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Modified Mendelian Ratios Problem
I'm studying modified Mendelian ratios. I've got a theoretical problem that I'm stumped on:
A species of bird may have normal wings, or a mutation that causes frazzled wings.
I do 15 identical breeding pairs, crossing a male bird with frazzled wings to a normal winged female. The progeny obtained are 46 with normal wings (22 male/24 female) and 23 frazzled, all male.
Afterwards, I mate the normal winged females to the normal winged males obtained from the above cross. The progeny obtained all have normal wings.
I figured out a possible solution, and would like to know if it makes sense:
Z(f) = Z sex chromosome with normal wing gene, recessive
Z(F) = Z sex chromosome with frazzled wing gene, dominant
W =  W sex chromosome

Z(F)/W = abnormal wing female bird (does not survive as not enough
gene product produced)
Z(f)/W = normal wing female bird
Z(F)/Z(F) = abnormal wing male bird (does not survive as not enough gene product produced, or possibly survives as just enough is produced by 2 mutant genes, doesn't matter anyway)
Z(F)/Z(f) = abnormal wing male bird (survives as wildtype gene produces enough product)
Z(f)/Z(f) = normal winged male bird

My crosses:
P1: Z(F)/Z(f) × Z(f)/W
Progeny: Z(F)/Z(f), Z(f)/Z(f), Z(F)/W (fails to survive), Z(f)/W
F1: Z(F)/Z(f), Z(f)/Z(f), Z(f)/W in 2:1 normal:frazzled wings
F2: Z(f)/Z(f) × Z(f)/W produces Z(f)/Z(f), Z(f)/W

This would account for the phenotypic ratios seen in the problem, and also uses the ZW sex chromosome system found in birds. The genetic explanation would be that a single Z(F) does not produce enough product to thrive, for whatever reason. The trait is sex-linked dominant on the Z chromosome.

Can you shorten your question. Just keep the latest part that you want to ask with minimum required background.


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Wasp nest destroyed by some big wasp like insect: is that the queen?
I had a small wasp nest in my bathroom. Initially there were only two to three wasps but the number increased to eight.
Today morning a big wasp-like insect (shown in the image below) came to the nest. I thought that it was the queen but it ate all the larvae and destroyed the nest.
Can someone please tell me if this is a normal behavior of a wasp? Is the "big wasp" their queen or another species?

The wasp is a Ropalidia marginata
The assailant is a greater banded hornet .
There is a video of one attacking a wasp nest here: https://www.youtube.com/watch?v=BPLRpEkNv8Q
Hornets can attack wasp and bee hives and decimate them. In europe there is an invasive species of hornet from japan which is twice as big as the european hornet.
The abdomen of the hornet is about half of the size of the animal. The abdomen of the queen Ropalidia marginata is about 1/3rd of the animal, she has very similar markings as the hornet, it seems to have evolved as a copy cat that consumes larvae. http://drkrishi.com/ropalidia-social-wasp/
You must add location information to search for animals by region and find the species. I only knew it was india/pak because of the avatar.

It is in India Bangalore. Thanks for the help!

What kind of individual is wasp photo you give? It doesn't have the black and yellow pattern you mention about the queen.

If you see the photo from the question, it is the small wasp on the right of the nest, you can see the similarity. the pics of the queen from the same species are on the page by drkrishi.

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Original Lotka–Volterra model
I am looking for the articles in which Lotka and Volterra published their original predator prey model. I couldn’t find the model presented in many textbooks in Lotka’s book Elements of Physical Biology or Volterra’s paper Fluctuations in the Abundance of a Species considered Mathematically.
Is there really Lotka–Volterra model for dynamics of two  competing species? On Wikipedia and Murray’s book Mathematical Biology: an Introduction, it is written that it exists but I couldn’t find it in their papers.

Volterra’s Variations and Fluctuations of the Number of Individuals in Animal Species living together features what is nowadays often called the generalised Lotka–Volterra model.
Thus it captures competition between and with populations.
In particular, Section 2 is titled:

Biological Association of two Species which contend for the same Food.

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Can a second mass extinction by microbes happen?
Science Daily reports that the mass extinction at the end of Permian period happened by the Methanosarcina archaea wiping out 90% of species:

Methane-producing microbes may be responsible for the largest mass extinction in Earth's history. Fossil remains show that sometime around 252 million years ago, about 90 percent of all species on Earth were suddenly wiped out […]. It turns out that Methanosarcina had acquired a particularly fast means of making methane, and the team's detailed mapping of the organism's history now shows that this transfer happened at about the time of the end-Permian extinction.

Is it possible by the global warming and all the spores of bacteria come out from melting glacier or anywhere else and mass extinction occur?

Worth considering that when reasons for a mass extinction are investigated the answer is often "All of the above", because anything that changes the environment contributes. we have for instance also found massive volcanics and a massive impact crater at the same time.

The first problem here is that the theory that the P-T extinction was caused by bacteria (rather than bacteria being a possible effect/feedback) is contradicted by LOTS of evidence, primarily the Siberian Traps eruptions igniting large coal beds.

First of all, let’s consider your Methanosarcina scenario in specific.
Methanosarcina with those properties are still around. So, there is no reason to expect that introducing some ancient Methanosarcina into a suitable environment today would do very much – as they should already be there. Any existing (large-scale) environment providing a niche for Methanosarcina should already be occupied by it as it only needs a single organism to get there by chance at some point.
Moreover, there must be some reason why our atmosphere stopped being filled with methane by Methanosarcina.
For example some prerequisite resource could have been depleted, some other organism evolved that consumes this methane or controls the Methanosarcina population by feeding on it.
Whatever this reason is, we should expect that it still applies: A required resource would still be gone and a second organism controlling this activity should still be around (or resurface together with the Methanosarcina in question).
In fact, the hypothesis you mention maintains that the nickel required by Methanosarcina to produce methane was available in huge amounts due to specific volcanic activity at that time.
Finally, the current ice masses of the Earth are much younger and have not been around since the Permian period.
In general, if geologically cryostoring microbes were relevant, climate change should be the least of our worries as drilling a hole into the ice or local geologic activity could suffice to bring back a few individuals – which is all that would be needed.
Now, what about unfreezing microbes in general?
Due to lack of fossils, we have little knowledge about microbe extinction, except that it does happen.
However, my ecological intuition is that microbes either lose their niche to a fitter alternative (which either be more apt at causing problems or avoid them resurfacing) or survive in small amounts (ready to occupy their niche if it should resurface).
Microbes are considerably different from higher, sexually reproducing life forms here as it only needs a single individual to revive the species and individuals can survive for a long time in adverse conditions, not only by sporulation but also because they need little energy to maintain their state.
Finally, if a niche for disastrous microbes exists, there is the constant risk that it will be filled by some ordinary microbes evolving, which brings us to the next point.
Something that is conceivable to happen is that some microbes spontaneously evolve to fill a big niche and have some disastrous by product leading to a mass extinction.
It is hard to estimate this risk, but as far as I know, there is no historic precedent after the evolution of higher life forms.
(As elaborated above, the Methanosarcina hypothesis for the Permian extinction features additional factors.)
However, I consider it unlikely that climate change increases this risk.
While climate change undoubtedly has disastrous ecological consequences, tilting individual ecosystems, I don’t see how it creates some fundamentally new environment (that wasn’t already there in similar form somewhere else on the planet) that would stimulate a spontaneous disastrous microbial evolution.

"a fitter alternative (which would be even more apt at causing problems)" I think I've heard specialists mentioning that being fit, for a microbe, entails not causing too many problems.

@bli That is only true for microbes that live inside another organism and depend on it to stay alive.

@jpa: Not necessarily.  Consider for instance a microbe that produces oxygen as a waste product, but can't tolerate high concentrations of environmental oxygen.  So as the Great Oxygenation Event gets under way, those microbes either mutate to tolerate oxygen (with the intolerant dying in large numbers), or they become extinct.  Or consider yeast fermentation: at some point the yeast produces an ethanol concentration that's toxic to them.

@jamesqf: Well, if we have something like the great oxygenation event, the oxygen producing microbes were fitter as far as the answer is concerned. More generally, my point is if microbes go extinct, it is because their niche is taken by some other species, which should either increase the problems (if the niche has gone too and then re-emerges) or voids them (because the dangerous microbes have no niche to go to). Also see my edit.

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Ecology: Indices to calculate species overlap in a community?
Experimental design: I sampled the abundances of four species for each month of the year. I did this across 6 sites. I want to calculate the temporal niche overlap for all the species at each site. Ultimately, I want to compare overlap across sites.
I want to compare the extent of overlap of species abundances in the following two secanrios. In the first scenario, species barely overlap so the index or overlap value should be zero. in the second case species fairly overlap in their abundances as shown below (Note: My data has absolute abundances not relative. The graph is only for representation).

I found Pianka's index (from Gotelli 2000) to be one such index that estimates the extent of overlap. However, on closer reading, I find that takes the resource utilization matrix as input and returns the average pairwise Pianka's niche overlap index averaged over each species pair as so:
$$O_{kl} = O_{lk} = \frac {\sum\limits_i p_{ij}p_{ik} }
    { \sqrt{\sum\limits_i p_{il}^2 \sum\limits_i p_{ik}^2}}$$
I am not sure if I can use it to compute the overlap of species abundance distributions alone. I am wondering are there any other indices that I can use to do a similar computation? Is there a statistical route that I can take to capture a breath of overlap?
Specific questions (in response to Ben Bolker's comments):

I want to calculate a single number for the overall overlap. It has to be able to deal with 0s.

I would be curious to know the overlap coefficient for each species pair. My guess is that I can use a alpha-diversity index like Shannon’s diversity index.

Would it be incorrect to use Pianka's niche overlap here? Why can't I?

