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Make list of figures/experiments to make by Mind Map: Make list of figures/experiments to make
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Make list of figures/experiments to make

Notebook Entry with links

Richard's Suggestions

First, congratulations on your new paper draft! My understanding of your paper is that you have shown that you can identify a naked dsDNA sequence by comparing its unzipping force-index curve against a library of simulated curves. Very cool, and I want to thank your team for writing the paper in a way that I can understand. Here are my comments: 1. I think the major contribution of the paper is that you have an accurate simulation and matching method, which can easily account for elasticity of the tethering construct, viscosity, etc. I'd like to see more details (or at least know that you have the answers in your back pocket) on the information-recall side of the problem. See terms at the bottom of < >. I'm no expert here, but here are my thoughts (and most of the answers don't necessarily belong in the paper): You matched experimental F-j curve vs simulated F-j curve. Why not interpret your experimental F-j curve into a sequence (with wild-card characters) and then do the matching in ASCII space? Is there a rough map between your match score and a BLAST similarity score? (But, since I'm no BLAST expert, I would need to do lots more research before talking about BLAST in the paper.) In the "Future Improvements" section, you discuss the general sequencing-by-unzipping problem. Imagine simulating all possible sequences from j=1200 to j=1700. In this space of 4^500 sequences, what are the characteristics of those close to your experimental sequence, and what is the minimum similarity that can be resolved? From the other side, in your matching problem, how much easier has the problem been made by considering only the 2700 restriction fragments instead of the 4^500 general genome? What if you don't restrict yourself to those restriction fragments, but allow any contiguous 500-bp sequence from yeast --- then how many false positives do you get? (In Figure 4, what is the outline that you would get by simulating random fragments from the 4^500 or yeast-sequence universe?) A related question is characterizing the sequences in the overlap of the Gaussians. Suppose when you unzip chromatin, you get only a naked-DNA signal over the linker DNA regions, and an un-simulated signal over the non-linker regions. Would you speculate on how that (change in signal) will affect your recall? Suppose you simulate all 2700 restriction fragments. What if you make another version of figure 4 by scoring all simulated sequences versus each other (sim vs sim instead of expt vs sim)? (Also, by symmetry, what is the spectrum of scores obtained for individual OT runs --- both match and mismatch --- against each other (expt vs expt, instead of expt vs sim)?) Is the difference in distance between your blue and red peaks roughly equal to the difference in distance between correct and incorrect peaks for the 2700 x 2700 test? (If not, then you will have to explain what's special about your 32 sequences.) Do the 2700 fragments include the reverse-complement sequences? 2. In the excerpt of the match score formula, I see kT / C / stdev(force difference). Here are the implications that I read from the formula: a. for a given match score, a 1-unit force difference on 4 indices (neighboring or separate) is worth a 2-unit force difference on 1 index; and b. for a given match score, the standard deviation of the force difference scales with the temperature of the environment. On (a), I recognize that this is a draft and see that you may not want or need to refine the formula. On (b), are you making a statistical-mechanical statement (and I see no support for it elsewhere in the paper), or are you just dressing up the standard deviation of force in statistical-mechanical clothing, by adding a kT and making it unitless? For the purposes of your experiment, kT/C is constant, so that portion of the relationship cannot be tested. My thought is, if you're not making a statistical-mechanical statement, then your match score should be as simple as possible: m2 = stdev(force difference/scale force) where the scale force is kT/2/C. Do you actually get a better Gaussian when you take exp(-1/m2)? My own biases would reverse your scale, suggesting numbers close to 1 as a good match, and numbers close to zero as a poor match --- maybe something that can be interpreted as a probability, like exp(-m2) / sum_{all sequences} exp(-m2). 3. The beginning of the paper reads to me like "peeing on the tree". In particular, I think mentioning Pol II is overreaching. 4. It really bothers me in figure 2 that the colors are swapped between A and B. Also, next to figure 4, in the last paragraph before "Future Improvements", you use the word "fell" instead of "felt". Also, I have not worked on the language very much, but the last line on page 8 is a little strange. 5. You mention viscosity but don't explicitly say whether your modeled force includes viscosity. I am going to guess that it does, because otherwi

Sensitivity and Specificity

Richard suggests simulating unzipping for all fragments 1200 - 1700bp in length and testing for false positives, etc.

test simulated sequences against each other

match uncontinuous DNA unzipping to simulation library

add nearest neighbor energy values to unzipping simulation

Link to public site with comments from Richard and responses by Steve

Improvements/Ideas after reading the paper

use simple hamiltonian but allow for ssDNA extension instead of dsDNA

put ALL software on github and link to it



Other stuff to include