of cDNA (made from mRNA) but not genomic DNA. Thus you can be confident, if your primer works, that you are measuring expressed mRNA.
Presumably you are actually doing qRT-PCR off of mRNA of a eukaryotic organism, not just qPCR off of genomic DNA. That is why you want the exon-exon junctions, as explained above.
For a step by step guide to qRT-PCR primer design, you can see this.
See also their figure from the paper linked above:


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Sequence of DNA
While giving an amateur reading to DNA. I was stuck within the first few lines:
Refined resolution of the structure of DNA, based on X-ray crystallography of short synthetic pieces of DNA, has shown that there is a considerable variance of the helical structure of DNA, based on the sequence. For example, a 200-bp piece of DNA can run as if it were more than 1000 bp on an acrylamide gel if it has the right sequence. The double helix is not the same uniform structure.
What does this highlighted portion mean?

Please provide a source for your quotation. The English is slightly strange, especially the last sentence. I think I know what it means, but an answer would be more useful if the context were provided. Your title should probably be something like "The effect of base composition on the structure of DNA", if this is indeed what it is about.

DNA sequence is what we call a string of nucleotides in the DNA polymer, such as GATTACA, representing a chemical structure wherein each letter ("G", "A", "T", "T", "A", "C", "A") represents a nucleotide that has a chemical bond to the next nucleotide. Possible nucleotides in DNA are adenine, guanine, thymine, and cytosine, e.g. A, G, T, C (you may also see such terms as thymidine, thymosine etc. depending on what piece of the DNA molecule you're talking about). What such a sequence implies for double-stranded DNA is that those nucleotides are then hydrogen-bonded to another "reverse complement" sequence, which for every nucleotide in the first sequence has a "C" for every G, a "A" for every T, a "G" for every C, and a "T" for every A. One way of representing this would be as follows:
GATTACA
|||||||
CTAATGT

Where | represents a hydrogen bond between the two strands of DNA at each nucleotide. A single-stranded DNA however would not have the H-bonds or the (lower) complement sequence.
Altogether, this is what is implied by DNA "sequence", though normally we only write GATTACA for simplicity.
What your quote means is that GATTACA can have a different behavior in a polyacrylamide gel than GGGGGGG or ATATATA or CCGGCCG, or any number of other 7-nucleotide DNA polymers. This has to do with the shape of the different nucleotides, each of which has a distinct chemical structure:

Some of the nucleotides force the DNA polymer molecule to take on different shapes, or to be stiffer or more flexible, because of their underlying chemical shape.
A polyacrylamide gel is basically a big tangle of polyacrylamide molecules and bonds. So think of DNA moving through it like trying to drag a rope through a forest. A very long rope will tangle more than a short one and will take more effort/time to pull through the trees. However, a rope of the same length that has a different structure (maybe it has lots of knots tied in it, or it is stiff rather than flexible) may also be harder to pull through the forest, even though it's not any longer.

I like your analogy in the last paragraph. I might borrow it...

@MattDMo thanks! it is my go-to for any kind of electrophoresis. There's a professor at UNM, maybe emeritus now (I forget her name... just saw a talk one time) whose whole thing is to think of all the tiny molecular stuff as happening on a spatial scale that is similar to our environments ("walk into a cell. what do you see?"). I think she's very right that that makes a lot of this stuff click in human brains.

It means what it says.
DNA runs through a gel at a speed which is due to its shape, which is mostly determined by its size.  What it's saying is that certain sequences alter the shape of the DNA so much that its passage through a gel resembles that of a much longer piece with a more standard helical shape.

Does it mean this: that a 200bp DNA can run at slower speed as if it is a 1000bp DNA, if it's sequence is altered in a certain way.    WHAT DOES THE RIGHT SEQUENCE MEAN IN THE SECOND LAST SENTENCE.?

@Arjun Tilak It means that some sequences can make the DNA take on different shapes like sequences with lots of G can form G quadruplex structures. https://en.m.wikipedia.org/wiki/G-quadruplex#:~:text=In%20molecular%20biology%2C%20G-quadruplex,one%2C%20two%20or%20four%20strands.

@ArjunTilak Avoid using strings of all-caps like that. It is perceived as "shouting" on the internet and can come across as quite rude.

@BryanKrause ok sir!! I am new at this site,will keep in mind!


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RNA sequencing and taxonomic analysis of gut microbiome
I've been searching for methods of RNA sequencing, and I always find that the step of rRNA removal is emphasized. However, in my research, I need to analyze both functional and taxonomic data from the RNA extracted from stool to study the gut microbiome.
Do I just to just synthesize cDNA and then use nextera xt for library preparation? or should I use another kit such as truseq stranded total rna / scriptseq v2?

It’s unclear what you are asking. You want to do RNA-seq of a microbiome, but you also want taxonomic data, and you are worried that rRNA depletion will prevent taxonomic identification? You’ll need to do metagenomic sequencing and assembly in order to have contigs against which to map your RNA-seq reads

https://www.nature.com/articles/srep26447 Actually i want to try this methods mentioned in this article .. by studying functional and taxonomic data using just RNA-sequencing. But I didn't found any other article to understand the approach. when I start searching for library preparation kit I always find that there is a rRNA removal step.

mOTUs does some taxonomic analysis... not sure whether this is what you need.

The article that you link to clarifies in the methods section that there are two different extraction protocols employed, one for 16S information (probably DNA extraction) and one for total RNA. In fact, the 16S data comes from a previous publication. I would suggest following the recommendations in these methods sections with regard to kits, sample treatment, etc., if you are intending to use their methods.
Alternatively, you can do total RNA sequencing, which will strongly bias towards rRNA, which makes up 80% of the total RNA pool. For trying to choose between total RNA sequencing vs. the two independent preps, you can look at this tech note and at the methods sections of any papers whose protocols you want to follow. The reason that rRNA depletion is so common is that if you are not interested in rRNA, it is kind of wasteful to lose 80% of your reads to it. Also, informative rRNA regions such as the hypervariable regions contain most of the taxonomic information, and these are specifically targeted by e.g. 16S amplicon primers, such that very little of the rRNA sequence from total RNA sequencing is useful anyways. Moreover, it is probably preferable to use DNA sequencing to determine taxonomy anyways, as it will probably track actual genome abundance a lot better than rRNA, which is after all probably imperfectly correlated with genome quantity.
If you do two separate preparations, I would strongly recommend not doing the two preparations at two different points, but doing them side by side from the same sample subdivided. It is not at all clear that this was done properly in the linked article. Minor differences in sample preparation are notorious for their effects on microbiome taxonomic/functional composition due to e.g. differences in toughness of gram positive vs. gram negative organisms.

You could also do 16s amplicon sequencing on DNA rather than RNA. Without having looked into it specifically, my gut tells me that DNA would be more accurate than RNA for looking at community composition. There are also commercial kits for extracting both DNA and RNA from the same sample.

@MikeyC this is accurate, I think it is preferable to use DNA. I will update to reflect this.