Might be handy http://www.zo.utexas.edu/courses/thoc/sppdiv.html

https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient ; can you be a little bit clearer about your experimental design? i.e., do you have a set number of observations over the course of the year (e.g. one per month)? Do you want a single number for the overall overlap, or a coefficient for every pair of species? (I'm assuming that you have "the abundances of 8 species", not "8 observations of species abundance for n species")

@BenBolker  I have updated my answer based on your question. TLDR: looking for a single coefficient for all the species. I have 6 different sites, so I am trying to compute the overall overlap (across the year) at each site. The Dice/ Jacard etc calculate for two species at a time not overall. Pianka or Czekanowski niche does across all species. One of my questiosn is why can't I use pianka's index here?

I am confused now: Do you care about niche overlap or pure temporal overlap? In the former case, you obviously need to take resource consumption (and similar) into account. For example, foxes and trees can exist simultaneously while having zero niche overlap. With that being given, what’s wrong with Pianka’s overlap index? In the latter case, why do you write niche overlap in your first sentence?

If you only care about direct overlap, this sounds like a case for classical correlation coefficients. These answer you whether a high abundance of Species A coincides with a high abundance of Species B. The known advantages and disadvantages of the respective correlation coefficients apply, e.g., ranked correlation coefficients being less sensitive to outliers.
Beware that when you want to determine the significance of your results, you need to take into account that your individual data points are correlated through time. For example, if Species A and B have a high abundance today, they probably also have a high abundance tomorrow. Therefore you cannot apply standard ways to obtain significance as those assume uncorrelated data points and you would commit pseudoreplictation. However, it is quite easy to implement a computational null model that preserves the temporal correlations while destroying the ecological correlations (i.e., between species): For example, you can randomly shift one time series if your data is cyclic (e.g., capturing a strictly yearly cycle) or sufficiently large that border effects do not matter. Or you can use time-series surrogates to create null-model data with an identical frequency structure.
Things become more complicated if you care about close overlaps, like Species A being abundant in the month after Species B was abundant. Here, you can use tools like cross-correlation, but this really depends on what exactly your constraints are and what you want to know.


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When does a virus become a different species?
Related:
When does one decide to refer to a virus as a new variant?
I've been thinking about all the news related to "variants" of the SARS-CoV-2 virus, including the infamous "Delta Variant". One thing that strikes me as odd is that there seems to be no discussion on the possibility of a variant of this virus evolving so much that it becomes a distinct virus species - something other than SARS-CoV-2 but clearly descended from it.
I know that biologists typically define a species as a set of organisms whose members can produce fertile offspring (that is, if two populations either cannot produce offspring or can only produce sterile offspring, they are considered two different species), but this definition would not seem to work here as viruses do not reproduce sexually.
So, is there a general definition or consensus on when a virus lineage has experienced sufficient genetic drift from its sister lineages that it is a brand new virus rather than a variant? Is it based on percentages of base pairs changed? Is it based on changes in the clinical effects of infection (e.g. if a human Influenza virus underwent a mutation that made it unable to infect humans but highly infectious to the spleens of small marsupials, then it would be a new virus, even if the mutation consisted of only a single base pair change)?
Somehow, I'm imagining that a future 31st century biology textbook is going to say something like this:

While SARS-CoV-2 is no longer with us, its descendants live on as a family of viruses that cause Exploding Camel Brain Syndrome as well as a distinct cluster of closely related species that cause muscle inflammation in dogs.

Take two coronaviruses and the closest relatives of both, if they have the same host species, same geographical distribution and are genetically close enough then they will experiment a lot of recombination and the phylogentic tree will become messy enough to make clear that they are not distinct species. Conversely if they don't have the same hosts or geographical distribution then they tend to be quite divergent genetically.

The general issue of what exactly a "species" is has been addressed numerous times here, in different forms. Some good answers can be found at:
Defining "species" (Are species an emergent property or an ensemble of quantitative differences?)
How could humans have interbred with Neanderthals if we're a different species?
When has an organism evolved enough to be called a new species?
and a so far unanswered question:
References for historical momentum in asexual species definitions
I think Remi.b's answer that says:

The concepts of lineage and clade / monophyletic group are much more helpful. IMO, the only usefulness of this poorly defined concept that is the "species" is to have a common vocabulary for naming lineages.

is the place to start. A virus "species" is just a label to a lineage at one point in time. When we discover separate lineages of a virus, historically they have gotten a new label. It's then possible follow the lineages from there into strains/substrains, etc.
SARS-CoV-2 was first identified as the cause of a novel illness with a placeholder name 2019-nCoV. Its relation to the earlier SARS-CoV lineage was quickly identified and it's now been grouped alongside SARS-CoV-1 as a strain of the SARS-CoV species. So, given that SARS-CoV-1 and SARS-CoV-2 are considered strains of the same species, it seems highly unlikely that any variants of SARS-CoV-2 will be known as their own species any time soon.
One could consider other virus lineages as well. Influenza A is a species whose subtypes are described by two antigens, H and N, giving the perhaps somewhat familiar naming convention like "H1N1" or "H3N2". Each of these has contained different lineages often named by those causing illness in humans or animals, but at no time have these been considered separate species, they are all still considered the same species Influenza A.
The naming conventions for influenza A are pretty solid from long-term study and following precedent. The International Committee on Taxonomy of Viruses sets some conventions for naming, but their principles are:

Stability
To avoid or reject the use of names which might cause error or confusion
To avoid the unnecessary creation of names

None of these attempt to put any standardized distance between what are considered "different" versus the "same" species across different lineages. The ICTV code says this in vague terms, bold mine:

The criteria by which different species within a genus are distinguished shall be established by the appropriate Study Group. These criteria may include, but are not limited to, natural and experimental host range, cell and tissue tropism, pathogenicity, vector specificity, antigenicity, and the degree of relatedness of their genomes or genes. The criteria used should be published in the relevant section of the ICTV Report and reviewed periodically by the appropriate Study Group.

So, different study groups choose which criteria to use, and these need not be the same criteria each time. Introducing any would likely result in this situation. Instead, we have a fairly ad-hoc system given there is no particular biological meaning to "species" except as a lineage label, and this system is governed as much by scientific history as by any set level of genetic similarity. In the interest of maintaining stability, it seems unlikely that new virus species will be recognized under existing known virus species without novel discovery.

ICTV says monophyly is the major characteristic: A species is a monophyletic group of viruses whose properties can be distinguished from those of other species by multiple criteria. from 3.20 ICTV code. Criteria include but not limited to Natural and experimental host range, cell and tissue tropism, pathogenicity, vector specificity, antigenicity, and the degree of relatedness of their genomes or genes

@bob1 Yes, but monophyly doesn't tell you where to draw the species line (as compared to some other level of classification). The other characteristics are vague and not consistently applied in different lineages.

I totally agree, you can randomly pull a branch out of the IAV H1 tree and call it monophyletic. Classifications in virus below about the family level are pretty wishy-washy in general. There are some where we can easily call it a species, but for many families it is very difficult.


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Can bacteria be killed by purely physical trauma?
In a question over on Skeptics Stack Exchange, I half-jokingly listed "blunt force trauma" as a means by which a bacterium could be killed. That makes me wonder if it really is only a joke or whether such an assault is plausible. The webcomic xkcd also did an episode on the possibility of physically attacking a cell (though not a bacterial one).
There are plenty of resources on how bacteria can die from biological processes (e.g. infection with a bacteriophage, biological effects of antibiotics, etc.), chemical attacks (e.g. membranes getting lysed with bleach), temperature (i.e. cooking), and radiation, but have bacteria ever been shown to be vulnerable to a direct physical assault such as being hit with a hammer, cut with a knife, thrown onto a hard surface, twisted until they burst, or being drawn and quartered?
I do know that many common weapons that are generally effective against animal life (swords, axes, clubs, assault rifles, etc.) do not have the exact same properties at the microscopic scale, but does that mean that physically beating a bacterium to death is absurd or is there a known way to kill a bacterium using only physical force?
To be clear, I recognize that our normal means of killing unwanted bacteria - cooking food to prevent food poisoning, treating bacterial infections with antibiotics, cleaning hard surfaces with bleach, applying rubbing alcohol to wounds, etc., are probably effective enough that most people don't bother pulling out that old Wild West six-shooter they have in the closet or the antique sword hanging on their wall the way they might consider them if attacked by a bear or wolf, but that's not the question.

What's the limit of what you'd consider physical trauma?  Bacteria can be killed with sufficient centrifugation, which is reverse blunt-trauma in that they are smashed against a comparably-large and comparably-blunt tube bottom

Does a diamond knife count?

@Joshua Only if it's a blunt knife.

@Punintended That counts.  Blunt force trauma isn't really a vector quantity, it's the resulting state produced by the application of a strong force.  Humpty Dumpty doesn't really care which reference frame you pick - either way he's broken.

I guarantee that any bacteria that found themselves in the immediate vicinity of a supernova would be destroyed. So, yes.

Through google found a similar question on Skeptics.SE Is it possible to crush bacteria?

