text:
Do gene expression levels necessarily correspond to levels of protein activation?
I have seen a lot of research into molecular mechanisms of diseases/phenotypes use measures of RNA as a 'proxy' for the level of protein available in the cell. Is this actually valid?
My problem with the assumption that RNA levels correlate with that of the active product (i.e. the protein) is that a lot of post translational regulation occurs, including co-factor binding and phosphorylation, to name but 2. Does anyone know of any studies that have looked into the correlation between RNA levels and protein levels, and separately into the correlations between RNA levels and active protein?
It makes sense to me that RNA would correlate with protein certainly, but whether this relates to the proteins active function is what I wonder - i.e. there could be a pool that is replenished as and when the protein levels drop, but the proteins are only actually active for short periods in response to specific stimuli. So, does anyone know of any studies that have looked into the correlation between RNA levels and protein levels, and separately into the correlations between RNA levels and active protein?

Update (04.07.12)
I have not accepted any answers as yet because none address my question about levels of protein activation, but I concede to Daniel's excellent point that proteins are not all activated in the same way; some are constantly active, some require phosphorylation (multiple sites?), some binding partners... etc! So a study looking at 'global' activation is not yet possible. Yet I was hoping that someone may have read some specific examples.
I today found an unpublished review by Nancy Kendrick of 10 studies that have looked at the correlation between mRNA and protein abundance - still not relating to activation. However she finishes the paper as follows;

The conclusion from the ten examples listed above seems inescapable:  mRNA levels cannot be
  used as surrogates for corresponding protein levels without verification.

If this is her conclusion about protein levels, then any correlation between protein activation and mRNA abundance seems unlikely (as a rule. Some protein levels do correlate with the RNA - see the paper).
I am still interested in any answers that give any information about specific examples of protein activation and mRNA levels - it seems highly unlikely there are no such studies, but I have been as yet unable to find any!

Good point: also you can imagine that if some mutation results in (for example) protein X being ubiquitylated you'd be likely to see an increase in the RNA that codes for X while the level of X drops. In this case the RNA would make a pretty lousy proxy.

To be frank, if you can't find a correlation between mRNA and protein abundance, why would you bother to ran the next experiment to find correlations between mRNA and protein activation?

@bobthejoe it does seem like it would be a fruitless experiment - doesn't mean no one has looked though (in fact I am surprised there isn't more literature on it). If no one else has found any then I will either accept one of the answers already given or post my own combining the evidence against any correlation.

@LukeTheDuke, I'm cautiously excited about your hypothesis.

A more recent review paper about this topic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654667/pdf/nihms395043.pdf

It has been well established that mRNA abundance serves as a poor proxy for protein abundance in most cases. This paper on yeast and this paper on cancer both establish this, although using older techniques (SAGE and microarrays, respectively), while this more recent review discusses the topic in light of more recent technologies (e.g. RNA-seq).
Perhaps the difficulty with exploring the correlation between mRNA levels and levels of active, mature proteins is that we still know so little about so many proteins and and what makes them active and mature versus inactive, premature, etc. Not all proteins need post-translational modifications to be active, but some do. Currently, this is investigated on a very detailed protein-by-protein basis (as far as I know), so gathering enough data for a large-scale study could take a long time. On the other hand, there are a variety of (increasingly affordable) high-throughput methods for measuring the abundance of thousands of RNA species simultaneously. I think people are using mRNA levels to estimate expression not because it's the most biologically cogent course of action, but rather because it is much easier, more affordable, and more high-throughput (which is all the rage these days).

It's interesting that the RNA doesn't even correlate well with protein abundance it that example, let alone activation state. I agree that the reason RNA is measured is for the comparative ease with which its abundance is quantified, however my issue is with the conclusions drawn in many papers based on the assumptions that RNA levels are at least indicative of protein abundance/activation! Thanks for the links and your reply

@Daniel: +1 for ending it at "..high throughput (which is all the rage these days)"!

Sometimes, overproduction of mRNA is a response to low concentrations of the expressed protein. Great answer.

I think that overproduction of mRNA in response to low protein is potentially what studies are looking for, as it implies the protein is in demand at that time (compared to a protein that does not need replenishing). This is of course confounded by that fact that proteins will have different lifespans! Thanks for your answer, I am hoping for more answers that might address the link between mRNA and protein activation, if there is any at all. If there isn't, then I fear that many studies may be reporting false claims!

I think you'll find very few papers that demonstrate changes in mRNA levels by microarray, claim that an increase in the gene product's activity is responsible for the biological effect, and then stop. mRNA data is complicated, not useless. Almost anywhere that mRNA upregulation is reported, it will be followed by a measurement of the protein levels and other follow-on experiments to confirm the protein's involvement.

This study in E.coli is useful in setting out some additional issues at play here, in a system where post-translational modifications are basically a non-issue.  The authors see clearly a correlation between mRNA and Protein levels (Fig. 3C) and theorize that differences translation rate and protein turnover are responsible for differences in protein level for two genes with similar mRNA level.
They investigate the issue on a single-cell level and find no correlation between a single cell's mRNA level and protein level for a given gene (Fig. 4).  This is attributed to the time-scale separation between mRNA turnover (usually a few minutes) and protein turnover (in many cases degradation is so slow, protein turnover is driven by dilution/cell division).  One way to understand this is there are several "generations" of mRNA contributing to a the population of protein at a given moment, which is not inconsistent with the central dogma.  However this result raises serious problems for using mRNA as a proxy for protein in comparing single-cells.
However generally, in populations of bacteria, using mRNA as a proxy for protein works pretty well, especially when comparing levels of a gene under perturbations.  Unless we believe translation or protein degradation is affected mRNA level should be a good proxy, though there can be issues with using this quantitatively because regulation is often not linear (i.e. double mRNA doesn't produce double protein).  See, for example, sRNA regulation.

Thanks for the answer, and link to the study. It makes sense that mRNA and protein levels correlate to some degree, for the reasons you mention there will never be complete concordance. I am interested to know of any studies that have looked into whether mRNA levels are at least indicative of protein activation - I am particularly interested with regard to the relevance to humans. Many thanks.


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What phenotypes can arise from gender-related aneuploidy?
Humans normally have 46 chromosomes (two copies - one from each parent - of each of the 24 chromosomes: [1:22] + [XX or XY]).
Aneuploidy is an abnormal number of chromosomes - Down's syndromes is an example of this, where individuals have an additional copy of chromosome 21.
I have read a little about gender-related aneuploidy, where individuals can have additional sex-specific chromosomes (for example XXY).
I was wondering what (roughly) the prevalence of the different combinations are, what the specific phenotypes are, and what (if any) medical complications can arise from these combinations, or indeed, what advantages could be conferred by the additional chromosome!

47,XXX
47,XXY
47,XYY

My question also stretches to whether the different combinations display a hermaphroditic phenotype?

Note that anyone with a Y chromosome is considered a genetic male. Also, aneuploidy is usually a result of nondisjunction (but can have other causes as well) and is usually not inherited.

47,XXX is called Triple X syndrome and occurs in approximately 1 in 1,000 female births. These individuals usually appear normal, but may have tall stature, a small head, and delayed development. According to this case report, the tall stature is possibly due to SHOX gene triplication, in some cases. The short stature homeobox gene (SHOX) gene is a gene on the X and Y chromosome and is associated with short stature if a copy of the gene is missing or mutated. The case report states, "The enhancing effect of SHOX gene triplication on growth was ascribed to the concomitant absence of gonadal steroids in cases of gonadal dysgenesis, allowing a prolonged period of growth."
47,XXY is called Klinefelter's syndrome. Here is an image/diagram of symptoms. These people may have gynecomastia (enlarged breasts), less muscle, less body hair, and hypogonadism (decreased testicular function, also resulting in smaller testicles and penis) due to lower testosterone production. These symptoms become more obvious after puberty, because individuals with Klinefelter's have less "masculinization" (for lack of a better word). Cognitive problems are usually present. It occurs in roughly 1 in 500 to 1 in 1,000 male births.
47,XYY is XYY syndrome and has an incidence of 1 in 1,000 male births. They appear normal but may be slightly taller than average. This study looked at the cognitive and motor phenotypes of boys with Klinefelter's and XYY syndromes. It found that both groups have generalized cognitive impairment, but XYY has more profound language impairment and Klinefelter's has more significant motor impairment. Their hypothesis is that "the more severe language-based cognitive phenotype in XYY versus KS is likely to be genetically determined on the basis of abnormal dosage of specific Y chromosome genes, but not abnormal levels of testosterone because XYY is not associated with testicular failure."

There's also 45, X0 (Turner syndrome), in which girls have a webbed neck,  broad chest, low-set ears, short stature, gonadal dysfunction, and often other conditions such as a bicuspid aortic valve, coarctation of the aorta, and vision problems. Image of symptoms. It occurs in 1 in 2,000 live births. Individuals usually have 1 X chromosome, but may also have 2 X chromosomes, with 1 of them being incomplete (source). The single copy of the SHOX gene (mentioned above) has been linked to the short stature in Turner syndrome.
Interestingly, people with 47,XYY and 47,XXX people often have normal fertility, but not in the other two cases. My uneducated guess is that the extra "normal" chromosome (for the person's sex) doesn't disrupt gonadal function.