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Looking for a way to create a video or animated image of a series of segmented lines with cells in ImageJ
I considered asking this on software recs, but felt that the biology community would know better if ImageJ was capable of doing something this way, so I'm asking here.
I'm trying to do a project with ImageJ to model the movement of the flagellum of a Euglena cell. I have an MVI video file of a Euglena cell whipping its flagellum and I converted it into a series of TIFs. Then, I used the segmented line tool to draw over the flagellum so that each image has a "skeletal" line over the flagellum. What I want to do now is put the images together to construct a moving image that would act as a simplified model for the original video, but I don't want the background of the cell to be visible - just the skeletal lines. I want to fix one end of the line on a graph as the origin and be able to play the series of images back-to-back so the viewer will be able to focus solely on the movement of the line to more clearly visualize the cell's movement. It should look something like this:
https://blogs.mathworks.com/pick/2010/08/13/animating-double-pendulum/
I've considered using MATLAB, but I don't know if it can actually do it under the conditions I've laid out. I hope what I said makes sense because its a little difficult to put into words. Is there a way to do this in ImageJ or MATLAB, or do I need a different program entirely?
Thanks in advance!

I think you're better off asking in a software forum as this isn't really a question about biology.

Or as a more specific alternative to @Armand suggestion, SE Bioinformatics, where people may be more familiar with the software.

Thank you. I was going back-and-forth with whether to post here or software recs, but I didn't think they'd know much about ImageJ. I've asked a similar question like this before and the recommendations I got there weren't really suitable for data analysis so I thought an answer was best from people who work in STEM. I didn't know there was an SE Bioinformatics so I'll try there.

I’m voting to close this question because it doesn't seem to be about biology as defined in the [help]. As suggested by @David it might be a better fit for [Bioinformatics.SE], but please do not crosspost, instead request migration by flagging your post for moderator intervention and explaining what you want and why.


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Zygote Implantation and Pregnancy Detection
In a normal pregnancy, the embryo (blastocyst) implants between 8-9 days after ovulation.  My understanding is that it is able to implant because the fertilized embryo produces a hormone that triggers the uterus to prepare for implantation.

At what point after this implantation can the first signs of pregnancy hormone be detected in the blood stream? (Hospital Birth Detection)
At what point after this implantation does the uterus prevent further embryo implantation

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


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Bioinformatics - DNA binding, sequence-specific protein data
I am a computer scientist and we are working on protein function prediction algorithms. Right now, we would like to examine protein-DNA binding properties computationaly. For this, we would make use of datasets about containing DNA-binding proteins and the DNA sequences they bind to. I was looking on Gene Ontology and Protein Databank where I'd expect such informations, but I was unable to find it. Are such data presently difficult to obtain? Or are simply not collected? Or could you give me some direction where to look for such data. Thanks.

Try these searches: TRANSFAC, JASPAR, ENCODE, position weight matrices (or PWMs)

You are seeking a database of transcription factor binding specificities. Some model organism databases (which are manually curated), such as Wormbase contain some of this annotation. I suggest you search PubMed for papers by Tim Hughes at the University of Toronto. He has published extensively on this topic over the past 5 years.  Papers he cites, and papers that cite him, will let you bootstrap your way in to the topic.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


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Tools to analyze RNA-seq data
I hope this is a good place to ask such question. I have to do some data analysis on RNA-seq data from human cells. I am currently searching for tools to help me with that. Specifically, I would need some tools to analyze the gene expression from the data. Something to help me plot the expression of selected genes in each fastq file and compare the differences in the expression with the possibility to export the results or some command line interface for scripting. Basically I need something where I can put a fastq file and perhaps also a human genome annotation file as input and get gene expression as output. I have looked at bioconductor and it's packages and on Wikipedia's List of RNA-Seq bioinformatics tools. I suppose some of these tools have to be able to do what I need, but I have been unable to find out which one and how should they be used to achieve that. Could someone please give me some advice?

I suggest you start from a good reading first - something like RNA seq Data Analysis.

You will likely need a tool to "map" the reads on the reference genome.
You may find such a reference genome, together with annotations, here:
ftp://ussd-ftp.illumina.com/.
Mapping tools such as bowtie2 or bwa take fastq files and reference genomes and output mapping results in a format called sam.
You then have a lot of options to estimate gene expression.

You can write your own algorithm to parse sam format and estimate normalized read counts on each gene.
You can combine more or less low-level tools such as samtools, pysam, htseq with some scripting to do this.
You can use tools that do the counting (like bedtools ot htseq-count) and differential expression analysis (like deseq2).

In the last case, I would advice to start from the documentation of the final tool to find out what are the tools you need to generate the output of the preceding step.
It is very likely you will use some R or Python, or use the web platform galaxy for some of the steps.
Edits
As mentioned by @scribaniwannabe in this answer, the paper about the Tuxedo suite of tools gives a good example of the steps to carry out an RNA-seq analysis using recent tools (as of October 2016).
As @Student T reminds in this answer, RNA-seq data contain reads that can come from exon-exon junctions, so the read mapper has to be set up in such a way as not to discard reads not mapping continuously on all their length on the genome. To my knowledge, HISAT2 and CRAC do this by default. Bowtie2 needs special settings.

TopHat is the part of the tuxedo suite that does splice-aware mapping.

@swbarnes2 You are right concerning the older tuxedo suite (the new one uses HISAT2). Curiously, TopHat uses bowtie2 internally, and it seems that it doesn't use the "--local" mode (look for "local" in the tophat executable, which happens to be a complex python program). I don't know how it achieves splice-awareness.

While I also agree @bli that R and Python (in particular Bioconductor) have more than enough packages for you to compare gene expression. You shouldn't align your reads with bwa or bowtie because they don't take introns into consideration. You should use TopHat or STAR.

You're right, although bowtie2 can be used with the --local option to avoid discarding reads mapping on exon-exon junctions. Other aligners that could be used are HISAT2 https://ccb.jhu.edu/software/hisat2/index.shtml and CRAC http://crac.gforge.inria.fr/.

I think that STAR is the preferred splice-aware aligner nowadays.  STAR can output counts by gene or by transcript.  Assuming you have Illumina data, you can try using the tools on Illumina's BaseSpace.  RNASeq might be one of the things that you can do for free there.

The answer @bli gave is great. I thought I would point out that Johns Hopkins also recently upgraded their tuxedo suite. Looks promising and has great instructions for use.
Also, I've begun to grow quite fond of the GeneTrail 2 tool for my RNA-Seq secondary analysis. Gives great results for enrichment analyses.
Hope this is helpful.

Lastly, I also like to use [HOMER][3] for de novo motif finding up/downstream of the promoter regions of deferentially expressed genes.
[3]: http://homer.salk.edu/homer/motif/

Thanks for the link about the tuxedo suite. The explanations in the article seem useful.

pyrpipe claims to be a one-stop python library for RNA-seq analysis. Here is an illustration from their paper:

Furthermore, I would like to draw attention to ENCODE's official RNA-seq pipeline, which is actively maintained on ENCODE-DCC's GitHub repository.

