@dimension9: The "Rules" for designing RNA are not known, so stop preaching.


I am concerned, upset, and I feel like there is no point in even attempting to collaborate. You are allowing someone to dictate “this won’t work” in the tutorial/help section of this site.

It was my understanding, and perhaps I am wrong, that the purpose of this project was to promote a collective problem solving environment of experimentation, couple with analysis and lessons learned.

The link above clearly indicates that we should not do x, y, or z. The person who wrote it speaks with authority, and frankly it seems that they can solve this without me…
He/she even speaks to “this won’t fold in the lab”. Frankly, as a programmer with 20+ years experience, I would have to ask if you already know what won’t work, why do you allow it in the “game”.

That was a rhetorical question, I know the answer. But you seriously should consider moderating comments. Especially those listed under “guides”.


It is really kind of a rude shock to discover, that what I had thought was helpful, enthusiastic participation in a new fun community, has had such a completely opposite and negative effect on someone.

I guess I will have to re-evaluate my degree and frequency of community participation in light of this complaint. In fact, I think I may also have to re-evaluate my whole style of communication, and my ideas, and even my self-image as well.

Fomeister, I offer you a sincere apology for having offended you so, and for having so spoiled your early perceptions of this absolutely wonderful game and community.

Please do not allow my post (which I intended only to be helpful, and truly believed it was) to put you off of this incredible game of EteRNA.

Best Regards,


People are free to take the advice or not. If you choose to submit unoriginal patterns of 3-stacked “x-mas” C-G overloads, do it at your own discretion, but don’t wonder why it fails. D9 is simply trying to preach what he’s learned so far, in his 2 months of playing the game.

If you’re the type to want to learn a game by yourself, as I am, you can just ignore the guides and figure it out by yourself. But when designs give us the reason to think that they have a high probability of failing or struggling to gain their proper shape, we should mention it, and that’s what dimension9 has done. Just because we know what seems to fails, it still doesn’t mean we know what will succeed. D9 is only trying to advise about the types of design that seem to have a high-rate of failure.

My only bone to pick is that certain people voting for lab submissions really don’t seem sure how to evaluate an RNA sequence and they end up going for the most conspicuous data, the kCal provided at the top. I, myself, know not a great amount on RNA, but I know that if the lowest kCal would be the best, computers could figure it out within in less than a day. We have to use the values provided to our advantage to understand the limits of the RNA and it’s interaction with itself.

Do you not think that it’s odd that out of the top 8 voted design, 2 of the submissions contain the said flawed design. Together, they both raked in 40 votes. Yes! 21 for one, and 19 for the other. That’s at least 21 people that are going against what the last rounds result has shown us–especially the one that completely failed on the gel pattern.

If you bring up an interesting point, there’s no fear in collaborating. If you have a reason to think a certain design may be stable, state your reason and it will be taken into consideration. There ought to be no intellectual censorship on here.

Hope you understand,


I think this issue betrays a lack of background knowledge that needs to be presented in the appropriate places in the instructional and training materials for this game, especially for those submitting lab designs.

  1. The rules for what percentage of GC will and won’t synthesize are well known by the companies that do DNA/RNA synthesis for a living that synthesize millions of sequences a day (ask IDT). The percentages of GC basepairs in living organisms (and hence foldable biological sequences) is also well known. So that is a known problem that does not need to be re-visited with whatever limited resources this project has available, to do so would be a waste.

  2. This is not fold-it, where the stated goal was to find the best energy period. Energy is only part of the equation here, and the true goal for design is to maximize the difference between the folded state free energy and that of misfolded/unfolded states. The relationship between free energy, stability, and specificity for a target state over misfolded states need to be clearly explained at some point for lab members who are submitting designs, especially the concept of entropy. Ideally, the game would expand at some point to actually show some measure of entropy in designs, possibly by showing suboptimal folds (which are already computed by the Vienna package I suspect is being used as a backend).

Even simpler is the concept that if the melting temperature is ~100C, that means it is highly likely the sub-optimal folds will become “unbreakable” even if you boil the sample, and therefore you will not get any target no matter how awesome the free energy is.

  1. The relationship between the “rules” of the game world and of the real world need to be clarified. The game uses a well studied energy function curated from thousands of systematic, real-world lab experiments on small RNAs, and is highly accurate for predicting base-pairing. The goal of the game is not to improve this energy function per se, but to use it to train players with what is already well understood before moving on to what it is blind to. It cannot predict complex shapes beyond simple helices and loops, it cannot predict 3d structure, and it cannot deal with functional constraints the RNA would experience “in real life”, such as being able to catalyze reactions, bind to signalling molecules, etc etc. This is why design goals orthogonal to the energy (# of GU pairs, etc) are included by the developers. Players should NOT be surprised to find out that strategies that work great at solving the artificial challenges may have little bearing on actual lab results.