Interesting that 47,XXX and 47,XYY both display a 'tall' phenotype, but otherwise appear normal. Any idea why this might be? You mention that 47,XXY individuals are defined as male due to the presence of the Y, but do they display feminine traits other than enlarged breasts? And what cognitive problems might occur? The more detail the better - had hoped for more than just wiki links :) Thanks

I added a little more detail. I'm not sure how 47, XXX individuals are taller, but I can see that the extra Y in 47, XYY individuals make them more masculine. Just a guess though. I'll see if I can find more info about how these disorders cause their cognitive and developmental effects.

@Luke Tried to answer some of your questions in the edits. I'm not sure about the cognitive problems though :( I hope someone else can add to it.

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Have any mutations or genetic loci been associated with exceptional longevity in humans?
Individuals that avoid age-related diseases into later life are known as 'exceptional survivors', and have increased longevity compared to their 'controls' (those that were born at a similar time, yet have 'aged' and died sooner). The Leiden Study determined that in these long-lived individuals there is a considerable genetic component contributing to the survival [Schoenmaker et al] (although this could equally be epigenetic - or even more likely, a combination of both).
I have read about studies that have identified genetic mutations that cause premature aging phenotypes (for instance mutations in the WRN gene cause Werner's Syndrome [Yu et al]).
I have yet to discover any studies that determine any genes/regions associated with resistance to age-related disease (i.e. those that age 'well'). My question is whether anyone knows of any such studies?
Arguably studies that have looked into predisposition to age-related diseases have found some mutations - for instance mutations within the 9p21 genetic locus (i.e. p16INK4a/CDKN2A) have been associated with heart disease and stroke independently (2 age-related diseases) [Wahlstrand et al]. Therefore individuals without any of these risk-increasing alleles could be considered predisposed to exceptional survival - but I see this as separate to my question. Are there any protective alleles/genes/loci that increase 'global' survival? Are there a few protective alleles, or many with small effects? Or are the exceptional survivors just without any disease-causing/risk-increasing alleles?
I am interested in published research (of course), but also in peoples general perceptions of the topic; there are plenty of theories of aging and longevity, but few facts - so please state what you 'believe', and explain why this is the case.
Thanks for your time.
p.s. I am aware that you can genetically modify lab models (e.g. C. elegans eat-2 mutants [Lakowski et al]) to live longer. I am interested in naturally occurring variants, specifically in humans (or that at least relate to human aging).

Update (11 May 2012)
I have found a study, published earlier this year, that finds a single genetic polymorphism to be associated (after correction for multiple testing) with centenarians [Sebastiani et al]. The SNP is in TOMM40/APOE (in LD), which is certainly interesting given the previous links between APOE and Alzheimer's (an age-related disease).
However, I am not convinced by the experimental design; they use age-at-death in centenarians as their cases (having genotyped them), and they use alive population controls as the controls. Whilst there is unlikely all these individual will become long-lived, a better design (to gain more power) would have been to use individuals who have died from 'premature' (e.g. between 60 and 70) age-related disease. So I am still waiting for a convincing study, but this does look promising!

i think you might mean 'longevity' rather than exceptional survival.  At different times in history different traits have been important to survival and it would be good if the title were more specific.   This is a pretty difficult question and its probably the case that there are no strong answers here yet.

@shigeta thanks for your observation. What I am getting at is the difference in longevity between individuals - i.e. are there any genetic mutations/loci associated with exceptional longevity, or put another way, have any genetic regions been associated with exceptional survival in humans - survival being simply 'not-having-died-yet'. Any study would naturally exclude accidental deaths, and only consider deaths from age-related diseases (and thus deaths from related to 'aging'). I am interested in the genetics, and therefore the (molecular) mechanism, by which the survival is achieved.

... I would gladly accept references to negative studies, or review papers that discuss the topic (i.e. are there individual SNPs that confer survival, or is it just being dealt a good genetic hand and not getting any deleterious alleles). Thanks.

It looks like you have uncovered the majority of the corpus of molecular genetics related to aging.  There is a follow up analysis just published this year by Sebastiani et al.  Pubmed has a feature that you can look at the publications that reference the paper you are looking at, and there are only reviews.  Nobody else seems to have done another yet.
You are probably right to feel that the power of the study is not great.  The researchers did try though. The Centenarians have long lived relatives and the controls had both parents die before they were 73.  Although the controls were 68 y.o. and older.  Yes not great.  The controls are very difficult because you don't want people who would die from heart attacks, hypertension and all sorts of things that would color the background.  Its a hard study to do.
No single gene will convey a phenotype like longevity.  Its basically impossible as the advantages would propogate through the population v quickly.  We could talk about how this trait is post-reproductive and about the grandmother effect, but I'm setting that aside for now.
The lack of statistical power of the study is also hampered because a mere 800 centenarians probably reflects dozens of different combinations of longevity groups.  (this is another reason all of these people are of specific european ancestry - to keep the number of variations in combinations down).  There are 281 significant mutations they found.   APOE is by far the most significant (fig1) but given the situation, just focusing on that one is not enough.
For example, even in flies it looks like the sir2-1 gene is not a single mutant anti aging phenotype.  When they cross the mutant strain with wildtype, the sir2.1 mutants live 15% longer.  Its at least one other mutation they had not detected in that caused a 50% longevity bump in the mutants.  Its probable that if there were 5 other important mutations, that the average crossover has one or more of these mutations with it too.
In order to try to understand how these loci might work together to expand life span, they created clusters of these SNPs (interestingly half of them are in genes which is relatively rare).  these clusters of genes look like they affect the lifespan together.  the paper is open and all the 281 genes are downloadable, each one of them has a known biological property you can look at.  how they fit together is still to be determined, but their clusters might point the way for now.

Thanks for your views. I do like the SNP 'profile' they determine, as it is much more likely that longevity is inherited via a large number of mutations with small effects - probably with epistatic relationships. And as you rightly point out, traits that extend maximum lifespan are unlikely to be directly selected for, unless they also confer a pre-reproductive advantage. Any lifespan extending SNPs with deleterious effects (comparatively) earlier in life will be selected against - so there is surely a lot of potential for there to be lifespan extending variants that just haven't been selected

@LukeTheDuke y.w.  at this point only so much work has been done.  All GWAS have this problem, but often there are more of them...

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Circulating factors affecting human health/longevity
Circulating factors present in young mice have been shown to promote rejuvenation of aged mice, suggesting that tissues have inherent capabilities to regenerate, and circulating factors may be promoting/inhibiting this [1].
Specifically; supplementation of GDF11 (growth and differentiation factor 11) can reverse some effects of ageing (muscle tissue rejuvenates, and some degree of cognitive improvements) [2]; conversely circulating CCL11 impairs neurogenesis in aged mice and causes cognitive impairment [3]. GDF11 decreases with age, CCL11 increases with age.

(Image from Bitto et al 2014, [1])
These factors (well, GDF11 at least) are not as highly expressed in humans as in mice [4], so the specific mechanisms may differ.
What other circulating factors been shown to affect health or longevity? Particularly in humans, but other mouse (or 'higher' organisms, e.g. primates) experiments would be interesting too.

Bitto, A., and Kaeberlein, M., “Rejuvenation: it’s in our blood.,” Cell Metab., vol. 20, no. 1, pp. 2–4, Jul. 2014.
Sinha, M., et al, “Restoring systemic GDF11 levels reverses age-related dysfunction in mouse skeletal muscle.,” Science, vol. 344, no. 6184, pp. 649–52, May 2014.
Villeda, S., et al, “The ageing systemic milieu negatively regulates neurogenesis and cognitive function.,” Nature, vol. 477, no. 7362, pp. 90–4, Sep. 2011.
Souza, T., et al, “Proteomic identification and functional validation of activins and bone morphogenetic protein 11 as candidate novel muscle mass regulators.,” Mol. Endocrinol., vol. 22, no. 12, pp. 2689–702, Dec. 2008.

What other circulating factors been shown to affect health or longevity? Particularly in human: The study on mice is published recently. I don't think it would have been studied in humans yet.

Oxytocin
It has been shown that oxytocin signaling helps in muscle regeneration by activating MAP Kinase/ERK pathway in skeletal muscles.[1]
Oxytocin is also known to promote adult neurogenesis.[2]
Myostatin
It is a paralog of GDF and like GDF prevents hypertrophy of cardiac muscles.[3]
Leptin
Though leptin is not really connected to ageing but it regulates appetite and thereby the metabolism [4]. It may contribute indirectly to ageing by reducing food intake and preventing obesity and other metabolic syndromes.
This review summarizes different soluble small (synthetic) molecules that have potential to induce regeneration.