I think HTSeq does almost that. It outputs a matrix of read counts per gene given a fastq sample and annotation file


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What is the best way from remove background signal from a band when doing Western blot image analysis?
I am doing image analysis of a Western Blot in Image J. I have calculated the total intensity of my protein bands of interest through outlining each band using the rectangle tool in ImageJ, and calculating the area and mean intensity. I have multiplied the area with the mean intensity to obtain the total intensity. However, I want to remove the background signal from each protein band of interest.
The background adjusted protein signal would be calculated as: (protein band mean intensity - background band mean intensity) * (area of protein band). On my blot, I have two isoforms of my protein of interest that were detected by the antibody, and I want to subtract the background from all bands for both proteins.
The way I am calculating the background, is for each lane, making a rectangular selection near the top of the lane where there is no labelling against any protein. The length of the rectangular section is equal to the length of the lane. However I am not sure how wide the rectangle for the background should be. From what I have seen online, there is no fixed method for calculating the background signal.
Are there any ways to determine how wide you want the band for your background to be? E.g. should the width of the band for the background be equal to the width of the protein band of interest, or can it be wider than that?
Additionally, how can you know if you have made the rectangular band for the background too wide?
Any insights are appreciated.

I haven't done immunoblots for years, and didn't do much quantitative analysis of them even then, so caveat emptor. However:
1) "Are there any ways to determine how wide you want the band for your background to be?"
I would say that the central limit theorem is a good place to start. Basically, as the size of your background increases, the precision of your estimate of mean background intensity will also increase (asymptotically). So you want your background sample to be as big as possible.
This does have some assumptions:

Little regional fluctuation of mean background signal

(related) Absence of gel artifacts

So, you want your background sample to be as big as it can be without including any artifacts and as long as it is representative of the blot in the general region of your band. In practice, I imagine that taking a selection the size of your protein band is probably fine in most cases, and probably easier to explain. You just have to pick a representative piece of background to measure.
2) how can you know if you have made the rectangular band for the background too wide?
Wider than the blot is too wide. On an infinitely large blot, I think you can't make it too wide. For practical purposes, you want it to be as wide as you can get it and not overlap obvious artifacts or the edge of the blot. But again, it's probably easier to explain just selecting a piece of background as big as the protein band that is roughly near the protein band, to account for local background variation.


text:
How many people need to have the same mutation of a gene in order for that gene to be seen as a feasible candidate for a disease?
I am learning about genome-wide association studies (GWAS) and I know that they are used to see whether certain SNPs are associated with a disease of interest. From everything that I have watched and read about GWAS, it seems to me that GWAS can only be used to analyse SNPs in relation to a disease.
However, I was wondering whether GWAS can be used to analyse gene deletions in relation to a specific disease. For example, if you are interested in seeing whether a deletion of a certain gene X is associated with a disease like intellectual disability.
Would it be feasible to recruit thousands of people with intellectual disability and thousands of controls and then sequence their genomes and see whether the deletion of a specific gene is significantly associated with intellectual disability?
I cannot see how else scientists can determine if deletion of a specific gene has a meaningful association with a disease. I know that many mutations in intellectual disability patients are de novo and rare, so it is hard to find a large sample size of intellectual disability patients with a specific mutation. Many journal papers focusing on the deletion of a gene in relation to intellectual disability just reference the cases of individuals carrying the deletion reported in the literature/databases, and use that as evidence to justify the gene as a candidate gene worth investigating.
However, if you have a very small cohort (e.g. < 10) of patients with intellectual disability carrying deletion of a specific gene (and have no other detected mutations), is this strong enough evidence to say that this gene is a plausible candidate for the intellectual disability?
In biological research, how many people with a disease need to be carrying a specific gene mutation in order for that gene to be considered a candidate gene for the disease (is there a minimum value)?

Genomic analysis of copy number variants is routine for GWAS purposes. Here is one early example. In that case I believe they used array CGH rather than sequencing but the principle is the same, and it is more cost effective with sequencing now.
I suggest that you look into the literature of power analysis for GWAS studies. Here is one publication, here are some course materials on the topic.
This is really a statistical question rather than a biological question, at its root.

Short answer: about a million people.
Check out Cochran's formula:

In reality, here's a paper that discusses it more (still using pretty simple assumptions):
"Our results indicate that predictive models for many complex traits, including a variety of human disease susceptibilities (e.g., with additive heritability h 2∼0.5), can be extracted from data sets comprised of n⋆∼100s individuals, where s is the number of distinct causal variants influencing the trait. For example, given a trait controlled by ∼10 k loci, roughly a million individuals would be sufficient for application of the method."
This subject is a lot more rich than I can do justice to here. There have been a handful of papers published on this.


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What does it mean to quantitatively describe a cell?
To begin this question, I will quote Molecular Biology of the Cell (page 38):

... Biological systems are, ..., full of feedback loops, and the behavior of even the simplest of systems with feedback is remarkably difficult to predict by intuition
alone; small changes in parameters can cause radical changes in outcome. To go from a circuit diagram to a prediction of the behavior of the system, we need detailed quantitative information, and to draw deductions from that information we need mathematics and computers.
... You might think that, knowing how each protein influences each other protein,
and how the expression of each gene is regulated by the products of others, we should soon be able to calculate how the cell as a whole will behave, just as an astronomer can calculate the orbits of the planets, or a chemical engineer can calculate the flows through a chemical plant. But any attempt to perform this feat for anything close to an entire living cell rapidly reveals the limits of our present knowledge. The information we have, plentiful as it is, is full of gaps and uncertainties. Moreover, it is largely qualitative rather than quantitative.
(Johnson, A. D., Roberts, K., Lewis, J., Morgan, D., Raff, M. C., Walter, P., Alberts, B. (2015). Molecular Biology of the Cell. United States: Garland Science, Taylor and Francis Group.)

Thus comes a fundamental question: what does it mean when cell biologists want to quantitatively describe a cell?
In my understanding, a cell is a complex system, and a quantitative description of it invokes treatment of the cell as a mechanistic, mathematically determinate system. For example, a projectile in the context of Newtonian physics is a product of such treatment. Physicists use a series of equations to represent motion of the projectile. They also plug in initial values such as velocities and positions to supplement the equations.

As an analogy, quantifying a cell might mean finding a series of mathematical expressions for every chemical reaction or every biochemical inside a cell, and using a set of initial conditions to calculate the overall state of the cell at its very beginning. In this way, cell biologists can theoretically calculate figures of interest for whatever chemical reaction at any point during the cell's life. Of course, such an effort would be astronomically large and complex, and not feasible at present.
However, if this is what quantification of a cell means, why do Bruce Alberts and other authors cite feedback loops as a hurdle that stands in the way of quantifying a cell? How do feedback loops affect quantification of a cell?

