CharonY

Which system/protocol is used for overexpression and isolation?

Nope. Backteria are prokaryotes but not all prokaryotes are microbes. All prokaryotes are microbes (which is not a taxonomic unit, though) but not all microbes are prokaryotes (mostly fungi are and sometimes other small, usually single-celled organisms are called microbes (as mentioned, they are not a real taxonomic unit, but rather a unspecific phrase). An improtant point regarding organelles is that their evolutionary history (and future) since the merger is directly tied to the organism in question. This is not the case for the other bacteria colonizing their hosts.

Looks like that to me, considering that evidence of life outside Earth is mostly rather weak and heavily challenged (especially the analytical evidence).

The purpose of PCR-based diagnostics is not necessarily to find out what infections you may have, but rather if you have the specific infections X, Y or Z. So even if you or some food stuff is infected by, say Salmonella, Bordetella, Campylobacter, using primers specific for Salmonella will only tell you whether that is present, but will not tell you anything about the other species. If your question is how one can be sure, that those primers do not cross-react, then the answer is that they are only specific within the known genomic space (i.e. the complete known genome sequences of all species). While they are tested for cross-reactions, it is of course theoretically possible that there are some other not yet identified bacteria around that may also have that particular sequence.

As a rule of thumb, if you repeat a given experiment or trial (i.e. all parameters are the same) and end up with different results for each data set, the chances are high that your sample size was too small. A larger set (by pooling) is required to demonstrate that whether there was an effect or not. If the experiment was set up (deliberately) different, they have to be treated as coming from different sample populations. However, if you suspect something was off, but do not know what, you still would have to pool the data in the hope that whatever bias you may have introduced gets drowned out, since you cannot infer which of the two trials reflects the true population best. If you just took one of these results, you would just be guessing and that defies the whole purpose of doing statistics. In short, if both experiments are conducted the same way, treat it as one sample population. Even if you suspect something is off, but don't know why treat them as one. If you know that there are technical differences (e.g. older animals, different test parameters etc.) you could do them differently and say e.g. under condition 1 we found no differences, however by altering the experimental parameter X to Y we found a significant difference in whatever. The important bit is that even you expect something to happen but do not see it, do not try to fix your data or statistical analysis in such a way that it fits your expectation. Again, that would be bad science and defeats the whole purpose of experimentation and statistics.

In order to change something as complex as an eye massive changes into a large number of genes involved in developmental functions are necessary. Considering that every gene is basically involved in a number of functions (to put it simple) each change would result in a boatload of other, unforeseeable changes. Expressing a simple, not very interactive gene (e.g. the gene for GFP) is relative trivial. Something that changes metabolic or developmental functions usually result in very complex changes to the organism and more often than not are very detrimental to the carrier.

Assuming that all trials were done identically, one would use the different trials essentially as coming from the same sample population. In other words, you would pool the data and make one single test with all data combined.

What, precisely, is your template? A cDNA library or a single cDNA template, for instance? Or purified RT-PCR products? A quick check (prior to sequencing) to see whether the amplificate is correct is doing a quick restriction analysis, btw.

Basically yes. Most of the time it is a small part of a gene (though technically it can be any locus that is specific enough). Also, most of the time qPCR is done.

As an additional note, many dislike the "x"-buffer notation, as sometimes errors are in the actual recipe. It is preferable to a) indicate the molarity and b) the pH. Only exceptions are pre-made buffers, in which case the manufacturer should be stated.

There are a number of possible scenarios, one is extensive exchange, another one would be the prevalence of a given strong selective pressure, which is absent in the other population(s) and so on. Any population boundary is to a given extent arbitrary and should be used to best reflect the hypotheses to be tested. For instance, nations appear to be a good delimiter. However, depending on the geographic size and distribution certain sub-populations are very likely to cross borders. Depending on the way samples were obtained, bias may be introduced. Islands tend to be easier, but then Japan has quite a few of them and so on. From the way the question is outlined it appears that there is also the risk of multiple hypothesis testing here (just as a sidenote).

Highly unlikely. Even with sci-fi genetic engineering, we as organisms are confined to certain biological realities. In fact, only a perfectly static equilibrium would essentially be free of evolutionary inputs.