Thanks - did not know about oxytocin! There are other things associated with health (like IL6) but I am not sure this falls into the same category really.

@Luke well ILs indicate inflammation but I am also not sure if they will fit the functional category that you are interested in. They may indirectly affect ageing process. But they don't generally affect all cells so they may be excluded. Thyroxine may also be a possible example.


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Is it possible to estimate how much variation in common phenotypes is predicted by genetics?
Phenotypes such as; skin color, eye color, nose shape, lip size, limb proportions, and skull shape.
Im trying to find a source for this quote.
"If you ask what percentage of your genes is reflected in your external appearance, the basis by which we talk about race, the answer seems to be in the range of .01 percent," said Dr.
Harold P. Freeman" From - http://astro.temple.edu/~ruby/opp/racesnyt.html

That source is outdated, but I think the answer is 1) yes, it is possible and 2) the number of genes that make humans appear different is indeed exceedingly small. Consider that (if I'm not mistaken) a tiny soil-dwelling worm (the nematoad C. elegans) has about 20,000 genes, and at least 40% of them have human counterparts. Look at dogs: a chihuahua and a great dane have the same genes. How hard can it be to believe that quote?

Its not hard I just need a peer reviewed source.

I tried and couldn't find any.

Height
This is just a single example, but "height" has received a lot of GWAS (genome-wide association study) attention, and last year the GIANT consortium published results of an analysis on 250,000 people looking at the genetic influences of height [1].
Using just 2,000 genetic variants they could statistically account for 20% of the phenotypic variation in height. Using 9,500 genetic variants this rose to 29%.
What this emphasises is that common traits (such as height) are highly polygenic; there is no "height gene" that controls it - instead it is the combination of many small-effect variants that cumulatively give rise to an individuals final phenotype (height, in this case).
Pigmentation
Pigmentation (of eyes, skin and hair) on the other hand is not nearly as polygenic. A 2013 review [2] discussed the known genetic variation for these traits. Prediction of eye colour using genetics, for instance, requires only six genetic variants "to be suitable to predict blue, intermediate, and brown colors with an overall accuracy of 0.91, 0.72, and 0.93, respectively".
So in contrast to height where even with 9,500 variants measured you would still only have 29% accuracy, you can be 91% sure that someone will have blue eyes if you know the individuals genotype at only 6 positions.
So in short the quote you reference is incorrect - we can predict many common traits with reasonable accuracy - although for some phenotypes the individual predictive capability of a specific genotype may be around 0.01% (e.g. some of the height variants).

Wood, A. R. et al. (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46, 1173-1186, doi:10.1038/ng.3097
Liu, F., Wen, B. & Kayser, M. (2013) Colorful DNA polymorphisms in humans. Seminars in Cell & Developmental Biology 24, 562-575


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What is the optimum length of abstinence for maximum ejaculation volume in humans?
There have been papers discussing this, and whether the abstinence period should be 1 week, 2 weeks or other [1, 2]. Some think the time to maximum ejaculation volume is even longer than this.

doi: 10.1530/jrf.0.0570391
PMID: 7114956

This value is going to be species-specific (cf. mouse vs. elephant), so in its current version your question is too broad.

There don't appear to be many robust studies on this, but this study by De Longe et al in 2004 suggests that, whilst the volume of the ejaculate is increased after 5-days of abstinence (in most participants), for many this did not increase further after 8-days [1].
Here are the 11 participant included (along the x-axis), and for each participant there are four coloured boxes:  dark blue, day 1 (corresponds to 24 hours' abstinence); red, day 3 (corresponds to 3 days' abstinence); yellow, day 5 (corresponds to 5 days' abstinence); light blue, day 8 (corresponds to 8 days' abstinence).

This study has a very small sample size, and really shows how the variability between people is massive. There could be many other factors affecting individuals, from smoking and drinking behaviours, to exercise and stress.
What seems much clearer is that sperm concentration increases with abstinence [2]. This study included 27 men and did not specifically ask them to remain abstinent for periods of time, but asked them how long. The average abstinence is about 2-days.

I could not find any study which investigated abstinence for longer periods - but there are undoubtedly more that I did not see.

De Longe, at al. 2004. Influence of the abstinence period on human sperm quality. Fertility and Sterility (http://dx.doi.org/10.1016/j.fertnstert.2004.03.014)
Carlsen, et al, 2004. Effects of ejaculatory frequency and season on variations in semen quality. Fertility and Sterility (http://dx.doi.org/10.1016/j.fertnstert.2004.01.039)

Very interesting findings.


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How many plants are needed to survive in an airtight chamber?
How large a garden do you need if you are to survive on them producing enough oxygen in a closed chamber?
And which are the most effective plants?

According to this news article, in a NASA experiment one man survived for 15 days in a sealed chamber containing 30,000 small wheat plants. If you read the article you will find that this did not produce a completely balanced system - some excess oxygen had to be removed, and some extra CO2 had to be pumped in.


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Difference between strand-specific and not strand-specific RNA-seq data
I would like to ask the difference between strand-specific and not strand-specific dataset.
As far as I know, strand-specific data means that we know which strand the transcript is from.
I do not have biological background. Please confirm whether it is correct. If we have a transcript, which is from sense strand, when RNA-seq is producing reads, is that first the cDNA is synthesised. Then this cDNA is used for PCR to amplify the sample? Then the reads generated could be from both strands of the original DNA?
For strand-specific protocols, what is different?
========================================================
Follow up.
Please correct me if I am wrong. There are multiple protocols to produce strand-specific RNA-seq libraries. The basic process is like:

Get the RNA;
Get its cDNA;
Somehow mark the cDNA as sense or antisense when amplifying (PCR?) (here comes the differences between different protocols);
Then REMOVE all antisense (or sense) cDNAs;
Read the reads from clean cDNA library.

The result is that, the reads from this RNA can be used to assemble the sense cDNA. And for not strand-specific libraries, using the reads would be able to assemble both the anti-sense and sense cDNA.
Am i right about this problem? Thanks.

cross post (first closed, then re-opened...) http://www.biostars.org/post/show/43507/charateristics-of-strand-specific-rna-seq-dataset/

It may be worth checking out the paper "Comprehensive comparative analysis of strand-specific RNA sequencing methods".
The most common techniques sequentially ligate different RNA adapters to the 5' and 3' ends of each RNA molecule prior to cDNA synthesis.  You will end up with an RNA molecule that looks like this (where A and B are the two adapters):
  5'-AAAAAA---------------BBBBBB-3'

These adapters are then used for cDNA synthesis and library preparation.  Finally, since each adapter is ligated to a specific end of the RNA the library can be generated in a stand-specific fashion; ie. the reads derived from the antisense strand should align to the antisense strand of the genome.
** Note that there are other variations of this technique but Illumina's protocol is one of the most popular.

Thanks, @GWW. Does it mean that Illumina is going to provide two libraries (one antisense and one sense) to users? Or they just mark every read which sense it is from? When giving reads to customers, have they removed the adapters beforehand? Or users need to remove by themselves?

By the way, I checked the paper... But cannot find much regarding the post-processing of the data...

You mean aligning the reads?

I mean, you said "the reads derived from the antisense strand should align to the antisense strand of the genome", so what would Illumina provide? If they just provide whatever reads, that is not strand-specific, right? They must filter out sense or antisense reads, or they may mark each read as whether it is from sense or antisense strand. Am I right?

No, after alignment the reads derived from the antisense strand should only align to the antisense strand of the genome (the same for reads from the sense strand).  With a standard RNA-seq preparation reads could align to either strand, which prevents you from inferring the actual strand the read is derived from.

Thank you for your patience, @GWW. Yeah, the reads from antisense strand should be aligned to antisense DNA. So given a read, we KNOW that this read SHOULD be from which strand, right?

Yes, assuming that the libraries were prepared using a directional library.


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Comparative leg sizes
When I was a child, my father showed me the classic essay "On Being the Right Size", J. B. S. Haldane.  It talks (among other things) about how large animals need stockier legs to support their weight.  I weigh about the same per leg as a 350 lb deer, but my legs are closer in diameter to those of a 1500 lb horse. Why?

Nice question. :-) I guess he was wrong or was talking about averages and not individual species...

@inf3rno:  he was clearly talking about averages, based on the body mass increasing with the cube of linear dimensions and the weigh bearing capability of legs increasing with the square.  I suspect the answer is humans stress their legs in ways that deer and horses do not, so it is advantageous to have stronger legs, but that is why I asked.

There is no way your legs are close in size to a horse.

@canadianer:  they are not as long, but in diameter they seem close.  I haven't been that close to horses, so I may be off.  When I asked a farrier this question he seemed to  think it was close.

If you can get access, check this out: http://link.springer.com/chapter/10.1007/978-1-59745-132-1_2  And then there's the musculature…

@canadianer:  No, all I get is a horse skull.  Not too useful for this.

You need to view the chapter, not just the look inside. Qualitatively, the horse femur appears ~2.5 times the diameter of the human femur. Same for the tibia.

