fibonacci

I'm trying to figure out how to sequence a gene I'm interested in from mRNA. Typically the way the lab does qrtPCR is they first make cDNA. using Applied Biosystems high capacity RNA to cDNA. Does this create full length cDNA of every RNA? For example, one gene I want to study is 5000 bp long in its mRNA. Does the reverse transcriptase make a full length cDNA? Next, what do I do with the cDNA? If I design primers using standard protocols that use online software, it gives me a pair of primers that are somewhere in the middle of the cDNA. Once I do PCR, will it amplify the whole cDNA or just the portion between the two primers? The problem is that I want the whole cDNA sequence to be amplified and sequenced. The entire sequence is already known for the mRNA, if I just designed primers that work all the way at the end of the 5' and 3' ends, would I get the entire sequence amplified using regular PCR? Would it have trouble amplifying a mRNA with 5000 bps? Would RACE PCR be appropriate here to create the entire cDNA amplification or is it not needed since the entire gene sequence is known for cDNA so I can get away with just regular PCR? Thanks in advance for any help. The reason I want to sequence the whole cDNA from mRNA even though the sequence for cDNA is already known is to look for mutations.

Good points. I agree, magnitude of effect and biological importance should be the most important concept to analyze for a biologist, but too often journals won't accept a manuscript if they don't see "statistical significance" , which in the end is meaningless without effect size. If one knows what statistical test they want to run on the data that they are going to obtain, I was under the impression POA should be used to determine how many times to run the experiment. Why is it then so many journals accept n=3 without justification for 3? I don't understand the rule of thumb I guess if there's simply so much other literature out there describing the importance of enough statistical power in fields like psychology, clinical medicine, drug trials, etc.

I don't think each cell counts as an individual "n". See: http://labstats.net/articles/cell_culture_n.html N should be the number of times your run your experiment independently. For example, let's day I do the tox test above on the cells day 1 with technical triplicates, repeat again on day 2 w/ technical replicates, and repeat again on day 3 with technical replicates. My n is still only 3 even if each sample I tested on those days contained millions of cells.. I've never seen a literature example where they run an ANOVA on a n=millions of cells. Cell culture cells all come from the same cell line and are all tested at the same time. Mice, however, are separate biological entities that are not the same, and can be tested independently. Testing all cells at the same time means they aren't independent because the same test was done on them all at the same time. That's on top of the fact they're all from the same culture. Again, in order to properly rule out error, you need to know how many times to run a test, which is "n". That's what power of analysis is for. So if that's what POA is for, then why is triplicate automatically assumed to be acceptable?

Ok, for a very easy example: Let's say I want to test drug X on cancer cells to test for toxicity. I test 0, 50, and 100 uM of the drug on the cells and then count them to determine toxicity. The typical way to complete this experiment would be to repeat this experiment 2 more times and then run ANOVA or some other statistical test to determine statistical significance between concentration and cell count. This would be acceptable for many journals. What I'm hung up on, is why is n=3 by far and away accepted as a default number of times to run an experiment like this? Shouldn't one do a power of analysis to determine how much of a sample size you'd need to perform the experiment and have enough data points to run a proper ANOVA? However, if I were to test drug X in mice at different concentrations, then it would probably be absolutely required by an institutional board to conduct a power of analysis to determine how many mice I'd need so that I'm not unnecessarily killing too many mice or to determine if I'm not using enough mice which would result in my data being worthless. My question I suppose is, what makes cells different than the mice? Also, one other thing to mention--is I don't think 'data is just data' and that it should be up to the reader to determine if it is useful or not. The problem also with underpowered studies is that it can propagate type II errors, once a study is published with a type II error and it is repeated in literature, it can gain a foothold and be established as a scientific 'fact' when in reality, the results from all of the underpowered studies that replicated the original results are wrong because they mistakenly made a type II error due to lack of power.

Right, experiments are done in triplicate so that statistical tests that generate P values can then be used to show "statistical significance". However, if your study is underpowered statistically, you run into all sorts of problems with type II errors and over estimating the mean effect size from an experiment. I'm under the impression that in cell culture work, each cell doesn't represent an independent measurement. Anytime you obtain a sample and assay your cells, the sample as a whole only counts as one measurement (technical replicates do not count of course). Am I confusing something here? I don't understand why many branches of science require a priori power of analysis while other fields can get away with simply doing triplicates.

I don't understand why many biological experiments in literature are automatically run in triplicate. What's so special about the number 3? Why is it automatically assumed that a study run in triplicate has enough statistical power to make the observations from that study meaningful? Don't you have to run power of analysis first before determining proper sample size? Why is "triplicate" a mindless automatic default for number of times to do an experiment? I even see papers that get published in Nature and Science with experiments "done in triplicate". I don't understand why so much of the literature out there talks about the need to do power of analysis for proper sample size (in fields like clinical medicine etc.), yet many other branches of science can simply get away with the de facto default of "triplicate" and conclude that results from such an experiment are meaningful. Why doesn't all science require power of analysis to design an experiment?

Let's say you messed up big time and took a course that is impossibly difficult to pass with the minimum grade. Is it bad to withdraw from a class in grad school? Let's say I don't even give a crap about teaching in academia after getting a PhD and that I can take a summer course to supplement the credits needed. What would you do if you knew you couldn't pass an insanely hard class that's not required but one of your electives?