Borat eagerly awaits the answer to this question

My (limited) understanding is that it is quite hard to avoid killing some bacteria even with very gentle physical manipulation. On the other hand, it is quite hard to use physical force to achieve reasonable level of sterilization. Let's bring some examples with a few (hastily found) references.
Pressure
My guess is that most examples the OP mentions (hit with a hammer, knife) will exert their biggest effect on bacteria through a peak in pressure - bacteria are comparable in size to width of a moderately sharp knife edge and thus there will be more "crushing" than "cutting" even with a knife (Science of Sharp discusses that Chosera 1k - a very good stone - will sharpen a knife to 0.5 μm, Wiki has bacteria at 0.5 - 5 μm).
I recently collaborated on a project where initial interpretation of results was wrong simply because putting a glass slip over a sample to inspect it under a microscope caused around 10% of bacterial (B. subtilis) cells to die. Putting a small weight on the slip resulted in roughly 80kPa additional pressure applied and ~ 80% of the cells dying within 25 minutes. But 80% death rate won't do much to e.g. reduce risk of food poisoning - as long as there are nutrients, bacteria will regrow quickly.
As m4rio mentions in a comment to another answer, and in line with The effect of high pressure processing on the microbial, physical and chemical properties of Valencia and Navel orange juice you need to ramp up the pressure to 100s of MPa to achieve high efficacy. And even that may not work so cleanly - e.g., the linked paper mentions that after applying 600 MPa, bacteria regrew to detectable levels after 4 weeks of storage at 4°C. Killing all (or almost all) bacteria is just really hard.
Because it was fun, I'll also link to study showing that High pressure-processed guacamole (4 cycles, 689 MPa, 5 min each) is clean enough to not spoil for 30 days in 25°C.
Note: the two sources above were linked from a commercial vendor of high-pressure products and may thus be biased. While the exact numbers might be problematic, I think the main point is unlikely to be affected.
A review High-pressure processing – effects on microbial food safety and food quality mentions some limitations, e.g.:

Endospores tend to be extremely HPP resistant compared with vegetative cells, withstanding treatments of more than 1000 MPa

ascospores of heat-resistant moulds such as Byssochlamys, Neosartorya and Talaromyces are generally considered to be extremely HPP resistant

It should however be noted that it seems that high-pressure treatment is good enough to be semi-commonly used as the main sterilization for suitable foodstuffs - Current status and future trends of high-pressure processing in food industry reports high-pressure treatment to be FDA approved and USD 10 billion worth of food being treated as of 2015.
Pressure for killing cells previously briefly discussed at Can bacteria or other microorganisms be killed by applying pressure?
Ultrasound
While frequently used for cell lysis in lab, it also doesn't kill all cells. For example The Sterilization of Suspensions Contaminated with MicroorganismsUsing Ultrasound Irradiation reports that roughly 20-30% of bacteria (Bacillus) survived 60 minutes of ultrasound treatment.
Ball mill
This is probably closest to actual "physical trauma" - you put small beads and a suspension with microorganisms into a cylinder and roll it hard. A low energy process for the recovery of bioproducts from cyanobacteria using a ball mill discusses using this process to extract proteins from cyanobacteria. They don't report absolute number of cells killed, just that it varies considerably with the rotation speed, size of beads, .... My interpretation is that in most cases a non-negligible proportion of the cells survive while majority is killed (and their contents extracted for further use).

In microbiology, the "ball mill" method of bacterial cell lysis is called bead-beating.

@user1136 I am not primarily a biologist - I did statistics on the project, so hope I am not messing up. What my colleagues did was use SYTOX green staining which "penetrates into the cells with compromised/permeabilized membranes where it stains nucleic acids and indicates cell death", so I think the cells were not completely broken (which is presumably why all cells were considered alive in previous work on the subject). You are welcome to check the methods and details at https://doi.org/10.1038/s41467-020-18800-2, primarily Figure 2.

There are plenty of physical or mechanical methods of killing bacteria, but most are used in conjunction with other agents and probably don't qualify as "blunt force trauma". For example, beer distributers might snake a brush through a keg line to disrupt any bacterial biofilms before flushing them out with disinfectant. Similar logic applies to rubbing your hands together when you wash them or using a brush to scrub biofilms off your teeth. We might not think of it as traumatic force, but it can be to bacteria.
Since you mentioned it, there is something that might resemble them being "twisted until they burst" (more like crushed to death). A device called a French pressure cell press (or just French press) used to lyse bacterial cells with purely mechanical forces. It basically compacts a culture of cells under high pressure, causing them the burst and release the cellular contents. Of course, it would require somehow gathering all of them together into a containment vessel and having an expensive laboratory instrument to put them in.

My understanding of how a French Press works is that the disruption is due to forces generated on the cells as they pass through the valve rather than the high pressure crushing the cells.

@tyersome Yeah, that's probably a more accurate description of the mechanism, though there are food safety applications that rely on pressure alone for bacterial killing.

The French pressure cell press idea is used today commercially. It doesn't have that name though. It's called HPP or high pressure processing. Hiperbaric is the company that sells and makes the machines. It uses up to 10,000 bar of water pressure in a container in order to kill bacteria. It's effective as long as the water activity of the food being processed is at a certain level. Also the pH matters as HPP cannot kill spores from sporoforming bacteria and has difficulty with molds. However it can kill E. coli, Salmonella, and Listeria in juices easily (I work with these machines).

@m4rio so to be clear, it’s used as an alternative to pasteurisation for liquid consumables!?

@Tim Yes. Most common liquid (or juice) pasteurizations include heat, UV, or HPP. HPP is the newest among the three and for companies who do not like to heat their product or UV it due to whatever reasons, be it they want it more organic, or it changes the flavor of the juice, HPP can be an alternative. It is the most expensive of the three but can give a good ROI if done in big bulk. As far as I know only big or big-ish companies use it. If you're doing HPP and you get a good CFU/ml reduction of bacteria as the FDA requires you can use HPP as your sole method of pasteurization.

From a biochemical point of view, it is surprisingly difficult to break open a bacterial cell. A molecular biologist, for example, might want to break E.coli cells expressing a protein of interest, in order to purify the protien
Some of the methods employed are agitation with glass beads, griding with glass beads, sonication, and high-pressure cell disruption with the French Press.
In contrast, liver cells, or brain cells, may be broken by simple homogenization (for example in a Waring blender)

Yes of course. While you can't swing a blunt object comparable in size to humans at something many orders of magnitude smaller to kill it. You can use something like an ultra sonicator, which swings a small blunt object at --you guessed it-- very high speeds to generate lots of shear forces in a liquid culture. Usually these instruments are not meant for killing cells, but even with my most gentle use for breaking up cell clumps, a look under the microscope would show ruptured cells with their interior spilled out.

I use a sonicator for making (mammalian) cell lysate all the time. But, I think you might be confused about how a sonicator works. It's not the force of the moving tip itself that does it - the extremely rapid motion of the tip produces very tiny vacuum bubbles in the fluid that almost immediately collapse, sending a strong shearing force through the liquid and disrupting whatever it comes into contact with. The principle is called ultra-cavitation.

@MattDMo Didn't I say shearing forces in liquid?

@Cell — The way you wrote your answer does sound like direct action by the tip — I encourage you to change your wording and ideally add the type of information mentioned by MattDMo. Your answer would also be improved by including references.

...cut with a knife...

Yes, freeze them.... slowly. <evil laugh>
Freezing causes ice crystals to form inside the cells. These act like little knives. The slower the freezing happens, the larger these ice crystals get. The larger the ice crystals, the more damage they will do by piercing the cell wall and other important membranes.
This is why if you freeze fruit in a home freezer it thaws out as a goopy mess; the cell walls have been punctured. This is also a key problem in cryogenics, the freezing and thawing process damages cells.
Flash freezing uses very rapid freezing to very low temperatures to prevent large ice crystals from forming and minimizing damage. Food which is flash frozen is less goopy.
Note that when done properly freezing can be used to reliably kill some microbes, freezing at home is not a reliable disinfectant.


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Preserved alpha complementation over evolutionary time?
Has the result of alpha-complementation ever happened via mutation through evolutionary time, and been preserved in modern day organisms?
In other words, has a functional gene product ever been split or terminated by mutation, exhibit alpha-complementation, and has since been preserved as a quaternary protein? This could be found out by comparison of proteins in closely-related species, but don't know of any specific examples. Has such a thing ever happened?

Would trans-splicing or chimeric transcripts qualify?

Those two are almost "the opposite" of the answer I was expecting. Two genes which "came together" through evolutionary time. Although it might be interesting to point it out in the answer, I am thinking along the lines of "one gene becoming two associated ones though evolutionary time".

Yes, there are cases where one gene has become two. Or, at least, where multiple  functions carried out by a single protein, the product of one gene, are carried out by distinct proteins, the products of different genes, in another species.
One case I have personally worked with is the bacterial SelB protein. It is essential for selenoprotein biosynthesis and has a dual function as an elongation factor and RNA binding protein. In eukaryotes, these functions are split between two proteins coded for by different genes. The Sec specific elongation factor EfSec and the RNA binding protein SBP2. See here [1] for more information.
Chimeric transcripts are also examples of this. One I happen to know of is the protein secp43 which in the mosquito A. gambiae is encoded by genomic sequences found on two different chromosomes [2]. Essentially, by two different genes.
The problem is that although we can easily find such cases of one protein's function being shared between multiple proteins in other species, or of one protein being coded for by multiple transcripts, it is hard to reconstruct the evolutionary history that gave rise to the current situation. So, whether the current situation has arisen because of alpha complementation or other causes is hard to know.
REFERENCES:
1) Lescure A, Fagegaltier D, Carbon P, Krol A., Protein factors mediating selenoprotein synthesis. Curr Protein Pept Sci. 2002 Feb;3(1):143-51.
2) Chapple CE, Guigó R., Relaxation of selective constraints causes independent selenoprotein extinction in insect genomes. PLoS One, 2008, 3(8):e2968.

Great answer, but I'll wait to see if anyone has any evolutionary evidence of the described situations.

AFAIK, there is no way of knowing if alpha complementation was involved. We can only infer the ancestral situation from what is available today and the specific mechanisms by which genes were split cannot really be seen.

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


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What types of archaea have been found in animals?
I am curious as to the species or "types" of archaea that have been found to reside within animals symbiotically. One of the only ones I can think of off the top of my head are methanogens, which live in the gut of ruminants for example.

Humans:
A pioneering study using 16S RNA primers found only Methanobrevibacter smithii in the human intestine.

Eckburg PB et al. (2005) Diversity of the human intestinal microbial flora. Science 308 1635-1638

In a recent review there is a comprehensive Table of archaea found in humans (this includes non-intestinal flora):

Dridi B et al. (2011) Archea as emerging organisms in complex human microbiomes. Anaerob2 17: 56-63

Much of the work that is catalogued by Dridi et al. is repeated identifications of Methanobacteriales (Methanobrevibacter smithii, Methanosphaera stadtmanae and Methanobrevibacter oralis), but there are others from the Thermoplasmateles, the Halobacteriales and the Sulfolobales, plus some that are thought to be members of unknown archaeal orders.


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What is the difference between xDNA and yDNA?
Wikipedia states that:

xDNA contains expanded bases, in which a benzene ring has been added, which may pair with canon bases, resulting in four possible base-pairs (8 bases:xA-T,xT-A,xC-G,xG-C, 16 bases if the unused arrangements are used). Another form of benzene added bases is yDNA, in which the base is widened by the benzene.