@canadianer:  That makes the area ratio close to the weight per leg ratio.  I'm surprised.  Thanks.  That would be a reasonable answer.

I suppose it answers your specific example of horses and humans, but there might be merit in someone actually discussing the biology of weight to leg size ratios. Seems interesting and would be more in the spirit of your question.

@RossMillikan Maybe by a pony it is so long, but an average horse has about 2 fold longer leg than a human...

@inf3rno:  yes, I was more interested in diameter than length, because that sets how much weight a leg can support.  More length makes it more prone to break in buckling, but that would argue for more diameter for the longer legs.

There is an interesting relationship between the length of an animals femur, the thickness and the mass of the thickness. A point that should be taken into account is that bones can different densities and so might be able to support varying amounts. The muscle in you leg also does nothing for the support of you weight it is (basically) all in the bone. Really looking purely at the leg won't tell you much you need to strip back the tissue and get you measurements straight from that. Given the different gate strategies of various animals this can lead to differing levels of muscle surrounding the leg and could be confounding your answer.
Walter Lewin a professor of physics at MIT did a lecture series where in the first lecture he went off on one about the relative sizes and thickness of animal femurs and it is really interesting (http://www.youtube.com/watch?v=PmJV8CHIqFc - between 11 minutes and 22 minutes). The short answer was that the ratio between the thickness and the length stays similar relative to the length.


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Lifeforms concentrations of the categories of macromolecules, and Lipids
Lifeforms are formed of large, modular, organic molecules called macromolecules, large organic molecules called Lipids, and simpler molecules such as H2O.
Macromolecules are commonly grouped into the following categorises:

Protien
Nucleic Acid
Carbohydrate

The concentrations of these types of molecule by mass, in a dehydrated (therefore dead) "E. coli" are:
Protein: 55%
Nucleic Acid: 23%
Carbohydrate: 0%
Lipid: 9%

From: http://book.bionumbers.org/what-is-the-macromolecular-composition-of-the-cell

Q: Repeating the above table for other lifeforms, for the whole organism if it is multi-cellular, can examples be given of lifeforms that are particular outliers?

I have been unable to find much more useful information, however this data probably exists, I could find nothing for larger lifeforms such an animals. Around 60% of a cells cytosol is proteins, however this is by volume, not by mass.

I hope I was able to better word the question. Knowing more terminology made it possible to search for information more effectively and I was able to find an example. I can continue working on the question if needed, feedback is appreciated.

Extreme, unusually high or low proportion by mass of one of those four categories. Protein, Nucleic Acid, Carbohydrate, or Lipid. An outlier in these concentrations compared to the vast majority of other lifeforms. From the chat, bacteria have a lot of protein and grains have a lot of carbohydrate, although I think grains are only part of a larger organism.

The values that you mentioned are not absolute. There is certainly a range. If you look at plants, the composition of carbohydrates will be high because of the cellulose cell wall.


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Are there any air-plants with parts that are edible for humans?
Are any air plants (Tillansia sp.) safe for humans to eat? I haven't been able to find whether or not there are. Pineapple is in the same family (Bromeliaceae), but isn't an air plant.

Many species of the genus Bromelia produce edible fruits altho they also are terrestrial bromeliads.

Monstera deliciosa seems to fit the bill. From Wikipedia:

This member of the arum family Araceae is an epiphyte with aerial roots...
Wild seedlings grow towards the darkest area they can find until they find a tree trunk, then start to grow up towards the light, creeping up the tree.[6][dead reference link]
Fruit

The fruit of Monstera deliciosa is up to 25 cm long and 3–4 cm diameter, looking like a green ear of maize covered with hexagonal scales.
Fruits of plants of the Araceae (Arum family) often contain Raphides and Trichosclereids – needle-like structures of calcium oxalate.
The fruit may be ripened by cutting it when the first scales begin to lift up and it begins to exude a pungent odor. It is wrapped in a paper bag and set aside until the scales begin popping off. The scales are then brushed off or fall away to reveal the edible flesh underneath. The flesh, which is similar to pineapple in texture, can be cut away from the core and eaten. It has a fruity taste similar to jackfruit and pineapple. The unripe green fruits can irritate the throat and the latex of the leaves and vines can create rashes in the skin, because both contain potassium oxalate: that is the reason why the fruits have to be consumed when the scales lift up.[9]
Plants grown indoors in temperate regions occasionally produce flowers and fruit.
In Mexico, a leaf or root infusion is drunk daily to relieve arthritis.[6][dead reference link]
In Martinique the root is used to make a remedy for snakebite.[6][dead reference link]

Here's a picture of the fruit by Bruno Navez:

Dragonfruit (Hylocereus undatus) is also hemiepiphytic. You may also be interested in this page about epiphytic cactus fruits and this post on Yahoo! Groups where I found M. deliciosa. The author claims:

I have eaten the fruits off my Acanthostachys strobilacea, an epiphytic bromeliad, and they are reminiscent of the related terrestrial pineapple just slightly more acidic but still very palatable (to me at least). [Hyperlink added.]

Self refilling fruit bowl?

I can't find anything on anyone growing these without an environment like soil to surround them, not just by just placing them down indoors and spraying them once or twice a week with water for example. Everything says or shows planting, sowing, plants in soil, plants in plant pots full of soil, etc…

Yes! Tillandsia usneoides and recurvata are both edible https://www.eattheweeds.com/spanish-moss/

Wth my answer already has an explanation and citation

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Why do humans stop growing?
Humans grow most quickly when young, and to a lesser extent in preadolescence and early adolescence. However an equilibrium is then reached in adulthood, typically at around 1.6/1.7 meters tall.
I have found the age at which people "stop growing", meaning that they are at their tallest hight, is around the age of 21-25.

Why is it beneficial for humans to grow to 1.6/1.7 meters?
Why is there so so much variation in this, with some reaching there full height in there early teenage years?

It would probably be useful if you read this and then asked a more specific question: Wikipedia: human height

Human bones grow in length due to the effects of human growth hormone on the epiphyseal plates near the ends of the bone. Over time these two growing margins decrease in size, or width, until those special cells are either gone, or can no longer respond to hGH. At that point the body has no mechanism to increase the size of the skeleton, and growth stops. In females this tends to happen earlier than in males; approximately 2 years after the onset of puberty for females.


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How many dinosaur genes are in a chicken genome?
I read recently that we can "work back" to a dinosaur by selectively turning off certain genes in chickens, and we were able to create a chicken with a snout instead of a beak.
How many dinosaur genes are still hanging around in a chicken genome? I understand there is a lot of overlap for vertebrates, but shouldn't these genes have shuffled out over time?
Otherwise, couldn't we work back to worm or even earlier life forms? Is the whole history of a creature's evolution contained in its genome?
Edit This was a news story about a month ago. Here's excerpts from the BBC's write-up: "Chicken grows face of dinosaur: A chicken embryo with a dinosaur-like snout instead of a beak has been developed by scientists"

To understand how one changed into another, a team has been tampering
  with the molecular processes that make up a beak in chickens.
By doing so, they have managed to create a chicken embryo with a
  dinosaur-like snout and palate, similar to that of small feathered
  dinosaurs like Velociraptor. The results are published in the journal
  Evolution.

Link to Evolution paper included.
Also this piece from about a year ago: "Paleontologist Jack Horner is hard at work trying to turn a chicken into a dinosaur"

In 2009, the world’s most famous paleontologist made a bold claim. In
  “How to Build a Dinosaur,” Jack Horner proposed re-creating a small
  dinosaur by reactivating ancient DNA found in its descendants,
  chickens.
The toothy snout is already here. At his lab at Harvard Medical
  School, Matthew Harris has made chicken embryos that express ancient
  genes for the growth of conical, crocodile-like teeth.

What were you reading?

Related: attempting to give a chicken a tail: http://www.evodevojournal.com/content/5/1/25

@canadianer I edited the question to include an article about a paper in Evolution, and also an earlier article about a project by Jack Horner.

The must-see Jack Horner's TED talk about the "chickenosaurus" https://www.ted.com/talks/jack_horner_building_a_dinosaur_from_a_chicken?language=en

Chicken is a dinosaur..It is a theropod (same group as T.rex )

@WYSIWYG: Wrong.  If chickens are dinosaurs, then mammals are reptiles, and all of them are fish.  As to having common genes, humans share about 50% of their genes with cabbages :-)

@jamesqf You are wrong. Please read the wiki page on theropods. Reptile is not a clade (monophyletic group). Like humans are primates, chicken are theropod dinosaurs.