I don’t think cell biologists have or ever had any interest in “quantifying“ cells, whatever that may mean — and it will certainly mean little to people describing themselves as cell biologists. By definition and the origin of the term, these are people whose interest lies not at the molecular or chemical level, but in the behaviour of those components visible through a microscope. The book section quoted is at best an apologia for the non-molecular or numerical nature of the discipline. It worst it is pretentious nonsense and in any case it is best ignored.

The 2017 article "The quantified cell" ( https://www.molbiolcell.org/doi/full/10.1091/mbc.e14-09-1347 ) seems appropriate to cite here

Quantitative in the context of biology is similar to chemistry, and means "how much of something there is" - for example, how much of a particular protein is produced under what conditions.
Now, you might think this is a simple problem, just measure the protein/RNA/DNA and find out. However, it isn't quite as simple as that. Even if the best methods we have were truly quantitative (e.g. see Western blot quantitation for measurement of protein changes), which a lot of them aren't, the changes you see in one cell type might be different in another cell type (or even between cell lines of the same type of cell) and certainly between species.
You then have to consider - how well does a lab strain of something (say a bacterium like Escherichia coli or a cell line or cultured virus) differ from those found in the wild, and how does that vary geographically.
Life consists of huge numbers of feedback loops - stimulus results in action, which removes stimulus, which returns to baseline, allowing stimulus to function again. You experience this all day, every day of your life. It is how things like nerves function for example. In almost all situations it is not a single input results in a single action, rather that multiple inputs result in multiple outcomes, meaning an interconnected web of feedback loops. Have a look at this figure for a well known pathway and well studied, Caspase 3 signalling, from this article (I have no affiliation to the study):

This is one of the simple ones, which results in just one form of cell death - notice how many inputs there are and all the players. In general we can model 2-3 part systems fairly easily, so to work this out, each individual part of it needed to be studied and worked out empirically, determining which parts interact with which and why they do that under what stimuli.
Go and have a play at string.db with something like the well known and heavily studied tumour suppressor p53 (hit search, type in p53, choose human (Homo sapiens), then hit continue). Keep clicking the "more" button on the results page, and it will give you some idea of just how complex this is. Similarly, have a look at a metabolic pathways chart - this happens in every single cell.
We cannot currently reduce life to equations.

Even more fun: many of the feedback loops share the same intermediate signals so that when one loop is running, it implicitly modifies the behavior of the other loop.  The biggest difference between biological and engineered systems is that biological systems have absolute no design constraint for comprehensibility.  Evolution can and will build ridiculously complex systems that stretch belief as to how they can possibly work, yet somehow they do or they wouldn't have been selected for.

Quantitative vs. qualitative
Quantitative description usually implies numbers, rather than just saying which response follows which stimulus. For example, describing a chain of biochemical transformations is qualitative description - we know what processes happen, in which order, etc. Quantifying how many of the molecules would actually undergo this transformation up to a certain step (or to the very end) would be an example of a quantitative description.
Types of quantitative description
Quantitative description usually implies doing some math to relate the input and the output parameters, although the origin of the equations can be different. One typically distinguishes:

first principles description - when the model is based on the fundamental laws of chemistry and physics;
phenomenological description - when one uses the laws attested experimentally.

Examples of quantitative description of a cell
One should not really be looking for one fits all sizes mathematical model. Rather, the mathematical/quantitative description is usually tailored to a problem in hand. I give below a few examples, but this list is by no means exhaustive:

elastic properties - one can use elasticity theory to describe the deformations of a cellular membrane in response to pressure, attempts to pierce it, etc. In the former case one may be able to provide the mathematical expressions for the shape of the cell in response to specific level of pressure, in the latter one aims at determining the breakdown point
transport of chemicals/molecules - most of molecules absorbed by a cell or produced in it, travel to their destinbation via diffusion or with convection flows. Such flows can be modeled using diffusion equation and hydrodynamics equations respectively. Alternatively, if we are dealing with more directed transport chains, various random walk models become of great use. This allows to extimate the efficiency and the speed with which chemicals are transported to their destination.
transcription/translation are routinely described quantitatively, e.g., using rate equations models for the transcript/protein molecular growth.
Protein and RNA folding are a domain where thermodynamically motivated energy minimization has been of use for many decades.
Cellular dynamics is often modeled using non-linear dynamics, e.g., an HIV infection goes through a few phases: early on the auxhiliary proteins for reaction catalysis and interfering with the cellular machinery are produced, which is followed by synthesis of the essential proteins and then the new viral genomes. The transition between different stages is triggered by concentration of earlier synthesized proteins, and is nicely modeled by non-linear equations similar to Lottka-Volterra type. The feedback loops mentioned in the OP are often studied using non-linear dynamics.

One could go on further, to propose quantitative models for describing optical, thermal, chemical, replication, growth, and other processes - whatever is of interest. I suggest consulting the books I cite below for information on more specific topics.

References

Phillips et al., Physical biology of a cell
Keener and Sneyd, Methematical Physiology: I Cellular physiology

Let’s start by considering a very simple feedback loop, namely a system with just two genes A and B which repress each other.
Such a system can be in two possible states (it is bistable):

A is expressed and thus suppresses B, which is not or hardly expressed.
B is expressed and thus suppresses A, which is not or hardly expressed.

We may perfectly understand this system on the biochemical and genetic level, which allows us to quantify the two above states.
However, without further information about the system’s history (initial state), we cannot say in which state the system will be at a given time.
Moreover depending on the strength of the expression it may be that the system occasionally switches between the two states just due to noise.
(Note that for the above bistability, a second ingredient is needed, namely non-linearity, but we get that from the thermodynamics of chemical reactions and genetic expression anyway.)
Now, in a cell you have a myriad of such feedback loops intertwined, which grants you a bazillion potential states to consider through combinatorial explosion.
The challenge is now to figure out which of these states are generally and historically plausible.
This is horribly difficult even when you perfectly understand all the individual molecular processes.
Note that through the complexity you have more than just binary on and off states like in my introductory example, but a fine spectrum of potential states which gets fully continuous through molecular noise – thus affecting quantification.
A helpful analogy might be a modern computer running several interacting programs compiled with a highly efficient compiler.
You can perfectly understand single operations on the level of a transistor and similar fundamental components, but this doesn’t get you very far with understanding the programs that are running.
Also, to even remotely predict the computer’s behaviour, you need its history, i.e., what is currently stored in memory.


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How to create a 3D density map of a system of varying number of particles?
I have a time-dependent system of varying number of particles (~100k particles). In fact, each particle represents an interaction in a 3D space with a particular strength. Thus, each particle has (X,Y,Z;w) which is the coordinate plus a weight factor between 0 and 1, showing the strength of interaction in that coordinate. Here http://pho.to/9Ztti I have uploaded 10 real-time snapshots of the system, with particles are represented as reddish small dots; the redder the dot, the stronger the interaction is.
The question is: how one can produce a 3D (spatial) density map of these particles, preferably in Matlab or Origin Pro 9 or ImageJ? Is there a way to, say, take the average of these images based on the red-color intensity in ImageJ?
Since I have the numerical data for particles (X,Y,Z;w) I can analyze those data in other software as well. So, you are welcome to suggest any other analytical approach/software
Any ideas/comments are welcome!

well, have you tried anything so far?

yep; ImajeJ and Origin Pro; both not successful.