It depends very much what you want to test, of course. If, for instance you want to test difference in allele frequencies in two populations, merging two subgroups are going to increase the variance, and hence reduce the sensitivity of the test. On the other hand, it may be important, not only for the sake of statistical power, but also for the kind of hypothesis that you are forming, to combine them.

More complex immune system as ours requires cell populations to interact. Moreover, the DNA is clearly not a central control center. In fact, nothing like this exists in a cell. Regulation is fairly distributed and occurs on basically all levels. The DNA is, alone, probably the most inert stuff within the cell. All the information is maintained and acted upon primarily by proteins in conjunction with metabolites and the DNA itself (to put it very simply and horribly inaccurate).

That is still science fiction. What you may be thinking of are nanomaterials that are used to deliver cytotoxic compounds efficiently to cancer cells. Whether they are of clinical value remains to be seen.

The different polymerases have different capabilities, e.g. with dealing with DNA defects (either detection or bypassing "roadblocks" due to lesions) and are required at different stages in the life of the cells.

The important bit is that single-cell signal processing is very limited as opposed to multi-cellular signal processing. Due to physical limitations there is far less plasticity in the regulatory networks. Sure, one could use similar words (like sensors) but the scope and potentials are vastly different. In the end, it is basically a scaling issue.

Also, the premise is flawed. Our ancestors did have to cope with a lot of environmental pressures. Tool use is only one of the ways to cope.

You were asking for algorithm that are used for DNA or AA matches. From your own link you will realize that there the type of information is relevant, specifically for the scoring matrix. Of course, the problem can be generalized to general pattern matching, however, other algorithms or different implementations of the algorithms used for DNA and protein alignment are more useful than that as Arete mentioned. Maybe to illustrate: compare Amino acid sequence SPT to SPT. Apparently no difference. However, let us say that on the DNA level it is TCT CCT ACT versus TCC CCC ACC. You will note that despite the same AA sequence at least three substitutions happened. The scoring matrix assigns that a specific distance. Or another example. Assume you have a substitution of S to I on the AA level. If you just measure letter exchange it would be the same as, say C to W. However, in the first case the exchange may have been anything from TCU/T/A/G to ATT/C/A (so at least two substitutions). However from C to W it would be from TGT/C to TGG (so only one substitution needed). The substitution matrices take these possibilities into account (so funnily the Blosum62 had an error in it, but BLAST performed better with the error). However, for an arbitrary string in substitutions do not follow a specific rule (as in this case that of DNA mutations) the distance would be based on a totally different measure, or could be binary.

This does not make much sense to me. Why would you want to convert integers into DNA/AA sequences? Conversion from DNA to AA creates a specific context (governed by the genetic code, as well as likelihood of certain point mutations). If the integer sequence was obtained from another source, the whole context does not apply (i.e. it does not matter what you do, it would be arbitrary in any case).

1) Plastids possess their own DNA (you may be confusing coding regions with mRNA) 2) likewise the plastome refers to the totality of the genetic material in plastids (i.e. total DNA). The totality of its transcripts would be the transcriptome (I do not think that someone invented a specific term for the plastid transcriptome).

I don't get it.

Note that if we use the term "junk-DNA" for all non-coding regions, it would also include regions with well-known functions. Examples include ribosome binding sites, promoters, operators etc. On a point that Greippi touched on, the three-dimensional arrangement of DNA also influences the and rate with which for instance coding-regions are transcribed. One simple example are enhancers. In this case regulatory elements initiate a kind of loop or fold in the DNA that allows the polymerase to bind more efficiently and hence, increases transcription initiation. This effect is dependent on the distance and geometry of the loop, which, in turn, can be controlled by the length of the sequences between the enhancer and the promoter region. Then there is the condensation of chromosomes (i.e. euchchromatin and heterochromatin dynamics) that is affected by length and much much more. The DNA molecule is actually highly dynamic and non-coding sequences are highly involved in that.

Ah the FASTA algorithm (which is also the origin of the fasta file format, forgot about that). Right, that one is actually based on Smith-Waterman (IIRC). The required file formats are dependent on the software. The majority of alignment tools use fasta (the file format). This is clearly not a biological issue, and I move it to the computational section.

That is a false premise. While tool use may spread within the population, it is not a certainty that proficiency will increase. Anatomic limitations are a simple counterexample. And of course tool use often spreads non-genetically within a population.