Be careful when reading media as they tend to exaggerate. That Abzhanov paper is interesting; it will be better when it's published and the figures released. He does a lot of evo-devo work surrounding beak development. What they did in this paper was to inhibit in chick embryos two signalling pathways (which are actually conserved amongst all animals, not just birds and reptiles) so that their expression domain more closely matched that of extant reptiles (which lack medial activation, as opposed birds). They found that doing so results in premaxillary and palatine bones which resemble ancestral archosaurs. While incredibly interesting, this is a far cry from creating living chickens with dinosaur snouts.
To somewhat answer your question, chickens do not contain all of the genes that dinosaurs once had. They will have both gained and lost information. There will be much conservation in the same way that there is conservation between all organisms. For example, the Wnt pathway studied in this paper is conserved across all animals, from chickens to humans to placazoans to, presumably, dinosaurs. It's impossible to say how much has changed since we do not have any whole dinosaur genomes, but it's also important to note that it's not only which genes an organism has but also when and where they are expressed and how they interact.

What does evo-devo mean?

@user151841 evolutionary developmental biology


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What's the exact meaning of and how to derive the coefficient of the path from sire to offspring?
According to Wright's Coefficients of Inbreeding and Relationship,
the coefficient of the path from sire to offspring is given as
$p_{o \cdot s}=\frac{1}{2}\frac{\sqrt{1+F_s}}{\sqrt{1+F_o}}$
But how to get this formula?
And what is the  exact　meaning of this path? It's the path from sire's genetype to offspring's corresponding genetype, or just from one allele in sire's locus to offspring's one allele in the corresponding?
Furthermore, Wright gives
$F_o=\frac{1}{2}r_{sd}\sqrt{1+F_s}\sqrt{1+F_d}$
From Wright's another paper Correlation and Causation we know
$d_{o \cdot s}+d_{o \cdot d}+d_{o \cdot \bar{sd}}=1$
in which
$d_{o \cdot s}=p_{o \cdot s}^2$,
$d_{o \cdot d}=p_{o \cdot d}^2$,
$d_{o \cdot \bar{sd}}=2p_{o \cdot s}p_{o \cdot d}r_{sd}$
Thus
$\frac{1}{4}(1+F_s)+\frac{1}{4}(1+F_d)+\frac{1}{2}r_{sd}\sqrt{1+F_s}\sqrt{1+F_d}=1+F_o$
$\implies F_s+F_d=2$
Since $F_s,F_d\le 1$,
we have $F_s=F_d=1$......
Incredible. But what's wrong with it?

I think it must be forwarded to Math.StackExchange rather than stagnant here

In The Method of Path Coefficients(Wright1934) Wright showed that the path linked two objects is the path linked their genetic constructions. Probabily "genetic construction" means sequences of loci rather than a locus. However, if we consider a genotype of a locus as a random varible, the total genetic construction of a germ cell will be a random vector. Hence something like correlation or covariance between genetic constructions of germ cells is usually in form of matrix or tensor rather than a real number $F$(which is Wright actually showed). Moreover, if germ cells are made of several chromosomes, the correlation between corresponded pairs of loci in two germ cells may be not equal to each other. (e.g. chromosomeI of $G_1$ and $G_2$ are from the same ancestor, but chromosomeII are not)

So I guess things were worked out in this way.
Suppose $G_1=(g_0,g_1,\dots,g_n)$,$G_2=(g'_0,g'_1,\dots,g'_n)$. If $g_i$ is mth chromosome's kth locus and original from an ancestor germ cell $G_A$. And let us only modify $G_A$'s mth chromosome's kth locus, then $g_i$ would get modified; $g'_i$ would also get modified if only $g'_i$ is also original from $G_A$; loci on $G_1$ or $G_2$ in other chromosomes or in mth chromosome but other loci would not get modified anyway.  Hence $\rho(g_i,g_j)=\rho(g_i,g'_j)$ for any $j \neq i$. Therefore $$cor(G_1,G_1)=cor(G_2,G_2)=\mathcal{I}\dots(1)$$ $$cor(G_1,G_2)=diag(\rho_{g_0,g'_0},\rho_{g_1,g'_1},\dots,\rho_{g_n,g'_n})\dots(2)$$
Since $1=\sum\limits_{i}{p^2_{G_1\cdot g_i}}+\sum\limits_{i\neq j}p_{G_1\cdot g_i}\rho_{g_i,g_j}p_{G_1\cdot g_j}$ and by $(1)$, $1=\sum\limits_{i}{p^2_{G_1\cdot g_i}}$. hence $p_{G_1\cdot g_i}=\frac{1}{\sqrt{n}}$ if we weighted every $g_i$ the same. And $p_{g_i\cdot G_1}=\rho_{g_i, G_1}=\rho_{G_1, g_i}=p_{G_1\cdot g_i}=\frac{1}{\sqrt{n}}$, Analogously to $G_2$.

Therefore $\rho_{G_2,G_1}=\sum\limits_{i,j}p_{g_i\cdot G_1}\rho_{g'_j, g_i}p_{G_2\cdot g'_j}=\sum\limits_{i}\frac{1}{n}\rho_{g'_i, g_i}=E(\rho_{g'_i, g_i})$. i.e. in this case the correlation between germ cells are just the mean of correlations between their corresponding loci.
So similarly the coefficient of the path from a sire to its offspring is also the coefficient of the path linked their genetic constructions. and in detail is from sire's  genetic construction to its components, then from components to offspring's construction's components, then to offspring's construction.
The derivation was given in the article. In fig6 the coefficient of the path from a sire to its offspring can be gotten from the product $p_{o \cdot G_s}p_{G_s \cdot s}$.
$p_{G_s \cdot s}=\sqrt{\frac{1+F_s}{2}}$; $p_{o \cdot G_s}=\sqrt{\frac{1}{2(1+F_o)}}$.
So $p_{o \cdot s}=\frac{1}{2}\frac{\sqrt{1+F_s}}{\sqrt{1+F_o}}$

If $F_s=F_d=F_o=r_{sd}=0$, then $d_{o \cdot s}=p^2_{o\cdot s}=d_{o\cdot d}=\frac{1}{4}$, $d_{o \cdot \bar{sd}}=0$ . Thus
$d_{o \cdot s}+d_{o \cdot d}+d_{o \cdot \bar{sd}}=\frac{1}{2}<1$. This result means  parents' genetic constructions are not sufficient to determine their offsprings'. But it is very normal the same parents have both sons and daughters. Which causes their difference in gender are random factors.


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How to quantitatively measure work done by a biological system?
Thermodynamic efficiency can be expressed as the ratio of Work done(W) to Energy invested (Q).
Thermodynamic efficiency= W/Q

How can one measure work done by a biological system?

Shouldn't this be W/Q? If Q/W then "thermodynamic efficiency" is very high when you do little work but put in a lot of energy. Doesn't seem very efficient.

Measuring the work done by a biological system seems pretty impossible. Imagine how many different ways one cell of your body uses energy (ATP). You can't really measure all the work done by every cell on a macro scale. Metabolic efficiency has been defined as... "health". That seems just a little ambiguous. That's why we use things like averages to determine if energy use is normal or not, such as in metabolic age.
In short, work is a more tangible term in discrete physics examples, but there is so much complicated energy use in biological systems that total systemic work can't be easily defined.

Just like with work in non biological systems, one needs to consider the path that the system takes. The definition of work for a process depends on a path. As well, different types of potential energy can be equilibrated for different systems.
So what's your system? Let's say a plant is in a sealed system, with a movable piston. It starts with a small amount of CO2 gas and a lot dissolved in solution. Then the plant is placed in a light source, which can enter the closed system and drive photosynthesis. As the plant makes oxygen it comes out of solution and increases the volume/pressure of gas in the system. As a result, the photosynthesis could do work against the piston. Then, since the energy inputted from the light source can also be readily estimated, the thermodynamic efficiency of "driving a cylinder using photosynthetically derived oxygen gas."


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What are the mechanisms that cause bananas to ripen? How is or isn't it related to the banana peel ripening?
So recently our commercial kitchen got some bunches of bananas with a curious feature. On the inside they were firm and delicious (no jokes, please), on the outside they looked incredibly sickly. Specifically, they had this grey/green/brownish yellow with lots of black spots, some of them with depth.
Situational details: these are Cavendish bananas, and a weekend of ripening in the open air did nothing for them.
This got us curious about the mechanisms of banana ripening - it's clearly not as closely related to how good the banana is to eat as we previously thought! Can someone explain it more?

Check the situational details I just added.

Ah, not Canarian then. In any case, this question is not really on topic here as it has nothing to do with the underlying biology. I would suggest,however, that you have one half-opened banana, allowing your customers to see that the inside is fine despite the blemishes. If you place the peeled banana next to the others, your customers should see that they should never judge a banana from its peel!

I couldn't find a cooking/culinary/restaurant .SE, so I posted it here as the most relevant field.

Yes, indeed, and you took pains to politely acknowledge the possibility of its not being on topic. My last comment was not a complaint. You could try your luck over at cooking.SE.

If you flag your post, you can ask a mod to migrate it for you.

Actually, I'm going to do it manually, so I can split the topic into the mechanics of it (here) and what other people do to reduce the problem (cooking.SE).