R packages misc3d and rgl. The function image3d in misc3d does what you need and is very parametrizable via rgl (change the transparency, the view angle, axes, etc...). I used it with fairly big arrays (~10000000 data points/plots) and it produced great 3D density plots.
Both R (e.g. package biOps) and Matlab (image processing toolbox) have very powerful tools for image analysis if you need to get for example RBG levels in your plots but as far as I understood, you have the 3D arrays containing the interaction values, right?
Matlab proposes similar tools if you prefer Matlab (don't remember the exact functions) over R and both will require a fair amount of RAM as the data must be loaded in the environment.
As your system is time dependent you can also create movies fairly easily if you wish so (e.g. rgl's movie3d function).
Here a quick example of a 3D density plot:

Yep; you're right. I have the arrays of $x$ , $y$ , and $z$, and $w$ for the interactions locations and strength.

Ok, then it is quite straightforward to make such plots using those functions.

Well, I installed R , misc3d and rgl packages. But, to use image3d, one needs a "3 dimensional data array". My collection of $(X,Y,Z)$s makes a 2D array.  Should I convert it to 3D??? and how??

@deeep how is a collection of (X,Y,Z) 2D?

Well, here is the collection of my $(X,Y,Z)$s:
$$x_{1@t1} \quad y_{1@t1} \quad z_{1@t1}$$
$$x_{2@t1} \quad y_{2@t1} \quad z_{2@t1}$$
$$...$$
$$x_{N@t1} \quad y_{N@t1} \quad z_{N@t1}$$
$$--------------$$
$$x_{1@t2} \quad y_{1@t2} \quad z_{1@t2}$$
$$x_{2@t2} \quad y_{2@t2} \quad z_{2@t2}$$
$$...$$
$$x_{M@t2} \quad y_{M@t2} \quad z_{M@t2}$$
$$--------------$$
$$.$$
$$.$$
Where $t1$ refers to time 1, $t2$ time 2, etc. Also $N$ is the number of particles (=interactions) at time 1, $M$ number of particles at time 2, and so on.
To me this is a 2D array, like in Matlab.

@deeep I see. To convert that to a 3D matrix you can use your X,Y,Z coordinates to map the w into a 3D matrix. For example a 3D matrix M@t1 (casted using array() and of the size of your entire space) will contain the value $M(X_1,Y_1,Z_1)=w_1$, $M(X_2,Y_2,Z_2)=w_2$ ... $M(X_N,Y_N,Z_N)=w_N$ and I guess 0 for all values not mapped by any particles. I guess the assignment M(C)=w would work with C being your coordinate matrix @t1. Once this done, you can call the function image3d(M,x,y,z) with x,y,z being the vectors $x={x_1,...,x_N}$ and so on. You will need to create $t$ 3D matrices.

@deeep if you need more help we might chat instead of using the comment section. See https://chat.stackoverflow.com/. You will need 20 reputation points though. Also you might want to shoot me an email with say the data @ t1 so I can make a R script that would do that and so you have an actual example using your data.

Thank you! Like you said chat is not an option for me at this point. I dont have enough reputation. I'll send you the data.

@deeep ok see me my profile.

Cagliari2005, I was able to create a 2D array (=matrix) $N$ x $3$ of N tuples (x,y,z) with the following piece of code (I'm pretty sure there must be a much neater and cleverer way to do so, but I am not familiar enough w/ R). Yet, was not able to convert it to a 3D array using weight factors $w$ .
`df <- read.table("xyzw.dat")
x <- array(df$V1)
y <- array(df$V2)
z <- array(df$V3)
w <- array(df$V4)
ar2d <- array(0,dim=c(N,3))
for (i in 1:N)
{
ar2d[i,1] <- x[i]
ar2d[i,2] <- y[i]
ar2d[i,3] <- z[i]
}`
I sent you an email yesterday w/ the data @ times 1 to 10.

deleted my previous comment as I thought I found a direct solution but no. You will need to estimate a weighted kernel. I am not certain as how to exactly incorporate the  weight. pythonist answer is more complete than mine.

So, suppose that they are all equally weighted, i.e. there is no weight function. Then how should I make a 3D array from $(X,Y,Z)$s?

One approach: fit a kernel density estimator to the observed coordinates $\{ (X_i,Y_i,Z_i) \}$ and color by density: https://stackoverflow.com/questions/25286811/how-to-plot-a-3d-density-map-in-python-with-matplotlib/25354417#25354417
Incorporating the weights $w$ of each observation is a bit trickier, but there's code to do this: https://stackoverflow.com/questions/27623919/weighted-gaussian-kernel-density-estimation-in-python/27623920#27623920 , which you can swap in instead of stats.gaussian_kde.

Trying to analyze that much data in ImageJ seems likely to end in frustration. Matlab is probably a more productive angle. Personally, I would use Python. Specifically, the numpy module. Try looking into the histogramdd function in numpy. It might be able to accomplish what you're after.


text:
Why is the genetic code so heavily conserved?
Except some organisms, most organisms follow the same Genetic Code
tRNAs, tRNA synthetases, ribosomes, etc. comprise the translational machinery for converting nucleotide codons to proteins.
My question is:
Why is the genetic code so heavily conserved across life, given that the genes for the translational components noted above presumably vary significantly across evolution?

Can you cite any sources or point to anything that indicates where you got your information? Note that the genetic code is not the same for all organisms: https://en.wikipedia.org/wiki/Genetic_code#Alternative_genetic_codes

It looks like you might be looking at this or something like it: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3783965/ Note that while tRNA genes sometimes go into the nucleolus, they do not leave the chromosomes. It is just that the tRNA part of the chromosome goes to hang out in the nucleolus for a while!

@MaximilianPress my bad, I didn't google alternative genetic codes, because I thought only one existed. Still, the vast majority of organisms (Except mitochondrial genes of some organisms) follow the same genetic code. I will add edits to my question.

@MaximilianPress You mean that tRNA are synthesised from chromosomes the same way mRNA are (by transcription)?

Even if they are synthesized from DNA, what if that specific part of DNA (which codes for tRNA) undergoes variation?
Wouldn't that lead to large diversity of genetic codes, possibly one for each species?
Why is it not the case?

I suggest that you read more about the evolution of translation and transfer RNAs, they are not stable in their sequence (e.g. https://www.frontiersin.org/articles/10.3389/fgene.2014.00303/full). While in general the genetic code and the translational apparatus is remarkably conserved (and therefore a good argument for a single origin of life), it is by no means "frozen" or identical across all organisms.

tRNAviz is a neat tool from the Lowe lab that allows you to visualize the structure and sequence variation of tRNA isotypes across the tree of life.

looking at update to question: what do you mean by "vast diversity of genetic codes"? I am sure that the nucleotide sequence variation underlying the genetic code is "vast", but that is probably not what you mean. I am not sure what "vast" diversity of a qualitatively-defined multidimensional construct looks like. If you are confused about evolutionary stasis, why it's hard to get "better" than the existing code, that would be a more answerable question.

