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%% http://papersapp.com/papers/

@article{Graveley:2013cs,
author = {Graveley, Brenton R},
title = {{Tips for Preparing mRNA-Seq Libraries from Poly(A)+ mRNA for Illumina Transcriptome High-Throughput Sequencing.}},
journal = {Cold Spring Harbor protocols},
year = {2013},
volume = {2013},
number = {5},
annote = {It's not obvious what kits they're using, and most of the advice seems to be about gel extraction steps, which is totally irrelevant for the Ampure pureifications. The one useful statistic is that with 76bp reads, 10-13% of reads span a splice junction, but it's not clear where that number comes from (or what species it's for)}
}

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}

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journal = {Journal of visualized experiments : JoVE},
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@article{Picelli:2014kg,
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annote = {Seems promising, but as far as I can tell, they never do the controls to determine whether there's a linear response to concentration.  The reproducibility is nice, but not enough to really go on.

Now that I've done it, I'm convinced that it works as well as other low volume protocols.}
}

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annote = {Nice review article. The field hasn't been around that long, so there's not that much new. Still good background and a nice coverage of possible functional aspects of eRNA.}
}

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annote = {Doesn't seem like, after all these experiments, they have any really exciting results.  Which, I suppose, makes me feel better, since what it means is that there really ins't a whole lot of low hanging fruit.  I will just have to think harder about a simpler problem (development?) and/or with better data. 


}
}

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@article{Zeigler:2014fm,
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title = {{Discrimination between thermodynamic models of cis-regulation using transcription factor occupancy data.}},
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annote = {One thing that the abstract and introduction don't make clear is that this is yeast data, and so cis-regulation means promoters.  Basically, they fit expression data based on{\ldots} expression data? I still don't get that part. They are using synthetic promoter libraries (not looking at genes). ``The model uses the sequence composition of synthetic promoters to predict their expression levels.''  Okay, I think what's going on here is :
	*	Use sequence + expression of fraction ƒ of promoters to fit parameters. Use those parameters to predict expression in the other 1-ƒ promoters, and compare fit.  
	*	Use sequence + binding of ƒ promoters to fit parameters. Predict binding in the other 1-ƒ
	*	Use sequence + binding + expression of ƒ promoters to fit params. Predict binding + expression in 1-ƒ.
In other words, we are trying to fit a function f(sequence), not f(expression). 

It's not clear that they have any deep biological insights to show from it, but they do turn up a potential competitive binding situation for one of the factors that they do ChIP on.  I think their figure 2 is actually somewhat disappointing: binding doesn't improve expression estimates compared to expression only, and expression doesn't improve binding estimates compared to binding only. It can let you distinguish between competitive and non-competitive models in the case of the ``switching behavior'' of Gcn4. 
}
}

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annote = {So it seems to me like these guys are answering kind of a different question than the one I'm really looking for (though not unrelated).  They want to figure out how to identify enhancers more or less de-novo, whereas I'm more interested in saying "Given that we know where the enhancers are, how do you predict what they're going to do". }
}

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annote = {Not many differences between wild and domesticated animals, and where there are differences, they aren't common to different domestication events. 

Perhaps not surprising.  All of these were from adults, not embryos or even juveniles. I would expect the largest differences to show up in brain development during embryogenesis, or at the very latest during the equivalent of adolescence.  Outside of that period, it seems more likely that all the expression is "Maintain the structure you have", whereas it's the actual structural/connectivity changes that are likely to lead to behavioral differences}
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