You can get the basic mechanistic details on wikipedia http://en.wikipedia.org/wiki/Ripening - for example fruits are shipped before ripe and then gassed with ethylene. This is a chemical important in apoptosis and ripening processes, it is given off by ripe/damaged fruits. So, what you can do is pick fruit before it is ripe, store it in a cool dark place then when you want to ripen put the desired amount in a paper bag with a few slices of apple.

@rsegal (can I recommend you change this question completely to ask "what are the mechanisms which cause fruit to ripen?" or something along those lines)

I'm closing this as it has been answered on Seasoned Advice


text:
In which way would the yeast cell cope with the excess amount of methionine in the growth media?
I guess that when there is surplus of methionine in the cell it is incorporated in the TCA cycle as a succinyl CoA, with cysteine as a by-product. But now the cell has the surplus of cysteine. What does it do with it? How does it remove excess sulphur from the cell? Would that process somehow influence NAD+/NADH balance?

Is there any evidence that methionine is toxic to yeast?

I know there are some strains of yeast that are unable to form colonies on the medium that has high concentration of methionine in it. I have reformulated the question.

In humans, cysteine first fills the glutathione pool (which itself is a reservoir for cysteine, apart from reduction equivalents and biotransformation). Excess cysteine can go three ways,

oxidation to sulfoalanine and further hypotaurine and taurine, via still unknown enzyme(s)
degradation to lanthionine and toxic H2S
degradation to serine and toxic H2S

Since cystathionine gamma lyase which (its L-cysteine desulfhydrase activity) catalyses 2 and 3 has homologues in yeast, my guess is that H2S is produced there too. Further oxidation of H2S happens through an ancient mitochondrial pathway which is highly conserved as well, yielding sulfite and sulfate. Sulfate then makes for a bit acidic environment but else is non-toxic.
I have linked to reactome.org entries where you can find references. Filling up the yeast's glutathione pool (if it happens) would move the NAD+/NADH balance to the NADH side, i.e., fill up reduction equivalents, too.

Thanks for pointing me to reactome.org. Wasn't aware of it :)


text:
Scoring sequence against position weight matrix
I have a weight matrix of length 20 x 15 (amino acids x sequence positions). Each element of my weight matrix is a relative probability
If I have a sequence say "AAPGTGASMHSGLLW" how would I score it against the matrix? I tried taking the product of probabilities corresponding to the matrix, but I end up with a really small number
Any ideas?
Edit:
Consider the simple matrix:
    1    2   3     4
A 0.3 0.90 0.5 0.0001
B 0.2 0.05 0.4 0.2
C 0.5 0.05 0.1 0.8

The best match is, with a score of:
CAAC = 0.5 * 0.9 * 0.5 * 0.8 = 0.18

If you change the first letter to an B instead of C
you get a match, with a score of:
BAAC = 0.2 * 0.9 * 0.5 * 0.8 = 0.072

Which is a huge difference for such a small change...
This is even worse with my larger matrix since the score is easily affected by small probabilities

Maybe your sequence is a bad match?

I edited my question with an example. See above ^

The probabilities are correct. You must take the product (in log space this is equivalent to sum). The reason the probability looks small is just that you are perhaps thinking the score should be close to 1. However, this is not the case. To get a score of 1, you need the PWM to have 1/0/0/0 at all positions and get a perfect match.
So what should you compare to? What people usually do is compare this to a background distribution, the easiest being uniform, so the PWM is 0.25 everywhere. For your example, the score in this case will be 0.25^4 = ~0.004 and this is what you should expect by random.
This is why people usually look at the ratio between the score of the PWM relative to the score for the background model (and usually take the log2 of that), which in your case will be 0.18/0.004 = ~46 so the sequence you got is 46 times more than you would expect by random! And for your second example, 0.072/0.004 = ~18 times more than expected, so that is still high.
More conceptually, what you are doing is comparing two probabilistic models, your PWM and a background PWM, and comparing the probability to get your observed sequence according to each one of them. This is a common approach in general for comparing probabilistic models, even if they are more complicated.

Thank you for that detailed answer! I am trying to get scored between 0 and 1 (i.e. a probability).
I thought about taking the ratio of the score to the best match to the PWM.
I'm guessing from what you just explained, I would have to divide by 0.004 for the sequence score and the best score. So using the score from BAAC : (0.072/0.004)/(0.18/0.004)? Would this work?

Also, if I have real life background probabilities (i.e. determined from a large database) would I just take the product of everything?

@Omar regarding normalizing the score between 0 and 1, you can normalize the score in the way you suggested (note that the background model probability cancels out so you don't need it). The interpretation then will be different, as it will not tell you anything about how likely it is to find your sequence by random. So I guess it depends what you want to use the scores for. Note that as long as you divide all your sequences by a constant score, be it background or the highest probability, the ratios between the scores of different sequences will be maintained.

@Omar regarding background models, if you are not using uniform background and you are making predictions on a given organism, it would make sense to calculate the A/C/G/T percent of the whole genome, and use that as the background model, i.e. PWM with the same length as your PWM of interest, just that all positions have the same genomic probabilities.

Thanks. I am using amino acid sequences so I guess the background model would be (1/20)^15 then. I run into the problem when dividing the product of my score by the product of the best score since they are both very small numbers: for example 10^-19 / 10^-12 = 10^-7, which is indeed between 0 and 1 but it does not give me a sense of how similar it is to the best. What I am looking to score is how well some peptide matches a PWM (ranging from 0 to 1). Does this make sense? Appreciate the help

@Omar since the numbers are very small, you can work with their logs (of course then you need to change division to subtraction). Apart from that, I still don't see why you think the numbers are "don't make sense". As I explained, 10^-19 can be either high or low, depending on what you are comparing to.

Well I see how the numbers make sense now. But my problem is rather getting my numbers to a 0-1 scale so that I can apply a cutoff in mind (maybe 0.8?) to filter peptides by the ones that bind better compared to the best match. I see how 10^-19 / 10^-12 would give me a number between 0 and 1. If I log2(10^-19 / 10^-12) I would get -23. Which essentially means there is 23 times less chance of my sequence occurring compared to the best. But my problem is mapping that value back to a probability 0-1

let us continue this discussion in chat

According to [this page][1], you should take the sum and not the product:

Once a Profile has been derived from a set of functionally related
  sites, the Profile can be used to scan a query sequence for the
  presence of potential sites. Usually you run a window the length of
  the matrix along the sequence, and sum the coefficients from the
  matrix corresponding to each nucleotide in each position on the window
  sequence. Formally, the score of a matrix M for a site s of length l
  (s = s1, ... , sl, and sk being one of {A, C, G, T}) is computed as

$m_s=\sum\limits_{j=1}^lM_{s_{lj}}$
I highly recommend you read the rest of the page, the author, Roderic Guigó, is an authority on the subject.

He is assuming by Msij he means the log(frequency/background.frequency). I dont have the frequency matrix. Just the probabilities.

It shouldn't make a difference, the score for the sequence should still be the sum of the scores at each position.

@terdon actually it does make a difference, in probabilities it is a product but in log space it is mathematically equivalent to the sum...

@Bitwise, all I am saying is that the summ is still sufficient to score the match. Said sum will not, of course, be a probability but it will be maximized at the region of the highest sequence similarity. In any case, your answer is much better and you are clearly more knowledgeable on the subject than I so I won't argue the point.

@terdon not arguing, it is just important to correct so the information will be clear. Consider a PWM that in first position has A with probability 1 and second position C with probability 1. The sequence AA has a probability of zero for binding this sequence (1*0), but the sum will give you 1, which is half the maximal score and thus clearly wrong. PWM is a probabilistic model of the binding affinity so it has to be manipulated as probabilities.


text:
explanation of conjugation experiment in bacteria
In this paper (On the expression of a structural gene) I am confused about what is being plotted in Figure 6. The x-axis contains the fraction of radioactive day and y-axis contains the enzyme formation (I assume this is just the concentration of enzyme like Figure 5?). Different point shapes are different times after mating. If so, why are there multiple points of each time? Why are there multiple measurements for say 48 mins past mating? Is the correct interpretation that for each condition, they are showing the fraction of radiolabeled material for each number of day post thaw, like figure 5? It is unclear.

When I tried to access the pdf you cite (or the host webpage ac.els-cdn.com) I get an Access Denied notification. Could you post a full citation so we can look it up, or post a different link to the pdf?

@A.Kennard fixed link

There are three variables being shown here:

The amount of time samples were stored in the freezer to allow $^{32}$P decay (x-axis),
The length of time samples were allowed to grow in a nonradioactive environment before being frozen (points with different symbols), and
The rate of $\beta$-galactosidase production when cells were thawed and grown in the presence of the transcriptional inducer IPTG (y-axis).