In this context, does widened and expanded refer to the same thing? If so, what would be the exact different between yDNA and xDNA.
NOTE: I am not referring to the X and Y chromosomes.

In fact, I just discovered the answer to the question in this article: http://www.aseanbiotechnology.info/Abstract/21026349.pdf. I will answer within the next few days unless someone beats me to it.

yDNA came first:

Lee & Kool (2005) A New Four-Base Genetic Helix, yDNA, Composed of Widened Benzopyrimidine−Purine Pairs. J Am Chem. Soc. 127: 3332-3338

As seen in this Figure from the paper it is a DNA consisting of T and C substituted with yT and yC, widened analogues.

xDNA came next (also from the Kool lab.):

Lynch et al. (2006) Toward a Designed, Functioning Genetic System with Expanded-Size Base Pairs: Solution Structure of the Eight-Base xDNA Double Helix. J Am Chem. Soc. 128: 14704-14711

As seen in this Figure from the paper, xDNA consists of an 8 base system A,C,G,T,xA,xC,xG,xT.

This field is reviewed in:

The XNA world: progress towards replication and evolution of synthetic genetic polymers
  Pinheiro & Holliger (2012) The XNA world: progress towards replication and evolution of synthetic genetic polymers. Current Opinion in Chemical Biology 16:245-252


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Simple experimental design of a chemostat without pumps
A chemostat is a device to maintain a cell culture in equilibrium, by continually pumping nutrients in and pumping excess cells and nutrients out.
What's the simplest design of a chemostat one can make? Has anyone tried to mount one without pumps?
A particularly sensitive issue here is sample taking, because you risk contamination of the culture.

Here is a link to a design in which electrolysis is used to generate gas which displaces liquid from the reservoir into the culture chamber, which in turn displaces culture into an outflow (where samples could be collected). The design does however require a pump for aeration, and I can't think of a way in which this could be avoided, although there is a reference cited in this article which apparently describes the use of gravity flow (I haven't checked it out).
Just for interest, this link is to a design which does require a peristaltic pump and a vacuum line but it looks to be achievable.


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How to model missing residues on a protein from multiple PDB files?
I have multiple X-ray structures (PDB files) of the same protein. All of them have missing stretches of residues. I want to use all of them to build a single model, with as little missing residues as possible. Is there a tool (web-server, software) available to do this?
In a particular application, I have one additional requirement. One of the PDB files is special (call it the template), in the sense that I only want to add missing residues to it. That is, the resulting model should contain the special pdb file, and align perfectly to it, the only difference being that the resulting model has some additional residues that were completed from the information in the other pdb files. Okay, the resulting model should not align "perfectly" to the template. I'll allow some relaxation in the connecting points. But still, the idea is that the template PDB should carry a "larger weight".
So, those are the two problems: 1) How to reconstruct a more complete model from a bunch of PDB files of the same protein? 2) How to complete missing residues in one PDB file using information from other PDB files of the same protein?

I'm not an expert on this, but the technical term you're looking for is "loop modeling", e.g. https://en.wikipedia.org/wiki/Loop_modeling -- I hope this gives you some leads

@MichaelKuhn That's the appropriate term if the missing residues are located on a loop (which they usually are).

I suggest you to use modeller - advance modelling (https://salilab.org/modeller/tutorial/advanced.html), Where you can use multiple template PDB to get final model structure.
All the best..:)

Please add some explanation. This looks more like a comment.


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Merge several .ab1 sequence files into a single FASTA file?
I have several .ab1 files generated from Chromas. I want to merge all of them into a single FASTA sequence file. How can I do this in an automated way?
Note that I don't have Chromas installed (the files were generated by someone else). I am using linux, so open-source, command line tools are preferred.

In general you should use a base calling algorithm to generate the sequences from the chromatogram and not directly convert it to fasta (Courtesy: Sven [SEQanswers] ). As mentioned in the link, phred and TraceTuner are popular base calling software that can generate a fasta output.
The software mentioned by The Nightman can be used for converting .ab1 to fasta. You can also try this BioPython module called abifpy. You can easily read each .ab1 file, using a python script, and write the sequences in fasta format as a single file.
Combining multiple fasta is quite trivial. You can use cat as mentioned by others.

Typical possibilities that come to mind are programs available in EMBOSS and Staden. However the question of how to access the sequence data stored in .ab1 files has been asked on Biostars a number of times, so I suggest having a look at the various answers there for options that are suitable for your environment and use case, see Biostars search "ab1".

DNA Baser has a batch abi to fasta converter here. After files have been converted to .fa files, they can be concatenated together in UNIX/MAC using cat *fa > output.fa


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How do major and minor grooves arise in the DNA helix?
I understand that they arise due to the pairing of bases of two opposite stands and are sites through which important proteins needed for replication and transcription of DNA interact. But I don't get how the arrangement of bases really generate the grooves.

@canadianer Though the other question was asked two years from now and 1 year before this but David's answer definitely better explains the concept and since new posts with better answers are often marked as mother questions, I think this should be done with this post too.

"Better" is subjective. The picture in the other answer is crucial to understanding why there are major and minor grooves. Perhaps a moderator can merge the questions.

Sure. Should this be flagged then? @canadianer

Try it. I'm not entirely sure how the merging process works.

First the MAJOR groove. This can be considered by forgetting that the two DNA strands are separate and thinking of them as merged together like a rope or an electric cable in my diagram below. The cable is wrapped around an axis, which can be thought of as an imaginary rod. One could wrap the cable tightly round the axis so it was always in contact with itself and no space in between (as on the extreme left), or one can wrap it round less tightly with space in between (subsequent images). The latter is what happens and the space left by the wrapping of the rope is the major groove (because it's wider than the minor groove we come to next).
So to your question, what causes this winding to be the way it is — which is to say what causes the particular pitch of the DNA helix? This is presumably the most thermodynamically stable conformation, maximizing the strength of the hydrogen bonding between the bases (most linear, not too close or too distant) and minimizes steric and electric replusion (the phosphate backbone is negatively charged).
Next the MINOR groove. We now have to consider that our 'cable' is actually two strands of DNA — a sugar phosphate backbone with the paired bases in the centre. Although the bases are in the centre, the space between the two sugar-phosphate strands is not completely occupied. This creates another groove - the minor groove - less wide and of quite different origin to the major groove, but still able to accomodate certain small proteins. As it happens, this is the case with the electric cable analogy illustrated. The cable is two electric wires, but they are encased in the same plastic inuslator.
So to your question, what causes the minor groove? This is just the three dimensional shape of the DNA double-strand which reflects the molecules that make it up. The large phosphate groups can be considered like wide tubes at either side, whereas the bases between the strands are flat and so lead to a groove, irrespective of whether it is wound into a helix or (in one's imagination) stretched out.
(The 'major' groove is exaggerated in size compared to the 'minor' groove in the cable analogy, but I hope it illustrates the difference between the two.)

Nice explanatory answer with a very good example of an analogy.

@Mesentery — Thanks. This was one of my first contributions to SE Biology, so I suppose I put a bit more effort in than I do now. But I do some of my best thinking in the bath or the shower, and my shaver cord was close at hand ;-)

Nice wire ;). Good illustration!!


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Is eye colour of Drosophila a polygenic trait or an X-linked trait?
I have seen examples of various eye colours in Drosophila and they have been described both as "X-linked" and "polygenic". I thought these were mutually exclusive. Is Drosophila eye colour polygenic or X-linked?

A trait can be X-linked and polygenic. It is not contradictory. Can you please link to your references?

I don't think this question is unclear, the user didn't understand the terms X-linked and polygenic properly. I have voted for reopening. @SanjuktaGhosh could you add sources to this showing where it says they are x-linked/polygenic?

Polygenic and X-linked are not mutually exclusive; a trait can be polygenic, X-linked, neither, or both.
Polygenic traits are those affected by many genes. According to Conner and Hartl, "Traits that are affected by many gene loci are often called polygenic traits." For example, childhood intelligence is affected by many genes throughout the genome. Monogenic traits are relatively rare, but are linked to some diseases, and occur when a single gene affects the trait; most traits are affected by multiple genes.
X-linked traits are affected by genes (only) situated on the X. If a trait is monogenic, it is very easy to describe it as being, or not being, X-linked. If the gene is on the X, it is X-linked. If it is on the Y, it is Y-linked. If it is in the autosomes, it is autosomally linked. If all of the genes affecting a trait are found on the X then that trait is X-linked. A gene on the X is X-linked, but I would only call a trait X-linked if all genes were on the X. If there was a mix of X-linked and autosomal genetic variation I would describe the trait as having some X-linked genes.
For polygenic traits it is less likely that the genes would be all X-linked, so it is less likely that the trait is X-linked. Imagine a trait affected by 10 genes, and those 10 genes are randomly spread across the genome. It is unlikely that all 10 would be on the X. However, if some were on the X, then we could describe the trait as having X-linked variation. Many highly polygenic traits will have some X-linked variation.
Having more genes affecting the trait further increases the probability that some genes will be on the sex chromosomes. For example, lifespan is expected to be a highly polygenic trait, and genetic variation in lifepsan can be attributed to the Y chromosome in Drosophila melanogaster, despite the Y carrying just a tiny fraction of the genes.
Many eye colour traits in Drosophila can be attributed to single genes, and White in the image below is X-linked. However, one can also produce a white-eyed fly using multiple genes; in this paper one of the lines used has white eyes as a result of combining genes (T(2;3) rdgC st pp bwD) which are on the autosomes.


text:
Plant dependence on CO₂ levels
I was wondering which kind of plants would survive longest without CO2 : plants with fatty, thick leaves or plants with thinner, less fatty leaves?
And can we conclude that the plant species that are more dependent on CO2 levels, would benefit from the greenhouse effect?