@acvill that link seems to be broken, this one works: http://trna.ucsc.edu/tRNAviz/summary/

@MaximilianPress Thanks!

I  suggest you reflect on what happens if a single mutation occurs in a gene during evolution. There is a chance that it can be lethal, beneficial or neutral. Now, how many mutations would occur in a bacterium of 1000 genes if a mutation of the anticodon of a tRNA caused the wrong amino acid to be inserted everywhere it occurred? And what effect would that have? So the question — which is not easy to answer — is really, how did the alternative genetic codes ever evolve?

@MaximilianPress >what do you mean by "vast diversity of genetic codes"< Explaining in detail: Think that all living organisms evolved from a single cell which had some DNA. Now, without variation, that single DNA would get replicated exactly as it is, and all that would exist on earth today would be single celled organisms. Because of variation, that DNA changed to large diversity of genomes unique to each organism.

@MaximilianPress My question: If tRNA is formed from DNA, then it should be subject to mutation and crossing over. That, in my opinion, should lead to an unique genetic code in each organism, which is not the case.

@David, do you mean "there obviously may have been mutations of tRNA anticodons, and that would effectively kill the organism in which that mutation occured."? If that is what you mean, what if a human zygote undergoes such a mutation? If that isn't what you mean, please explain more.

Comments are not places to answer questions but to suggest improvements in them or ask for clarification. Improvement (or withdrawal) of your question can, in my opinion, be made if you think a little more about the consequences of your proposition. I say nothing about what "obviously" must have occurred, only ask you to think of the consequences. How many mutations would a mutation in the anticodon that changed the amino acid specificity potentially cause in the case I mentioned? One, 1000, 5000? Could an organism (zygote or whatever) survive the larger numbers?

And you might edit your title to express what your idea properly. Transfer RNAs of all organisms are not "the same" — the relationship between anticodon and cognate amino acid conforms to the standard genetic code in most cases.

@MaximilianPress — Whether or not your edit makes it a good question, it clearly does not reflect the concern (albeit misguided) of the OP. And ribosomes and tRNA synthetases are red herrings. A more interesting case study would be of the original amber and ochre mutations that led to the identification of stop codons.

@David Following this over-long comment discussion, I see OP as being principally concerned about stability of the genetic code across evolution. From this point of view, I see the edit as directly reflecting the concern of the OP. I believe that a fixation on tRNAs is an over-reductive view on this very complex problem. If you or OP would like to roll back and propose a more appropriate edit, I invite you to do so.

@MaximilianPress thanks for the edit. It exactly is my concern.

@AsmitKarmakar Thanks for confirmation. I have now attempted an answer, as I think that this is easier to answer.

There are a variety of posts on this site already that address related questions:

History of ideas about the form of the genetic code
Why 20 amino acids instead of 64?
Is there recent info about the hypothetical ancient two-codon genetic code?
How did the genetic code evolve?

I don't believe that any of these answers specifically address the high conservation of the code, except as implicit (though I could be missing something).
A useful paper
However, one such post links to this paper that discusses the evolution of the genetic code.
Part of the introduction restates the question's motivation rather well, I think:

The fundamental question is how these regularities of the standard code came into being, considering that there are more than $10^{84}$ possible alternative code tables if each of the 20 amino acids and the stop signal are to be assigned to at least one codon.

In other words, why has this space of $10^{84}$ codes not been more widely explored than we observe in nature?
The first part of the paper's abstract seems relevant (I have bolded a few sections that might be taken as explanatory hypotheses):

The genetic code is nearly universal, and the arrangement of the codons in the standard codon table is highly non-random. The three main concepts on the origin and evolution of the code are the stereochemical theory, according to which codon assignments are dictated by physico-chemical affinity between amino acids and the cognate codons (anticodons); the coevolution theory, which posits that the code structure coevolved with amino acid biosynthesis pathways; and the error minimization theory under which selection to minimize the adverse effect of point mutations and translation errors was the principal factor of the code’s evolution. These theories are not mutually exclusive and are also compatible with the frozen accident hypothesis, i.e., the notion that the standard code might have no special properties but was fixed simply because all extant life forms share a common ancestor, with subsequent changes to the code, mostly, precluded by the deleterious effect of codon reassignment.

They then review a variety of evidence for each theory, from which I will present one example.
Is the genetic code optimal?
They note a 1991 paper that used biophysical properties of amino acids and estimated that a randomly selected genetic code was ~0.01% likely to be at least as robust as the existing genetic code. In other words, the code seems to have at least somewhat minimized possible errors. So, the existing code is at least well above-average in terms of possible codes, and it's possible that the reason no better codes have been explored is that it's trapped in a local minimum of the code landscape (see Figure 3 from that paper here).

They go on to discuss this in more detail. I personally find other evidence about the "coevolution" and "collective evolution" theories interesting, but obviously it is hard to repeat a multibillion year experiment.


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Getting some concept of cancer genetics
I have two groups of patients : Responders to chemotherapy and non-responders to chemotherapy. I treat this as a dichotomous outcome we can call response to chemotherapy.
For a fixed set of cancer driver genes, I have calculated cancer cell fraction (ccf) for each group.
Does anyone know of any interaction or relation of response to chemotherapy and ccf?

Can you clarify how cancer cell fraction is derived from what you call "cancer driver genes"? I understand ccf to be the proportion of cancer cells among all cells, which is not directly related to driver genes. Are you estimating ccf somehow from driver gene copy number or allele frequency or something? That might lead to a different interpretation of ccf than other ways of estimating it. Can you link to a method or other tool? I found one possible method but I assume there are many others: https://www.nature.com/articles/s41467-020-14351-8

I have estimated ccf from variant allele frequency

Assuming that my comment is trivial, I think that there is some literature on this question. I'm assuming that the ccf in question is for the relevant tissue type for the cancer.
You might also consider cross-posting to SE Medical Sciences, where people are more familiar with these subjects.
Of course, naively one would expect that a larger ccf would yield a larger possible population of cancer cells to provide "escape" mutations or resistance to chemo, so high ccf should then be negatively predictive. As an extreme example, a highly metastatic cancer will have a high ccf over the whole body, and will be more resistant to chemo just because that's a highly advanced disease.
However, as usual things are a little more complex. The ccf itself has subpopulations. The following I got by just googling around a little bit. For some of the fundamental thinking on this, see this old paper. In this paper, the author puts emphasis for example on the fraction of cancer cells in each stage of the cell cycle; this results in what the author calls "clonogenic" and "nonclonogenic" populations that respectively yield new clones of cancer cells or do not yield new clones. Probably that terminology is somewhat outdated, but it gives a sense of the problem; related are "tumor stem cells". For a more modern treatment, you can look at this.
For a med school lecture treatment, see here to learn a little bit more. For an example of a recent prediction study which uses omic data to predict chemo response, which might be helpful in reformulating your study, see here.
In other words, not all tumor cells are created equal, some are more likely to resist chemo than others. However, overall I would expect that there is probably some kind of relationship between ccf and response to chemo, because they are both related to the mediator variable "number of clonogenic cancer cells"; it's just a somewhat oversimplified model compared to the actual biology, so it looks like the publications in question don't tend to focus on it.