I've lost 22lbs while in grad school thus far, not because of some strict regimen of working out and dieting, but because of the fact that I simply don't have time to eat. Many days I only eat once per day. I wasn't that big to begin with, just slightly overweight (161 lbs for a male) and am now down to 139lbs. I haven't been this light since my freshman year in high school. At this rate, ill be sporting a 6 pack soon all without even trying, and ive never had a 6 pack in my life. Anyone else losing a ton if weight because they slend more time doing work than having time to eat?

Know how you feel. I'm older too, and went back to school after a half decade in industry. Your academic skills definitely deteriorate while working BUT you have working experience which most lunatics in academia have never had in their life. No one really cares about your grades in grad school, just pass the courses you need to and do interesting research. If you're getting A+s in your classes, that means you are spending too much time doing class work and not enough on research. Sometimes I think the undergraduates in some of the classes I take are way smarter than me, but just I realize that they've never had a job in their life and have a hard time sifting out the useless academia trivia from what's really important to know. Grad school isn't about good grades like high school or undergrad college, it's all about the research.

The postdoc we have in lab is starting to drive me crazy. She now thinks someone is sabotaging her experiments when she isn't around, and has now started whining to the PI about it. I know she's probably narrowed it down in her head to either me, or another guy in the lab. It just irks the sh!t out of me, that I'm probably being falsely accused of sabotaging her experiments behind my back. Now that she thinks sabotaging is going on, she's moving stuff around the lab and taking over cabinets and putting locks on them so someone can't "sabotage" her experiments. NO ONE IS SABOTAGING HER EXPERIMENTS. I don't understand where this bat sh!t craziness comes from. She's manufacturing completely unnecessary drama that's utterly absurd. I have never been around someone so toxic before at work. Not even in industry have I ever encountered someone so difficult to work with, so paranoid, and that's so against teamwork in order to take all credit for herself. Anyone else have a toxic lab coworker? What did you do?

It's time universities should start creating their own open access journals and peer review them among themselves. The publishing cartels charge outrageous fees that do nothing more than increase tuition costs for everyone. The vast majority of the work that goes into academic journals mostly come from tax payer http://www.guardian.co.uk/commentisfree/2011/aug/29/academic-publishers-murdoch-socialist

Look at that, Bank of America just dumped $75 trillion in derivatives liabilities onto the US tax payers: http://seekingalpha.com/article/301260-bank-of-america-dumps-75-trillion-in-derivatives-on-u-s-taxpayers-with-federal-approval This country is so screwed beyond belief because we let a few wall street banksters and politicians in Washington get away with rigging the system in order to makes millions, if not billions, on it for their own personal selves while it costs us a country as a whole trillions in losses. LIke the author said, these people privatize profits, but socialize all of the losses. These criminals should be tried as a racketeering ring under RICO laws. BoA dumping this kind of liability on the tax payers after a bailout that was only 4 years ago should be even more reason to protest for reinstatement of Glass Steagall.

And it has only been 7 weeks in. I'm struggling with course work right now because I really don't see the point of all this BS. Course work is such a stupid formality, I just hate how I can't get to work in the lab right away because we all know that's what I'm there for anyway. Just learn the science you need as you go along with research, why all the useless coursework that you'll probably never or hardly use again? Also, sometimes I bother wondering why I went to grad school anyway. The economy sucks, and it will for a LONG time. We're training way too many PhDs for number of positions available in academia (which I plan on staying far away from). All manufacturing jobs and R and D are being shipped over seas. I'll be educated all right when I leave, but I'll still be poor. I don't see the point of blasting my brains out when it is increasingly likely that none of this BS will pay off. The whole system is f$@%ed beyond belief.

if you haven't already: http://www.miller-mccune.com/science/the-real-science-gap-16191/ That article also recently won the American Association of University Professor's award for excellence in coverage in education.

It's because the majority on here don't want to believe that the mantra they've been fed their whole life--that more education is always better no matter what the cost--is really not that true at all. Next year when the department of education starts tracking default rates on student loans out to 5 years the default rate on student loans is projected to almost double from 7% up to 14%. Only 40% of student loans are currently in repayment, the rest are either in deferment or are in default. The US has a massive ticking time bomb with regards to education and debt. The numbers don't lie, education costs keep soaring while jobs that pay livable wages continue to be off shored or are disappearing all together as the manufacturing base in this country declines. I bet the vast majority of kids on these boards have never even had a job or even looked for one. They have no idea what's waiting out here for them once they get out. Right now we live in a time when we are sending the most kids ever in the history of this country on towards higher education while the US economy is suffering from severe systemic and structural problems that will take decades to fix (a lot of problems may not be able to fixed at all). Suggesting that people should go to college or grad school no matter at what cost, because more education is always better, is completely stupid, especially when the economy is increasingly unable to absorb the huge swaths of new grads and pay them livable wages. College these days for many kids leads to nothing more than underemployment or temp jobs with no benefits with tons of student loan debt. Grad school just prolongs the underemployment while the interest builds on the principal and then you're caught in 20+ years worth of student loans. The whole f^cking system is one giant scam.