Plants do not need CO2 for survival. They need it just for photosynthesis; if you supply them with alternate carbon source such as glucose then they would survive (See here for non-CO2 Carbon-source utilization). There are non-photosynthetic plants which are totally dependent on carbon sources other than CO2; some are carnivorous like venus fly-trap.
I am not sure what you intend when you correlate leaf thickness to CO2-dependence. Leaf morphology can affect photosynthesis rates but afaik there is no relationship between leaf morphology and CO2-dependence.

can we conclude that the plant species that are more dependent on
  CO2 levels, would benefit from the greenhouse effect

Greenhouse effect is not equivalent to increase in CO2; increased CO2 levels can cause greenhouse effect which is a physical phenomenon of entrapment of IR radiation.
CO2 is fixed by an enzyme called Rubisco; like most enzyme catalyzed reactions this will reach a saturation limit i.e. addition of more CO2 will not increase the rate of photosynthesis. However the rate of photosynthesis is limited at the Rubisco step because of low levels of CO2 (See here) and it can be expected that most photosynthetic plants will be benefited by increase in CO2. However, not all plants would be resistant to increased temperatures and moreover the rate of enzyme catalysis would also change with temperature. So it is not that easy to predict.


text:
Is there a list with common mistakes in database entries?
I am a university student at the department of Biology. As part of my assignment for the subject Bioinformatics, I was asked to refer to common mistakes in biological database (e.g. Uniprot). For instance, "dehydrofolate reductase". Is there any list with such common mistakes? Otherwise, in your own experience, could you mention some examples of similar mistakes that you meet regularly?

"dehydrofolate reductase" does not meet my definition of common. It is used in 14 out of 71 000 000 trEMBL entries in Uniprot. trEMBL is comprised of computationally annotated entries which are explicitly awaiting curation. Is there an error? Dehydrofolate reductase type I (Q4QZB8) has a meaningless name, but plenty of proteins or genes suffer from that fate. The gene ontologies and domain annotations correctly indicate the presence of dihydrofolate reductase.

If there were such lists (maintained by the databases) then why would the errors be still present? This is a manual curation assignment. You have to go through them one by one.

Microsoft Excel has a nasty habit of thinking it knows your data better than you do, and when it sees gene names like Mar3 and Sept7, it tries to be helpful and convert them to dates.
So anytime someone puts a long gene list in to Excel and takes it back out again, you see those genes and their relatives converted to dates.
This has been known for years, but you still see it in lots and lots of published papers.
According to http://dx.doi.org/10.1186/s13059-016-1044-7, almost 20% of article supplementary material gene lists made with Excel contain such errors.

If you find any such errors in curated databases such as UniProt, please let the database curators know.
In the expert-curated, reviewed section UniProtKB/Swiss-Prot, we'll be happy to fix typos and other errors. As far as the unreviewed UniProtKB/TrEMBL records are concerned, this is slightly more complex as Michael_A states above. However, in many cases, the nucleotide sequence databases (EMBL-Bank/GenBank/DDBJ) can be asked to correct the underlying source, and the corrected names can then be updated in/reimported into UniProtKB/TrEMBL.

Sequencing artifacts in the type A influenza databases and attempts to correct them. is a paper that describes errors in one biological database. There are several other papers that similarly address errors in influenza genome databases, but I particularly liked this one because of its unique approach:

As part of a high school class project, influenza sequences with possible errors were identified in the public databases based on the size of the gene being longer than expected, with the hypothesis that these sequences would have an error. Students contacted sequence submitters alerting them of the possible sequence issue(s) and requested they the suspect sequence(s) be correct as appropriate.

Kudos to the lead investigator, David Suarez, for combining a genuinely useful project with cool outreach.

Interesting though this is, it isn't the kind of textual mistake that the poster is referring to.

"could you mention some examples of similar mistakes that you meet regularly" - I think the paper covers this part of his question pretty well

Strictly speaking I should just vote to close this question as homework or too broad, or repeat WYSWYG’s comment as an answer. However if one changes the question to how one should go about this problem as a bioinformatician, it becomes more interesting.
For a Biology student with no programming skills, it seems something of a distortion to call it bioinformatics. One aspect of bioinformatics is to use programming techniques that automate repetition and if/else decision-making to avoid laborious manual operations. So can one think of possible non-programming approaches? Here are a couple of suggestions to which others might wish to add.

Make up a list of terms like nitrate, phosphate, citrate, sulphate/sulfate, which all end in -ate. In German these are generally similar but end in -at (without the e). Even if you have no German you can use an on-line translator. In my experience some entries in the PDB have mis-spellings arising from such linguistic similarities, so you could search for the German spellings. You might even search for German or French abbreviations for RNA and DNA (RNS, DNS, ARN, ADN). Still a manual approach, but it would have some logic behind it.
If it is possible to find all the key words in your database of interest, can you find how many times each occurs. I could do this with a program, but perhaps Excel has some facility for doing this. You could Google to see. Then you could make the assumption that mis-spellings would only occur a relatively small number of times. You could then work through the terms with the lowest frequency of occurrence, looking for mis-spellings.

If you had some programming ability you could modify approach 2 by looking for words that differ by a single letter, on the assumption that some of these differences would be spelling mistakes. (Presumably a language such as Perl that is designed for use with regular expressions would be most appropriate.)


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Is oxygen's paramagnetism biologically relevant?
It seems our most common everyday O2 molecule happens to be a paramagnetic one (see https://en.wikipedia.org/wiki/Oxygen).
But, does this have a biological relevance as well? In other words, Do any chemical processes occurring within the physical volume of any entity considered "living" depend in any way, shape, or form on oxygen's paramagnetic properties?
(It seems to me the answer would be "no", but I am no expert in this field!)

This Q is too broad.

Biology isn't a very narrow field  either ; ) Would the following phrasing be any more specific: Do any chemical processes occuring within the physical volume of any entiy considered "living" depend in any way, shape, or form on oxygen's paramagnetic properties?

The answer is either yes or no; I don't think this is too broad.

The answer to your re-phrased question

"Do any chemical processes occurring within the physical volume of any entiy considered "living" depend in any way, shape, or form on oxygen's paramagnetic properties?

is an emphatic 'yes', and this answer may be substantiated with a single word: haemoglobin.
Pauling and Coryell discovered in 1936 that the oxygen in both oxy-haemoglobin and CO-haemoglobin is diamagnetic, and I quote from the last paragraph of that great paper:

It is shown by magnetic measurements that oxyhemoglobin and carbon-monoxyhemoglobin contain no unpaired electrons; the oxygen molecule, with two unpaired electrons in the free state, accordingly undergoes a profound change in electronic structure on attachment to hemoglobin.

This result has 'stood the test of time' and is a key observation in explaining at a  molecular level the allosteric and cooperative properties of haemoglobin.  IMO, it is one of Pauling's great observations, and illustrates his phenomenal powers of deduction.
As Pauling points out, Faraday also investigated the magnetic properties of haemoglobin and recorded in his notebook 'Must try recent fluid blood'.
To again quote Pauling and Coryell :

If he had determined the magnetic susceptibilities of arterial and venous blood, he would have found them to differ by a large amount (as much as twenty per cent for completely oxygenated and completely deoxygenated blood); this discovery without doubt would have excited much interest and would have influenced appreciably the course of research on blood and hemoglobin

Magnetic properties usually do not play a significant role in chemical reaction unless some external magnetic field is applied. The magnetic properties are resultant of the chemical properties and it is the chemical properties that really matter. And how would the biochemistry of oxygen transport be affected because of the magnetic properties of oxygen? The only biological process that I am aware of that is driven by magnetism is magnetotaxis and it involves the ferromagnetic substance magnetite.

I got your point but I was arguing that the paramagnetism of dioxygen per se does not play any role in any of its biochemical reactions. The chemical properties that cause paramagnetism also affect some of the reactions of dioxygen. So even if those two properties are correlated there is no cause-effect relationship between them. See my answer.

It sounds a bit like chem exam question to be honest... Or at least the answer does... namely that O2 being paramagnetic creates a "spin barrier" that prevents most organic compounds from reacting fast with atmospheric oxygen:

The magnetic properties of O2 are not just a laboratory curiosity; they are absolutely crucial to the existence of life. Because Earth’s atmosphere contains 20% oxygen, all organic compounds, including those that compose our body tissues, should react rapidly with air to form H2O, CO2, and N2 in an exothermic reaction. Fortunately for us, however, this reaction is very, very slow. The reason for the unexpected stability of organic compounds in an oxygen atmosphere is that virtually all organic compounds, as well as H2O, CO2, and N2, have only paired electrons, whereas oxygen has two unpaired electrons. Thus the reaction of O2 with organic compounds to give H2O, CO2, and N2 would require that at least one of the electrons on O2 change its spin during the reaction. This would require a large input of energy, an obstacle that chemists call a spin barrier.

(quote from McQuarrie and Simon's (free) textbook Physical Chemistry: A Molecular Approach).

And by the way, there also exists a variety of singlet oxygen (1Σ -- second excited state) that is not paramagnetic, but is fairly unstable a room temperature.

Interesting, so not exactly out of some kind of interaction with magnetic fields of any sort (which is what I usually think of regarding paramagnetism), but rather due to the quantum effects (such as this "spin barrier") that just also happen to give rise to "paramagnetism" as well. It seems the answer would then be "no", although the underlying causes of paramagnetism do give rise to other more "useful" properties (such as inhibitions of chemical reactivity) however.

@ManRow you are right. Unless there is an interaction with the applied magnetic field, the magnetic properties per se do not have any effect on the chemical reactions.

According to my reasoning, the answer is no.

Bulk magnetic properties are a resultant of the atomic/molecular (chemical) properties and chemical reactions are usually not dependent on the magnetic properties per se. Magnetic properties can have a role if:

There is an externally applied magnetic field (https://chemistry.stackexchange.com/q/24507/5295). Magnetotaxis in some bacteria involves signalling by microcrystals of magnetite and greigite that guide the bacterial locomotion in response to the geo-magnetic field.
Some of the reactants or the components of the reaction center exert magnetic field (ferromagnetic materials, for example).  Herve et al (1984) showed that incorporation of magnetite particles increased the yield of some intermediates (light absorbing transients) during the photoreduction of benzophenone.