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Photo attached of flies ‘injecting’ into spider egg sacs. Why? What’s going on here?
Brisbane, Australia.
While no image is available, direct observations were made of flies doing something to the egg sac.

Welcome to Biology.SE. What makes you think the flies are 'injecting' something into the structure? Also please give us any information you have relevant to species-identification — in particular it could be helpful to know the geographic location as well as the ids of the species involved (if known).

Possibly cannot see in the photo, but I could see each fly had a part of themselves extended from their rear end going into the egg sacs.        She is a ‘tent spider’ and that’s how they lay their eggs. Each ‘bump’ is a sac of eggs.

Cyrtophora moluccensis- tent spider.
Injecting? I could see something from the rear end of the flies going into the sac. (Sorry, Not clear in photo).                                        Location: Brisbane Australia

(Converting from comment)
Based on internet searches, it appears likely that the fly is Sarcophaga arachnivora, which lives off of spider egg cases in its juvenile form; the fly is laying eggs.
If you follow that link you will see references to a couple of papers that describe the species in more detail, though I don't believe I have access to them.
The geographic location noted there of SE Australia agrees with your observation of the organism.
References about tent spiders also make note of parasitic "flesh flies" so the species interaction appears to be an established phenomenon.


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Destroying RNA of viruses using Ribonuclease
I wonder if it is possible to design some Ribonuclease to destroy only specific RNAs (like those of viruses). Then, if virus tries to infect, his RNA will be cut.
Or, instead of creating Ribonuclease, we can design suitable mRNA and let the cell do the rest.
Is this possible?

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The human body basically does this on its own. We express various RNAses, some of which appear to have specific antiviral or bactericidal roles.
(This appears to be due to both RNAse activity and to biochemical properties of the proteins independent of RNA digestion)

How about creating some drug based on this technique? Could this help fight the infection?

@Dzery I would say rather that it does help fight infections, as part of normal human biology. And sure, you could package these RNAses up into pills or in an aerosol spray or something. I don't know of any specific examples though.

that's the point - maybe it's a way to create a new type of drug?

CRISPR/cas is used for exactly this purpose in bacteria.
The CRISPR array contains sequences from bacteriophages, which will prime various cas-nucleases to cleave either the DNA or the RNA of the virus.


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What's a cells membrane potential without any leak channels?
Consider the following ion concentrations on either side of a cell membrane (in = inside cell, out = outside cell):
$[\text{Na}^+]_{\text{in}} = 10mM$,
$[\text{Na}^+]_{\text{out}} = 142mM$,
$[\text{K}^+]_{\text{in}} = 148mM$,
$[\text{K}^+]_{\text{out}} = 5mM$,
$[\text{Cl}^-]_{\text{in}} = 4mM$,
$[\text{Cl}^-]_{\text{out}} = 103mM$.
Let us further say that there are no leak channels or voltage-gated channels
in the cell membrane, and also that ion pumps stop working once they achieve these concentration gradients (i.e. the state is at equilibrium with respect to the ion pump.
There is a greater net positive charge on the inside compared to the outside. This should give me a positive membrane potential when I think about the cell membrane as a capacitor.
I know, Nernst and Goldman equations say otherwise. (For my example it would be zero membrane potential due to zero permeability).
Can someone explain why there is or is not a positive membrane potential, with appropriate sources?
There are already multiple questions with a similar background but none of the answers are satisfactory.

can you say anything about why the other answers are not satisfactory, and link to those questions?

Goldman equation is not applicable in this case. If you have a non-permeable membrane, all the permeabilities of the ionic species ($P_x$) are zero, which leads to an indeterminate form of the argument of the logarithm in the Goldman equation. The logarithm does not equate to $0$. Consequently, Goldman equation cannot be used to find the membrane potential ($E_m$) in this case.
Nernst potential is the potential when the ionic species are in thermodynamic equilibrium (i.e. the diffusive force and the electrostatic force are balanced) and there would be no net flow of the ionic species across a membrane even when the membrane is permeable to the ion. This again is not the case for your impermeable membrane since there is an "unbalanced" force which will cause a net ionic flux given some permeability.
The usual equations of a capacitor, however, are perfectly usable given that you know the spatial arrangement of the charge inside and outside the membrane (i.e. if the ions are closely pressed to the membrane or distributed in the cell and outside).


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Inferring mother's allele probability based on father and children
I am currently in the process of analyzing our family's genome. I do have access to his, mine and our father's genome, but not to our mother's (deceased). I was wondering how I could combine our 3 genomes to infer information about our mother's.
With regard to two different genes I am faced with these two situations:
Gene 1:

Father: GG
Brother: GG
Me: GA
Mother: Likely AA, less likely GA but most likely not GG or otherwise I would also have GG?

Gene 2:

Father: GG
Brother: GG
Me: AA
Mother: No idea, if she had GA I would have at most GA, if she had AA my brother shouldn't have GG. Does this mean a mutation is involved?

Thanks in advance!

Gene 1: Mother was almost surely GA (she couldn't had been AA because then both children were GA). Gene 2: Mutation or some other non-Mendelian inheritance or ... well ... different father.

In a basic biology class one of the first steps in understanding Mendelian inheritance is always building Punnett squares. I recommend you play around with those, with all your different guesses about mothers, to figure out what makes sense and doesn't. You'll learn more that way than asking someone else to give you the answer.

need to consider haplotype  (https://www.nature.com/scitable/definition/haplotype-haplotypes-142/)


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Testing primer for misspriming in 770 Mb lepidopteran genome
I designed primer pairs for a lepidopteran genome (Cydia pomonella, codling moth, ~770 Mb, ~60% transposable elements, Reference Genome Paper). I have around 60 primer pairs that bind to a specific chromosome and now i would like to test for their specificity, meaning i would like to test whether or not the primers are mispriming somewhere else in the genome. I tried it using Geneious prime, but to test all primers for all chromosomes takes a lot of time. Then i tried the Primer-blast tool from NCBI, but unfortunately Cydia pomonella is not listed as an available target genome.
Do you have any experience on how to test the primer specificity on (large) genomes in a reasonable time? Another thought would be to blast each primer and see if it is aligning an known transposable element (if yes: discard the primer) and then ultimately try to optimize the PCR itself. Is there another way to test beforehand?

I added and then deleted an answer that says primer3. Unfortunately that does not appear to be true. I would suggest looking around for a blast-based toolkit that accepts bulk input for this purpose. I was not quickly able to find one but did find this helpful page with information about blast command line parameters that might be useful.

do you know biostars.org ?