Let's break those down a bit more:
The x-axis measures "fraction of $^{32}$P decay." In terms of the experimental protocol, this translates to "length of time left in the freezer." For the $^{32}$P-decay experiments, samples are stored in the freezer for various lengths of time, where metabolic processes, like DNA replication or enzyme production, are shut down or drastically slowed. Although metabolism has been shut down, $^{32}$P can still beta-decay into $^{31}$S (according to Wikipedia), which promotes the breakdown of any DNA containing (formerly) radioactive isotopes. The implication is that the longer you leave samples in the freezer, the more $^{32}$P will decay and the more formerly radioactive DNA will be degraded. So you can read the trend in the x-axis either as "increasing fraction of $^{32}$P decay", "increasing amount of time left in freezer," or "decreasing fraction of intact $z^+$ DNA." The different points corresponding to, say, "48 minutes past mating" correspond to samples that were frozen down 48 minutes past mating but kept in the freezer for different lengths of time.
The y-axis measures "percent survival of enzyme-forming capacity." I think that it's safe to interpret this as the rate of $\beta$-galactosidase production relative to the rate with no $^{32}$P-decay. In other words, if you plot $\beta$-galactosidase production over time (as in Figure 5) for different conditions, you'll measure a slope, the rate of enzyme production. If you normalize all these rates from different conditions by the rate when there was no radioactivity (and thus no degradation of $z^+$ DNA in the freezer), you'll get the values plotted on the y-axis.
The final interpretation of this plot is that the $\beta$-galactosidase production rate is sensitive to the loss of the introduced $z^+$ DNA when the loss occurs soon after mating, but if cells are allowed to grow enough to divide once or twice before DNA degradation, then the loss of the original conjugated $z^+$ DNA does not affect the $\beta$-galactosidase production rate. This data is consistent with the idea that the DNA form of a gene is constantly required for sustained $\beta$-galactosidase production, and after several rounds of division the $z^+$ DNA has been replicated and is no longer radioactive and prone to degradation.
There's a lot of data in this plot (ah, the elegance of 1960s molecular biology!) so it can take a while to digest. Hope this helps!

Great answer - was working on an answer myself, but no need for it now. It's worth adding a historical note that this paper, and especially an earlier (1959) paper from Pardee, Jacob & Monod, formed the basis of the idea of mRNA - an unstable intermediate between gene (DNA) and protein. And this in turn led to the experiments of Nirenberg that are touched upon in this question


text:
difference between ischemia and oxygen-glucose deprived medium
Ischemia is roughly defined as insufficient blood flow to a specific region of the body and in the literature it is generally considered as causing oxygen and glucose deprivation in the tissue and therefore cells. Can we induce ischemia by depriving cells from oxygen and glucose in the medium or does it require more than that and ischemia is more complicated?
edit: i know the definition of ischemia maybe i should have said 'mimick' instead of 'induce'. Anyway, here is the better version of my question:
Ischemia is defined as blood flow shortage in a tissue and it is one of the main causes of non-apoptotic cell death. In an article i was reading, the authors claim that they induced ischemia in the glucose-serum containing medium. Does this mean they deprived the cells from oxygen and thus mimicked ischemia? Is this the way to create ischemia in a cell culture? What other ways can there be? Should not cells also be deprived from glucose?

Ischemia leads to tissular hypoxia and glycopenia due to an insufficient blood flow (e.g. due to atherosclerosis or thromboembolic diseases). Ischemia always result in hypoxia.
Hypoxia can occur without ischemia (e.g. due to anemia).
Tissular glycopenia can occur without ischemia (e.g. due to hypoglicemia).
Ischemia is a shortage in blood supply. Depriving cells form oxygen and glucose isn't the same as cutting their blood supply.


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Are we evolving to age slower, or to live longer aging at the same rate?
I wonder why most of us find senior people lovely and charming. Is it because to love grandpa and granny is vital to our survival as a species, in the same way as we love babies? If so, it would mean that we are either aging slower with time, or being selected somehow to love senior people despite their physical appearance, while they are aging at the same rate but living longer.
Update
Let's say every common characteristic among individuals of the same specie is product of evolution.
Many caracteristics are passed by the individuals to their offspring, but only those that were advantageous in some way or another allowed the offsring to survive and pass that caracteristic to their decendants in turn.
Our love for babies and elders is one of those caracteristics that we as indivudials of  a species have in common.
That make us to want our elders to be with us as much time as posible, just becouse our affinity with them.
This love for the elder have to be an advantaje that worthed to be selected by the evolutionary process, and this caracteristic is reflected in the tendency of the life expectancy to increase generation after generation.
One could say, that those elders that age slower would be more likely to benefit from the love of their offspring, than those that age faster.
On the other hand, to age slower may not make a difference at all, just because we love our elders despicte their physical appearance.
I'm considering the cultural and technological advantages that allow our elders to live longer to be a product of evolution too.
So my question is wheter we are evolving to age slower, or to live longer aging at the same rate?
As a side note, we are becoming more like what they call a super-organism, as most of os can only benefit of a longer life spam by living in society. Just like an anthill or a beehive. If that is true, we are not evolving to age slower.

Your premise is debatable and your argument is, frankly, nonsensical. Why does feeling affection for our elders imply that we are "aging slower with time" (whatever that means)?  Voting to close.

@terdon We feel affection for our elders, as a matter of fact, but that behavior has to be shaped somehow by evolution. Also life expectancy is increasing generation after generation, and we still feel such affection. The implication is obvious to me, we are evolving to love people that live longer. What I'm asking is whether we are being selected because we love people that age slower, or because we love elders despite their physical appearance.

Not all behaviors have to be shaped by evolution. We do plenty of things that are counter-productive to our continued existence as a species and/or as individuals, as a moment's reflection will tell you (nuclear proliferation, global warming, drinking and driving, etc.). Please read this question and its answers to better understand the terms "evolution" and "evolve."

@MattDMo I would say every behavior that is common among individuals of a species has to be due to natural selection, because it's being selected among many other behaviors that weren't. In the case of the love for the elder, it would mean that people that didn't experience such feelings in the same way was not favored by natural selection.

i wish we could be more tolerant of questions like this.

The question has a more or less official scientific name - the grandmother effect (or hypothesis).   In the rational world of evolutionary biology, children love their parents for the same sort of reason - the parents give them resources to gather their strength and develop as adults so they can have their own children with the most advantage that they possibly can.
Humans are off the charts in the amount of resources we invest in our children - our lives are 1/4 to 1/3 over before we sometimes leave our parents household (in some societies of course they never leave the house, but step into an extended family).  This may be one of the reasons we are so successful as a species - we live in practically every place we possibly could and have no danger of competition from any other living thing excepting ourselves.
The grandmother effect is essentially the idea that if women, who are more attached to the offspring in more cases than fathers, continue to live and help support the grandchildren and make them more successful, then this will allow post menopausal women to have a longer lifespan (which they do).
The evolutionary biologist Sara Hrdy, emeritus UC Davis, has written quite a bit about the nuances of the evolution of the role of motherhood - reading some of her articles or books might give you a deeper sense of how profoundly filial love has shaped human beings.
--- more answer this stuff may or may not be worth reading depending on how broadly you want to understand this question...
Its important to say that many of the expansions of human average human lifespan have not been genetic.  Its commonly cited that sewer systems, clean water, antibiotics and plentiful food are the three most important factors in human lifespan - and before modern developed world nations, the average lifespan of human beings was somewhere in the 30s.  And there are significant lifespan differences in regions where these factors and others (education of women, access to prenatal and early care etc) are available.
Studies continue to be published that examine environmental and lifestyle factors compared to genetics and it seems that environment and lifestyle can make an astounding difference.
But genetics undoubtedly has a role to play here too.  There are probably some individual humans and animals which have evolved to live longer.  This has been found to be genetically related in some humans by demographics and family lines.
Still there are genetic factors that are being identified.  I think its a common misconception that evolution changes the entire species, but its not really true.  There are some who have that tendency and others who do not - at times there will be more of say shorter lived people and other times longer lived individuals will be more common.  It depends upon whether the living conditions of the population really favor one tendency over another.  Usually shorter lived individuals can assume to have other advantages, say the ability to store energy more efficiently.

One could say that cultural and technological advantages are part of being human, if so they are being shaped by evolution too. In the other hand that could imply that our species is becoming more like a superorganism, like an ant-hill, where one organism  can't have the benefit of a longer live span by itself, but only if one live in a colony.

that's true, though in terms of biology and evolution that's language that needs careful discussion - superorganism sounds a little like group selection and not everyone is into that idea ;)


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Which sex has higher variance of reproductive rate in modern societies - male or females?
Who has a more varied reproduction rate in modern western societies - men or women? The average rate is the same of course, but I wonder which sex have higher variance - higher variance means that reproduction is more concentrated in some subgroup of given sex so this subgroup transfer their genes at higher rates then average rate in whole population. The next question would be 'is that subgroup somehow homogeneous' ?