In any case I haven't come across any research that states that the paramagenetism of molecular oxygen is a factor in any biochemical reaction. As pointed out in the other answer, the stable triplet state of dioxygen is slightly less reactive compared to the singlet state. The paramagnetism that results because of the triplet state is not the cause of the lower reactivity. In other words, there is no cause and effect relationship.


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How to compute CNV from TCGA data
TCGA provide CNV data for each cases like this.
I want to know, how can I calculate CNVs from this data? What are standard  algorithms and methods used?

Hi. The link your provided cannot be accessed any more. Can you provide information about what or which files did you use to extract CNVs?

The files you're referencing are completely processed files in the SEG format. The basic files are calculated using both germline and somatic CNV, whereas files denoted nocnv are only somatic (the numbers will differ because they are calculated either together or not together, so don't mix and match).
Among many tools you can use IGV from Broad Instutute to view SEG files at somewhat of a high level, or you can use various R packages i.e. Bioconductor to do your own analysis. You will probably need to do this anyways to annotate the gene names to the data. You can find somewhat of a how-to here or here. IGV will know what genes it's looking at based on chromosome assembly, and you can open it rather easily using java web start. I'm far from an expert, however.
Beyond that, you do need to make some additional considerations based on your expertise. For example, in this article, the authors set cutoffs for INDELS by filtering out seg.mean values between -0.2 and 0.2 (ref). They also filter out any markers for which 'num.mark' < 10, where 'num.mark' is the number of actual affymetrix probes detected, to account for false positives.
And so what you're left with then is the chromosome, the segment the probe spans, and the log2 mean of the microarray intensities calculated. So for that region of the chromosome, loc.start to loc.end, a seg.mean of 2 would mean 4-fold change in copy numbers over the reference for that region.
I think the take home point is the leg work on the data has been done, and level 3 TCGA data like .seg files are ready-to-go sources of CNV data. You significant tasks will be annotating, visualizing and assigning biological/statistical significance to your data.

Thanks for nice explanation. Just to clarify, should I ignore the data if num_probes < 10 OR segment_mean between -0.2 and 0.2?

It depends on what you're intending to analyze. If you have a significant segment mean but barely any probes, it could just be noise or an actual SNP as opposed to a CNV. Then for segment means, they set cutoffs: If your mean is below your reference, it could be a deletion (negative expression), and you aren't analyzing deletions. They chose >0.2 because anything lower may be noise or no amplification (you could argue different numbers. The idea would be to filter for BOTH to obtain high-quality data.

Copy number variation (CNV) has traditionally been detected in the wet lab via FISH, fluorescent in-situ hybridization. By combining targeted DNA oligos to fluorescent reporter proteins, a region with high CNV will "glow more brightly" than a region that doesn't have as many repeats.
More recently, with next-generation sequencing, a common approach is to align sequencing reads to the reference genome. If a particular region in the sequenced sample has a large CNV, then there should be disproportionately many reads (2x, 3x, etc) reads mapping to the CNV locus in the reference genome compared to the rest of the genome.
For example, if our sequenced sample has a 3-CNV for region B:
$$A-B-B-B-C$$
then aligning our sequenced reads to the reference genome, which is:
$$A-B-C$$
will yield 1x coverage at region A, 3x coverage at region B, and 1x coverage at region C.
There are, of course, more sophisticated approaches. For a good review, refer to
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0059128

Thanks for the reply. In my link of TCGA, each CNV file contains a column num_probes, do you know what does this column means or what would be the interpretation of this column?

Yes - the data are most likely from high resolution SNP chip arrays (mostly affymetrix) , and these have probes for specific genomic coordinates. The intensities then get combined into distinct segments with similar copy number estimates to produce the segmented files you have. The number of probes on the array that lie in a given segment is the num_probes column.
What particularly are you planning to do in terms of downstream analyses?

Also consider , in addition to CMosychuk's suggestions, sticking things through ABSOLUTE (particularly if you also have a mutation MAF file, which you can grab from the MC3 project on SAGE Synapse). The advantage of this is the ability to estimate and account for purity and derive absolute copy number values. EXPANDS is another R package that can do absolute copy number estimation.

What is absolute copy number? How it would be helpful? Sorry, I am from computer background

Usually copy number data is relative, you get a ratio in terms of tumour vs normal; this does not tell you how many copies of a given gene are actually present in a tumour, and if so, in how many cells in a tumour.
Absolute copy number is the actual number of copies per cell. ABSOLUTE builds a probabilistic model and fits combinations of purity and ploidy to derive absolute copy number.


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How to get the correct RefSeq Protein transcript for a given RefSeq Nucleotide transcript?
How to get versioned Protein Accession Number for a Refseq Accession Number?
I have some versioned RefSeq Accession numbers and I would like to know their corresponding Protein Accession Numbers.
According to the RefSeqFAQ   http://www.ncbi.nlm.nih.gov/books/NBK50679/#RefSeqFAQ.what_causes_the_version_number there is not a 1-to-1 mapping between these numbers.

Also, nucleotide (NM_, XM_) and protein (NP_, XP) records for the same
  transcript may not have the same version number after an update. For
  example, an update to the 5’ UTR of a RefSeq nucleotide record would cause a version number change for the updated nucleotide record but not for the corresponding protein record. Conversely, a change in the annotated start site of the coding sequence without a change in the underlying nucleotide sequence would cause a version number update for the NP_ but not for the NM_.

I have tried this, but it gives me a huge
>>> from Bio import Entrez
>>> Entrez.email='email@example.com'
>>> f = file('entrez_NM_005529.5.txt', 'w')
>>> print >>f,  Entrez.efetch(
...     db="protein",id="NM_005529.5", rettype="native", retmode="xml").read()

The XML results contain the Protein Acc No, <Textseq-id_accession>NP_005520</Textseq-id_accession> but there is no version specified. Is there a simple way to get obtain the matching protein transcript version?

Are you always dealing with the latest nt version, or do you need to get previous ones?

There is another simple way for human sequences.
Go to RefSeq FTP and download the LRG_RefSeqGene file. This file contains updated mappings between the gene, mRNA and protein sequences (latest versions).
From README:
    Tab-delimited file reporting, for each Gene, the accession.version
      of the genomic and RNA and protein RefSeqs the RefSeqGene/LRG
      project treats as reference standards.

    The columns are:
      NCBI taxonomy id (all 9606)
      GeneID
      Symbol of the gene (official from HGNC when available)
      Accession.version of the standard RefSeq
      Term describing the RefSeq
         RefSeqGene              genomic sequence
         Ref Std, nucleotide     RNA sequence
         Ref Std, protein        protein sequence

      The LRG equivalent of the RefSeq standard
         LRG:                    genomic sequence
         t1                      locations for transcript 1
         p1                      CDS from transcript 1

         NOTE: t values can be > 1 ,  the integer assigned to t
               is matched by the integer assigned to p

Some few lines from the file:
9606    3329    HSPD1   NG_008915.1     NM_199440.1     NP_955472.1     reference standard
9606    3329    HSPD1   NG_008915.1     NM_002156.4     NP_002147.2     aligned: Selected
9606    3336    HSPE1   NG_008914.1     NM_002157.2     NP_002148.1     reference standard
9606    3339    HSPG2   NG_016740.1     NM_005529.6     NP_005520.4     reference standard

It is not that easy for other organisms. For them, you should either parse the mRNA page to find the RefSeq protein id or go to their specific designated databases (such as FlyBase for Drosophila) and obtain information on the gene to protein mapping.
For obtaining RefSeq records from NCBI using parsing you should first obtain the GI accession number corresponding to the mRNA.
Example: NM_007393 (mouse beta-actin)
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=nucleotide&term=NM_007393

which would give the GI accession number: 930945786.
Then fetch the protein GI using this accession number:
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=nucleotide&db=protein&id=930945786

which gives the GI accession number: 6671509
Finally get the RefSeq id (if available) corresponding to this GI accession. You can download the feature table and look for the RefSeq i.d:
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=nucleotide&db=protein&id=930945786&rettype=ft

For more information on how to use Entrez E-Utilities see here.

Thanks for a very comprehensive answer. Fortunately I'm only dealing with humans, but this will serve as a very useful explanation for others.


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Are there animals that have evolved a resistance to human activity or encroachment?
There are countless sources, both peer-reviewed and popular, explaining how overuse and misuse of antibiotics is breeding a new generation of antibiotic-resistant "superbugs" such as MRSA (Methicillin-resistant Staphylococcus aureus) and MDR-TB (multidrug-resistant Tuberculosis). Over in the animal kingdom, the opposite seems to be happening - species after species is becoming endangered and/or extinct as humans destroy or alter their habitat through increased hunting, farming, construction, etc.
Are there any non-human animals that have been found to have evolved resistance to human encroachment into or alteration of their habitat in a way analogous to how bacteria have evolved resistance to human attempts to get rid of them? For example, this could consist of:

an animal that has adapted stronger bones to better survive collisions with vehicles
an animal that has significantly increased its blood coagulation rate to survive gunshot wounds from hunters
an animal that has developed better vision to see in urban environments
an animal that has evolved a skin pigment change that enables them to not take as much damage when they are sprayed with agricultural pesticides

One answer that came to mind is domestic animals - the horse and dog in prehistory, the cat in ancient Egypt, etc. That seems too obvious on one hand, and on the other hand may not really be an answer, as there seems to be no indication that pre-domestic animals were endangered by humans in any meaningful way. Are there animals that have significantly adapted themselves to surviving as wild animals in human-influenced environments?

Welcome to Biology.SE! Is there any reason to exclude the numerous "pest" species (e.g.s: cockroaches, mice, rats) that often "share" our homes?