Thanks for your suggestions, i will have a look in both :)


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What is the difference between Haploinsufficient and Autosomal Dominant mutations
I have been reading a paper that classifies genes in different groups by the type of disease-causing mutations. The categories of mutations (alleles) it gives are:

Haploinsufficient
Autosomal dominant
autosomal recessive

I was wondering what is the real difference between haploinsufficient and autosomal dominant.
As far as I know, a Haploinsufficient gene is the one that one copy LoF mutation produces the phenotype. This seems to be exactly the same as an Autosomal Dominant disease???
Can someone give me an example?

Genes can be haploinsufficient, but genes cannot be autosomal dominant, only mutant alleles can be dominant or recessive.  If m/+ has a wild type phenotype then the mutant allele ‘m’ is recessive (recessive to the wild type allele ‘+’).  However if an m/+ heterozygote has a mutant phenotype then the mutant allele is dominant (dominant over the wild type allele ‘+’)
Dominant alleles are usually, but not always, GoF (gain of function). If a gene is haploinsufficient then a heterozygote with a LoF mutant allele over wild type, m/+, has a mutant phenotype, and is dominant but is not a GoF. The classical test is when you have a deletion or deficiency of the gene, and still have one good copy, but still get a mutant phenotype.
The autosomal dominant alleles you refer to then have to be GoF, either hypermorphs, antimorphs or neomorphs.
In the model organism, D. melanogaster, the genes Ubx (Ultrabithorax) and N (Notch) are both haploinsufficient, because a heterozygous deficiency of either of those genes have their respective mutant phenotypes.  Examples of autosomal dominant mutations in humans are the classical oncogenes, for example the mutant ras gene alleles that cause bladder (and other) cancers.

In human genetic diseases there are in fact autosomal dominant mutations which upon detailed molecular analysis turn out to be loss-of-function alleles of a haploinsufficient gene.  Autosomal dominance is a pattern of inheritance defined by a phenotypic pedigree, it is not a molecular mechanism underlying biological function.  Haploinsufficiency, on the other hand, is a description for dose-sensitive genes where two copies of the gene are necessary for a wildtype phenotype.

Haploinsufficiency would be like a red and white allele making a pink flower.  If the red allele were totally dominant, a flower with one red allele would look exactly the same as one with two red alleles

In your example, if the red and white alleles are both dominant gf (gain-of-function) mutations, then what you have described, a flower where both phenotypes are present, is called codominant.  A classic example of codominance is in the mating type locus of the baker’s yeast S. cerevisiae. a/a diploids, alpha/alpha diploids, and a/alpha diploids each have different phenotypes. I believe Ira Herskowitz wrote a small review article about this as an example of codominance.

Typically, a white allele would be a loss of function; the pigment doesn't get made.  A pink flower is pink because it makes less red pigment than the flower with two red alleles.

The question is about the difference between haploinsuffieicny and complete dominance.  Yeast mating types are neither.  Co-dominant situations are neither.


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Why are multiple copies of the 35S enhancer used for overexpression in plants?
I know that the CaMV 35S promoter is widely used for transgenic plants, where it acts as an enhancer element for constitutive overexpression.
I noticed that it is always used as a tandemly repeated enhancer with 4 copies.
Why 4 copies? Why not 1, 2, 3, 5, or 10?

When the enhancer was first studied it was obviously in a single copy.
Later work found that tandem duplication (2 copies) increased expression levels greatly. This 2-copy construct was then used in some studies.
Here is a quote from a 2003 paper (Ma et al. 2003 Nature Reviews Plants):

The CaMV 35S promoter is now the most popular choice
in DICOTYLEDONOUS PLANTS (dicots). It is a strong constitutive
promoter that can be made even more active by duplicating the enhancer region.

By 2009 the 4X35S is showing up in publications but I have not had any luck tracking down its origin.
So in brief: the exact number is because of history. Earlier on they used fewer copies.
There are probably diminishing returns to enhancer strength with more copies, and fewer copies is not strong enough.


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Difference between in cis/in trans and in phase/out of phase
Something I've never been quite clear on is the difference between in cis/in trans and in phase/out of phase, as used for the genetics of diploid organisms.
My understanding is that the in cis/in trans terminology is used specifically when referring to the configuration of recessive and dominant alleles for a pair of genes, while in phase/out of phase is used when referring to a pair of individual variants, such as SNPs or indels, however it would be great to get an authoritative clarification on the issue.

Like all words, the meaning of terms like these strongly depends on context — quotes (with appropriate citations) would probably be your best bet for getting a clear answer, but at very least you need to provide more information than "in diploid organisms".

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Unless there are other meanings of the terms I'm not aware of, they are referring to slightly different things.
Haplotype 'phase' refers to whether alleles are found on the same chromosome (paternal or maternal). Hence, 'phasing' refers to the statistical / read-based process of determining which chromosome a genetic variant is found on. 'Unphased' data, typically produced by genotyping arrays or sequencing technology (e.g. short Illumina reads), doesn't contain information about which chromosome a variant is on.
In some contexts, cis/trans terminology refers to whether a locus (e.g. a eQTL/pQTL) is found near the gene it corresponds to. For instance, say you run a GWAS on protein levels and find a significant hit for a locus controlling the level of the protein X. If this locus is within some kind of threshold distance of the gene encoding protein X, perhaps 2Mb, then it is known as a 'cis' ('on the same side as'). Otherwise, if the locus is found distal to gene encoding protein X, it is known as a 'trans' locus. Similarly, 'cis' regulatory elements are regions of non-coding DNA which regulate the transcription of nearby genes.
For example, from this paper:

In the cis-eQTL analysis, we evaluated the associations between lncRNA
expression levels and the genotype at a given SNP locus located within
1 Mb upstream and 1 Mb downstream of lncRNA. trans-eQTLs are defined
as associations beyond the 2-Mbp interval.

I took a closer look a number of Wikipedia articles, including those you linked to. The Latin root of cis/trans means this side of/the other side of, which calls to mind pairs of chromosomal homologs, however modern usage of the terminology appears to mean something more like same molecule/different molecule. Whether or not acting "in cis" requires that the action is in some way facilitated by the genetic elements all belonging to the same DNA molecule is left unclear, and "in trans" is still sometimes used for distant but still chromosomally co-located genetic elements.

Can you provide a citation for the use of cis/trans that you give — you seem to be saying it is primarily about proximity, which conflicts with the usages I've encountered in biology (and chemistry) ...

@tyersome could you be more specific about what use I refer to that you're interested in more information about? If you mean the use stated in my original post, then just go on YouTube and type in "cis trans genes", the top hits will all be videos explicating the so called cis/trans configurations of recessive and dominant alleles.

@Thoth given there seems to be different definitions of cis/trans, you should really give the context with which you are asking this question.

@user438383 in the context of genomics/genetics.

Ok well my answer is in the context of genomics.