Good question.  I wouldn't be surprised if the distributions of fecundity (offspring reduced) were multi-modal (have more than one peak).  When you just look at sex, the answer is lopsided in more than one way.  http://www.telegraph.co.uk/news/newstopics/howaboutthat/3685314/Young-women-have-more-sexual-partners-than-men.html  http://www.nytimes.com/2007/08/12/weekinreview/12kolata.html

Sperm are cheaper than eggs
According to Bateman's Principle the males of a species are (typically) more able to produce a greater variance in the number of offspring. This is because generally the males can produce many gametes and achieve many matings whereas females are limited by the number of eggs, resources, and time lost to pregnancy. Therefore, just by simple logic, it would appear likely that males have greater variance in mating success (strong/attractive males would be able to mate with many females while weak/unattractive males would have little to no offspring). See Wikipedia for an introduction to Bateman's Principle. Also see this paper about sexual selection and mating success variance.
An Example
An extreme example of higher variance in male mating success is that seen in lekking species. These species form groups of males called leks where females enter and choose mates. Often one/few males will sire the majority of offspring in the next generation. Fallow deer are one lekking species where males have higher mating success variation than expected by chance.
In Humans
Here is a blog post discussing a paper which showed that males have greater reproductive success variance than females in humans. In the paper see table 1 (Vm:Vf column) which indicates the majority, particularly those close to ancestral type lifestyles, have much greater variance in male reproductive success. Only one measured (Finland) is the reverse pattern.

nice reference.  polygamous cultures are a good illustration (see blog post table 1) of how 'some guys get all the luck'.


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Does male or female DNA contain more information?
I wonder if the male or the female DNA contains more or less information than the other?

Related: https://biology.stackexchange.com/questions/57479/do-men-and-women-have-the-same-number-of-genes

Define "information" — females have more base pairs and more individual genes because X's are larger than Y's, but males have a large number of unique loci and thus unique genes, because they have an X and Y chromosome.


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What insect is this on the photos?
What insect is this on these photos?
Found in Russia in the living room.

I think I found it. It can be Lacewings:
https://en.wikipedia.org/wiki/Chrysopidae

Yes, it look like a lacewing. Many species in Chrysopidae overwinter as adults, and these are often found indoors in the autumn where they are looking for overwintering sites.

And given the state of genus-group classification of the family (for a slightly dated worldwide system see here) that's as far as anyone can go on the available evidence.


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What is this plant on the photos?
Found in Kustaankartano, Helsinki, Finland in June 2015.
(for closeup view, copy image URL and open in new window and then zoom in)

could we get some close up pictures of the flower? I'd suggest it may be some kind of clematis (is it a climbing plant?)

@rg255 I don't have them from another angle, unfortunately, however you can copy the image URL, open it in a new window and watch the photo much closer than SE shows.

@rg255 what about climbing, I've added one additional photo to the question, please check it out. It seems to be that it's climbing on a metallic netted fence.

It is one of the many species and hybrids of Clematis that is grown in gardens. It looks similar to the common Clematis alpina but might be a closely related species or à hybrid.

(Picture of C. alpina fr.o.m. Wikipedia)

Would have been my guess too. Those stems look pretty diagnostic. Do you find that quite often the answer to the question "what's that interesting blue-flowered vine?" is Clematis? :)

@Oreotrephes Hehe, yeah, it's often a good bet, at least in European gardens.


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Ammonia smell after physical exercise? What is the explanation?
After exercising vigorously one can sometimes smell ammonia and it feels like it's coming from within the nose actually. There is no indication that others can smell it when I do (i.e. seems to be internal rather than external). I know I'm not the only one experiencing this, but I was wondering what the physiological processes are that are responsible for that?
Also: is this merely a perceived smell or is there actually ammonia in/on my body after the exercise.

it might be the odour of the sweat. But I don't think that is the smell of ammonia.

Do you eat a lot of protein to promote muscle growth?

@NeoDevlin: well, I eat "a lot" of protein, yes, but not in particular for muscle growth. AFAIK the body cannot metabolize as much protein as for example body builders usually take in.

There are two explanations which come to mind answering your question:
I asked if you eat a lot of protein, because the amount of protein which some body builders consume leads to renal failure. And one sign of renal failure is the smell of ammonia. It's a quite common problem. But I don't think this applies to you. Your protein intake shouldn't exceed 2g per kg of body weight per day, some body builder eat up 4g per kg.
Ammonia is normally linked to your protein metabolism, which is higher during and after sport. The smell of Ammonia can occur when your carbohydrate reservoir is depleted and the body is mainly using protein as fuel. This effect also occurs when fasting or in patients with diabetes.
Try to keep your blood sugar level raised during exercise by eating something, like a banana, powerbars (also known as high energy bar) or directly dextrose. Or eat more long-chain carbohydrates. And this always depending the form of sport you are practicing. Just like the pasta party prior to marathons.

Wow, renal failure sounds severe. But I have this only after exhausting exercises, not every type of exercise either. Yeah, I think I don't get over the 2 g/kg limit, though. Will try the advice about carbs.


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Perception of distant lights without glasses
I am fairly short-sighted and wear glasses pretty much all the time. Naively, I would expect that when I take my glasses off, the image I see should look very much the same as as a photograph that's out of focus, or an image to which someone has applied a Gaussian blur filter. In bright conditions this is mostly true, but at night if I look at distant point sources of light, the blurry halos around them appear to have quite a complex structure, and a fairly well-defined edge. Why is this?
To make it clearer what I mean, and at the same time to make a hypothesis about the answer, here is a public domain image of some lights, taken with a camera out of focus:

(image source.) Notice that the image cast by each light is not simply a uniform circle. They are each slightly hexagonal; they are slightly brighter around the edge than in the middle; and there are distinct nonuniformities in the brightness in the interior of each shape.
What I see is actually fairly similar to this but much more extreme. The shape formed by a light is much further from a circle, being very irregular in shape and distinctly elongated in one direction. (This latter is perhaps not surprising given that I have a strong astigmatism.) The bright edge to the shape, and the non-uniformities in its brightness, are much more pronounced than in the camera image.
Now, in the photograph I know that the hexagonal shape is due to the hexagonal aperture in the camera. This can be ruled out in my eyes because my pupils are definitely circular. (Unless some light also passes through parts of the iris?) I would guess that the bright edges are caused by diffraction, and that the nonuniformities are caused by defects in the lens and/or dirt on its surface. I suppose that diffraction and lens aberrations must be the causes of what I see when looking at distant lights without my glasses.
So in the end, my question is mostly just, am I right about this? Are the shapes I see the result of defects in my eye's optics, and which specific anatomical features of the eye are likely to be their main causes?

This isn't an answer, just speculation. In dim light, your rods are primarily responsible for perception (cones don't work very well). Cones are also more concentrated in the center of the retina, which would mean that rods are more concentrated at the edges. Something to do with focusing in nearsighted people might explain this.

dd3's suggestion makes sense to me but I wonder if you could add a short paragraph to explain the 'structure' a little more?

@daniel I'm not sure if I can easily describe it in words. If I get a chance some time I will make a sketch.

@Nathaniel Could just be refraction playing tricks on your eyes.

@Nathaniel or could be floaters. http://en.wikipedia.org/wiki/Floater

@TheLastWord it's definitely something to do with refraction, and not floaters - the effect is the same for every distant light source, whereas floaters would be intermittent. The question is why my eye's optics (plus processing by my visual system) produce a different effect from that of an unfocused camera lens. I'll try to get around to sketching it one of these days.

@WYSIWYG merry Christmas.

@Nathaniel merry christmas to you too. Don't mind that I voted to close. It is difficult to really know what you perceive and therefore answering this question is difficult.

@WYSIWYG I think approximately 50% of the population will not have any difficulty knowing what I perceive. However, I will edit the post to make it clearer.

@Nathaniel As you said you can make a sort of diagram to explain the perception. BTW what did your ophthalmologist say regarding this?

@WYSIWYG see the edit. In making it clearer I may have answered most of it myself, but I hope you can understand what I mean now. I've never spoken about it to an opthalmologist, because it's a subtlety of perception that only occurs at night, so I've never thought to mention it. (I suspect that it's what everyone shortsighted sees.)

@WYSIWYG I think you're being a bit hasty here.  There is an extensive body of literature on the physics (and psychophysics) of vision that can be brought to bear here.

@jonsca After the edit it is fine.. Before that it was a little difficult to understand what perception OP was referring to. I retracted the close vote

This phenomenon is known as bokeh.

You are right in the hexagonal shape being produced by the iris (of the camera), and from this, it is clear why this happens: The iris blocks out some of the light travelling into it, and a shape approximating that of the camera's iris aperture is projected onto the CCD.
The same thing happens for the eye. The iris will block out a portion of the light, causing the bokeh to approximate the shape of the iris aperture (aka the pupil), which is roughly circular for humans. Animals with non-circular pupils will perceive differently shaped bokeh. If the lens suffers from astigmatism, it will also result in non-spherical bokeh being projected onto the retina.
This stackexchange link goes into greater detail on Gaussian blur vs bokeh.

It seems like you're saying that the shape of the human pupil is hexagonal? If so, I would like to see a reference for this.

@fileunderwater No, I am not. The hexagonal shape of the bokeh in the OP was caused by the hexagonal shape of the camera iris. I will reword the answer to prevent this misunderstanding.