"Evolved" is rather tricky, but there are a number of species that live happily in places densely populated by humans: pigeons, the urban peregrine falcons that nest on skyscrapere & prey on them, urban coyotes, bears that feed on trash, even the occasional mountain lion that comes into town to visit the casinos: https://www.kcra.com/article/a-mountain-lion-in-a-casino-don-t-roulette-out/6398674#

@tyersome thanks, and not really. An example of cockroach or rat evolution could be an answer, but I'm looking for cases in which the organism has clearly developed a notable adaptation that it didn't have (or that at least was rare) before, rather than cases of animals that have more or less always been pests. For example, if there is a paper showing that certain varieties of cockroaches in North America developed bleach resistance between 1830 and 1870 as a result of its use as a pesticide, that would be an answer.

By excluding cats, you're excluding what is possibly the clearest example.  Cats don't fit the normal profile of domesticated animals -- you can't tame a cat the way you can tame a dog or a horse.  One explanation for this is that cats weren't domesticated.  Instead, they evolved to fit into an agrarian society.

An example given in "Guns, Germs and Steel" is the simple observation that megafauna extinctions were near total anywhere humans first arrived as fully evolved homo sapiens, but significantly less extreme anywhere humans had been co-evolving with the local megafauna (in particular Africa, to a lesser extent across Eurasia). We can't say precisely what attributes and behaviors African/Eurasian megafauna evolved that American/Australian megafauna did not, but the historical evidence suggests that simply having time to co-evolve to deal with humans helped a lot. "Fear of humans" seems likely.

There are some good examples of behavioural adaptations to urban environments listed here: https://www.independent.co.uk/news/science/why-did-the-hedgehog-not-cross-the-road-because-it-had-adapted-its-behaviour-in-order-to-thrive-in-a-8527825.html

Does "becoming cuter so humans will protect you instead of hunt you" count?

Normally it takes many generations for genetic adaptation. But I realized through your question, that by removal (e.g. hunting) of a specific phenotype encoded with one dominant gene allele, that allele is removed as well. Unless one knows how a phenotype is encoded genetically, it is not possible to conclude that it was an evolutionary human encroachment with wildlife, i.e. whether the genome was changed or not.

An excellent example of physical adaptation is the American Cliff Swallow which appears to be evolving a shorter wingspan, enabling them to be more agile in traffic. I'd post this as an answer but bio.SE doesn't love me :(

@vsz yes, I think it does.

@RobertColumbia : they I guess cats will count...

Note: This is an answer to the last line of your question.
A classical example of animals adapting to the influence of humans on their environment is the adaption of the Peppered Moth.
Here is a brief summary:
The peppered moth was originally a mostly unpigmented animal (<1800). During the industrial revolution in the southern parts of the UK a lot of coal was burned. This led to soot blackening the countryside. Soon afterwards, a fully pigmented variety was first observed. Only a hundred years later, in 1895, this pigmented variety almost completely displaced the unpigmented variety.
It has been shown that the pigmentation is under strong selective pressure as birds hunt these moths. Since birds rely on their visual system to detect their prey, the variety that blends in with its environment (=camouflage) has a selective advantage over the variety that stands out.
As pointed out by Tim in the comments, since the 1970s there has been a rapid reversal with unpigmented animals being more abundant. As far as I understand, it is accepted that this reversal is due to a decrease in human induced air pollution leading to less sooty barks on trees which makes the unpigmented variety harder to prey upon.
Addendum: genetic basis of adaption
In a beautiful recent study, the causal mutation for the pigmented, or melanic, variety was identified: A ~9kb transposon insertion in the first intron of the gene cortex. The authors calculate that this mutation happened in the year 1819, a few years after the industrial revolution was in full swing. The interpretation is that due to sooty tree bark this mutation, causing pigmented moth, was under strong selection.

This is a famous example of observed evolution. The early research had a couple of hiccups, but plenty of other research has verified it. +1

This is a famous example of natural selection.  One should take care about equating that with evolution, which includes genetic mutation as an essential component.  As far as I am aware, the peppered moth case has not been shown to have involved introduction of new or variant genes, but rather just environmental factors favoring a previously rare phenotype.

@JohnBollinger, the authors of this recent paper (https://www.nature.com/articles/nature17951) find that a transposable element in the 'cortex' gene is causal for the pigmented moth variety. They calculate that the insertion happened around 1819 when the industrial revolution was well underway. I don't want to overload the answer - do you think it would make sense adding to the answer as an addendum?

That's (obviously) news to me, @dtadres. In any case, I was primarily responding to your first comment, not directly to the answer, which presently does a smooth job of avoiding the question of how to label the phenomenon it describes.  I think it's doubtful that the OP cares much about the distinction, so I think the answer is fine as it is, but if you want to add an addendum referring to the paper then I don't think it would be overbearing.

Many insects (as well as some other animals) have documented resistance to pesticides.
For example, the German cockroach (Blattella germanica) can be resistant to multiple insecticides1. In addition, some populations of this cockroach are now repelled by glucose, which leads to them avoiding traps2.
References:
1: Fardisi, M., Gondhalekar, A. D., Ashbrook, A. R., & Scharf, M. E. (2019). Rapid evolutionary responses to insecticide resistance management interventions by the German cockroach (Blattella germanica L.). Scientific reports, 9(1), 8292.
2: Wada-Katsumata, A., Silverman, J., & Schal, C. (2013). Changes in taste neurons support the emergence of an adaptive behavior in cockroaches. Science, 340(6135), 972-975.

Bighorn sheep are developing smaller horns and elephants are becoming tuskless in Africa:

The horns of some bighorn sheep are getting smaller, because hunters are picking off the most impressive rams before they reach their breeding peak
Elephant poaching, for example, is thought to have led to an increase in the number of tuskless animals in Africa.

Source

Though it's an interesting question whether these statistical adaptations represent survival benefit for the populations.

Rattlesnakes in the southwest US are learning to stay quiet around humans so they don't get shot.

Nightingales have adapted to city noises by singing louder. Given that one function of singing is finding a mate there must indeed be a high, direct selection pressure to make oneself heard. Other birds have adapted in a similar fashion, e.g. by singing in a higher pitch, or at different times.

Not so much physical changes, but changes in behavior caused by human activity has numerous examples.
Foxes, pigeons, sea-gulls adapt very well to city life. Some species nowadays have larger  numbers in cities than in the country side.
Polar bears are another good example. Disappearing sea-ice and increased human activity in the Artic has turned many polar bears into pre-dominantly garbage scavengers in stead of hunters/carrion-eaters.

Is the polar bear adaptation really due to evolution? Have there really been enough generations to allow this to happen? I suspect they're just naturally flexible enough that they've been able to alter their behavior. Kind of like how human society has suddenly adapted to Coronavirus.

@Barmar That’s the question isn’t it. Polar bears are opportunistic scavengers, but in what degree this is evolution? It’s probably too recent to say for sure. Another thing about polar bears: they are moving South and come in ranges where regular brown bears live. Hybrids are becoming ever more common. These hybrids are apparently fertile with polar and brown bears leading to the question if polar bears are actually a separate species or just a local color variant of the brown bear. Biologists can’t agree on that one.


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What kind of spider is in this pic I took?
What kind of spider is this?
It has a weird black and white backpack looking thing. Found a couple of them in Auburn, Alabama. Any help is greatly appreciated.

It's a Spiny Orb Weaver, aka Spiny-backed Orb Weaver. Orb Weavers are a very large genus of spiders; this one is a Gasteracantha cancriformis. The spikes can be black or red; there are many colors as well.

Pictures uploaded for you :) Feel free to change them

@Remi.b - Oh, thank you!!! I only have a phone now, and I couldn't figure out how to do it! And you included the creepiest one, too, ha!


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For a luekemia patient - how does the chemotherapy fixes the bone marrow to not produce immature cells
For ALL patient this link says 90% of the children are cured.   It has high survival rate. From what I understand is that - luekemia is type of cancer where the immature cells does not turn into mature cells. How does the chemotherapy with other drugs - fixes the bone marrow immature cells defect?
What are the survival rates for acute lymphoblastic leukemia?
About 98 percent of children with ALL go into remission within weeks after starting treatment.
About 90 percent of those children can be cured. Patients are considered cured after 10 years in remission.

Chemotherapy in general works by killing (hopefully all) cancerous cells, while leaving the patient / healthy cells alive.
The drugs used for this usually are in principle toxins/poisons, but are more deadly to cancer cells, since these cells are growing very quickly compared to healthy cells. This general strategy is true for a lot of different types of drugs (and cancers). However, because the drugs are still generally toxic they are hard to dose correctly and also the reason why chemotherapy often has quite strong side-effects.
Additionally cancer can quickly evolve to gain resistance to specific drugs, which is why nowadays often a combination of multiple drugs at once is used.
In leukemia - which is a form of cancer where the blood and immune cell precursor cells in the bone marrow become cancerous - additional ways of selectively killing the cancer are radiation therapy (of the affected bones) and in some cases complete bone marrow transplants (which have their own issues, so they are not usually  used as first option).


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Protein molar mass from Uniprot ID?
I have a long list of uniprot IDs. How can I get the mass of the "canonical" isoform for each? Ideally from some Python library.

You don't need any external library, it's enough to query Uniprot and extract the data you want from the response.
Here an example in Python 3:
from urllib.request import urlopen

id = "P42212"
data = urlopen("http://www.uniprot.org/uniprot/" + id + ".txt").read().decode()
result = data.split('SQ   ')[-1] #here all the info you need
mw = int(result.split(';')[1].strip().split()[0]) #extract the molecular weigth
print(mw)

If you want to batch query a lot of sequences then I suggest to sleep(0.5) after each query to avoid problems with their server.
Update
As correctly pointed out by @Jerven (see comment below), you can query the mass directly.
In this case, the code is the following:
from urllib.request import urlopen

id = "P42212"
data = urlopen("http://www.uniprot.org/uniprot/?query=id:{}&format=tab&columns=id,mass".format(id)).read().decode()
result = data.split('\n')[1].replace(',','')
mw = int(result.split('\t')[-1])
print(mw)

I don't have Python 3 available, so I can't try this as it is. I am not sure how to translate into Python 2...

It should be the same, except the import from urllib import urlopen maybe also .decode() can be removed. Of course, print without parentheses. I can't try now. Let me know.

You can use the column/tab/csv download to avoid needing to fetch the whole TXT file. Also do keep in mind that these are predicted masses and are not suitable for e.g. MS work as is. e.g. http://www.uniprot.org/uniprot/?sort=&desc=&compress=no&query=&fil=&limit=10&force=no&preview=true&format=tab&columns=id,mass

@Jerven I am not sure how to interpret that table. I agree that it would be nicer to have a smaller text to download, but the masses reported on the link you posted are not related to the whole protein. Can you make an example of how to get the protein mass for ID P42212 using "column/tab/csv download" ?

@alec_djinn just add id:P42212 to the query field i.e. http://www.uniprot.org/uniprot/?query=id:P42212&format=tab&columns=id,mass see our help pages for more details http://www.uniprot.org/help/programmatic_access


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The evolution of sleep
Are there evolutionary theories as to why we (and most animals) sleep?
Some claimed functions of sleep include: restoration, waste clearance, memory processing, etc. But unless we understand why these functions are necessary (in the sense that the cost outweigh the benefits in terms of evolutionary fitness) and why they cannot be accomplished awake, we won't have a clear evolutionary theory of sleep.
Are there current theories on the evolution of sleep?

What work have you done on your own to answer this question? As it stands, it is quite broad, and would require a very substantive answer. While this is an interesting question, I am voting to close it until it is narrowed to focus on one particular topic, with background detailing what you do and do not specifically understand.


text:
Cellular specific mass (in grams-dry-weight per pico liter)?
What is the cellular specific mass, in units of pgDW (pico-gram dry-weight) per pL (pico liter)?
I suspect there is some variability between cell types, but this variability must be limited. Perhaps bacteria and mammalian cells are different, but the number shouldn't change much among bacteria. This is just intuition, I could be wrong.
So, what is the cellular specific mass, for different cell types (assuming there is limited variability, so this is not an infinite table)?

Density of a variety of cell types is rather well known. It can been measured directly by examining buoyancy (tendency to float) in liquids of known density, and also by microfluidic methods. But the results depend on the osmolality of the solution in which the cells are kept, since cells tend to "swell" (take up water) in low osmolality (hypotonic) medium.
Using a microfluidic technique, this paper reports a density of 1160 $\pm$ 1 pg/pL for E.coli at 290 mOsm, and 1139 $\pm$ 3 pg/pL for red blood cells. At comparable osmolality and using a gradient centrifugation method, this paper gives a density of 1095 pg/pL for E.Coli. So the results clearly differ a bit depending on experimental conditions (possibly the medium composition was different). For budding yeast, this paper estimates a density of 1103 $\pm$ 3 pg/pL, compared to 1113 pg/pL by gradient centrifugation.
In all cases, cells are heavier than pure water (1000 pg/pL), and larger eukaryotic cells seem to be slightly more dense. As macromolecules are more dense than water, this would suggest that eukaryotes have lower water content.

The most common opinion about this is a bacterium weighs about 10^-12 g. Here is a link with different results found using different techniques: http://hypertextbook.com/facts/2003/LouisSiu.shtml
This link also has useful relevant numbers, good to keep alongside: http://kirschner.med.harvard.edu/files/bionumbers/fundamentalBioNumbersHandout.pdf
Assuming a bacterial cell has a volume of 1 um^3 (10^-18 m^3; 0.001 pL) and 1 pg in mass, the density should be around 10^3 pg/pL. Similarly you can find out for other cell types.
Cheers!

Can you confirm that those are dry weights?

What becko means is that you should add a reference for each value that you mention. If you have taken them from BioNumbers then you can provide the BioNumbers ID or even better (and highly recommended) the actual reference (Pubmed IDs given in BioNumbers).

Also, a number for mammalian cells would be nice.

As per dry weights: E. Coli has an avg. dry weight of 0.28 * 10^-12 g. (BNID: 103904, link: http://book.bionumbers.org/what-is-the-macromolecular-composition-of-the-cell/). So, multiply the result above by 0.28. For mammalian cells, total weight per cell varies but on an average it is 3-4 ng. (Pubmed ID from Bionumbers: 17389958; BNID: 108454). The cell vol. varies: 100-10^4 um^3.(http://www.rpgroup.caltech.edu/publications/SnapShot2010.pdf). There is no concrete literature about the ratio of dry weight to total weight, but it is thought to be around 50-70%. Again these are avg. estimates.

Thanks for pointing out the calculation mistake @Roland .Of course for an accurate analysis you may need direct density measurement data, and also in that sense you will only get an average density of the cell as cells are highly heterogeneous. What I mentioned along with the specific references are some of the evidences of what have been done so far.


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What is the difference between local and global sequence alignments?
There are a bunch of different alignment tools out there, and I don't want to get bogged down in the maths behind them as this not only between software but varies from software version to version.
There are two main divides in the programs; some use local alignments and others use global alignments. My question is threefold:

What are the fundamental differences between the two?
What are the advantages and disadvantages of each?
When should one use either a global or local sequence alignment?

The very basic difference between a local and a global alignments is that in a local alignment, you try to match your query with a substring (a portion) of your subject (reference). Whereas in a global alignment you perform an end to end alignment with the subject (and therefore as von mises said, you may end up with a lot of gaps in global alignment if the sizes of query and subject are dissimilar). You may have gaps in local alignment also.
Local Alignment
5' ACTACTAGATTACTTACGGATCAGGTACTTTAGAGGCTTGCAACCA 3'
             |||| |||||| |||||||||||||||
          5' TACTCACGGATGAGGTACTTTAGAGGC 3'

Global Alignment
5' ACTACTAGATTACTTACGGATCAGGTACTTTAGAGGCTTGCAACCA 3'
   |||||||||||    |||||||  |||||||||||||| |||||||
5' ACTACTAGATT----ACGGATC--GTACTTTAGAGGCTAGCAACCA 3'

I shall give the example of the well known dynamic programming algorithms.
In the Needleman-Wunsch (Global) algorithm, the score tracking is done from the (m,n) co-ordinate corresponding to the bottom right corner of the scoring matrix (i.e. the end of the aligned sequences) whereas in the Smith-Waterman (local), it is done from the element with highest score in the matrix (i.e. the end of the highest scoring pair). You can check these algorithms for details.
You can adopt any scoring schemes and there is no fixed rule for it.
Global alignments are usually done for comparing homologous genes whereas local alignment can be used to find homologous domains in otherwise non-homologous genes.

Global alignment is when you take the entirety of both sequences into consideration when finding alignments, whereas in local you may only take a small portion into account. This sounds confusing so here an example:
Let's say you have a large reference, maybe 2000 bp. And you have a sequence, which is about 100 bp. Let's say that the reference contains the sequence almost exactly. If you did a local alignment, you would have a very good match. But if you did a global alignment, it may not match. Instead, it may look for matches throughout the entire reference, so you'd end up with an alignment with many large gaps. It does not matter that it matches near perfectly at one particular region on the reference, because it's looking for matches globally (i.e. throughout the reference).
If you have a really good match it may not matter what type of alignment you use. But when you have mismatches and such it starts to get important. This is because of the scoring algorithms used. In the example above let's say that there is a 100bp region in the reference that matches your 100bp sequence with 85% accuracy. In local alignment it's very likely it will align there. Now let's say that the first 30 bp of your sequence matches a region in the beginning of the reference 95%, and the next 30bp matches a region in the middle of the reference 85%, and the final 40bp matches a region at the end of the reference about 90%. In global alignment the best match is the gapped alignment, whereas in local alignment the ungapped alignment would be best. I think in general gap penalties are less in global alignments, but I'm not really an expert on the scoring algorithms.
What you want to use depends on what you are doing. If you think your sequence is a subsequence of the reference, do a local alignment. But if you think your entire sequence should match your entire reference, you would do a global alignment.

Take look to the attached image file. It will clear your doubts about difference between local and global sequence alignments.

Please do not post text answers as image files on SE Biology. This discriminates agains people with vision defects who use screen readers, and does not allow proper indexing.


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What about 23andMe's SNP test gives it such bad efficacy as a diagnostic tool?
The recent news about the FDA stopping the google backed 23andMe service selling any more kits got me thinking. I understand the company may have been selling it as a medical tool prematurely, but what is it about SNP analysis that makes it so poor as at predicting disposition? Even other SNP companies have similar problems with accuracy and can't agree on disease disposition (Tonellato et al., 2011). Why might they differ from 23andMe's predictions?

There is a quite extensive discussion about 23andme on Biostars. It has lots of links to helpful and interesting sites. http://www.biostars.org/p/87272/

I believe the issue is not the predictive power per se. 23andMe is a service that provides a fairly comprehensive analysis of your SNPs, and prepares a report collating the conclusions of published scientific research regarding your SNPs. I believe the FDA decision is motivated by the fear that some laypersons will see "you have a SNP that Jones and Doe, 2009 have associated with higher risk of a certain kind of cancer in a certain population" and take that to mean "You will get cancer tomorrow! Better start drafting your will!" (despite the information 23andMe provides on the contrary).

The long and short of it is that genetic variation is actually not very predictive in comparison to "environmental" effects such as lifestyle.
Only a quarter of the variation in lifespan between twins is attributable to inherited factors (including genetics and epigenetics) [1] - the rest is environmental, from lifestyle to air quality.
Most genetic variants (such as those measured on the chip used by 23andMe) have a very small impact on outcomes such as "Risk of disease X, Y or Z". Many risk loci have been identified, but without a full understand of what this risk means, the results of the 23andMe analysis can be misleading.
Some few rare genetic variants have a larger impact on diseases (such as early onset diabetes), but this comes under the "monogenic" category of genetic variation, rather than the common variants typically observed in large populations.
Basically, genetic risk scores are, at the moment, only indicative of an individuals risk, and to get the real picture a lot more information is required - for instance smoking is a better predictor of early mortality than any "common" (>=5% frequency in a population) genetic variants, likewise a high fat diet is more indicative of cardiovascular disease.

Skytthe A, Pedersen NL, Kaprio J, et al. Longevity studies in
GenomEUtwin. Twin Res. 2003;6(5):448–454. [PubMed link]