I think this thread could be a great resource for players starting out in lab also. One of the most common questions I see about lab is “how am I supposed to know what to vote on?”
The first thing I look at is bond content. I pretty much only look at designs in which between 30% and 80% of the bonds are GC, and no more than 10% to 20% are GU. Those are rough guidelines – in a shape like the Cross or One Bulge Cross where most of the nucleotides are bonded (very little unbonded content) I want the GC content to be lower than in a shape like one of the Stars where there’s a lot of unbonded content and a more complex design.
Next thing I look at is tetraloops. If the tetraloops aren’t either AAAA or one of the known patterns that gets an energetic bonus in EteRNA I check to see if there are mispairing possibilities with a complementary sequence nearby (if for instance the four nucleotides in the tetraloop are AGUA and part of a nearby stack is UAC).
Then comes any multiloop. In the case of One Bulge Cross (the first lab I was around for) this would mean the center intersection. Fairly early on in the rounds it became clear that there were two central patterns that seemed to hold up better than others, so I looked for those. In the case of the more recent labs, it has meant making sure that any non-Adenine nucleotides in the unbonded parts of the multiloop don’t look like they’re going to interfere with the formation of that loop.
Next is the dotplot. I usually look at this as it is, then make any tetraloops in the design AAAA and take another look, then make them all GAAA and take a third look. This is harder to set rules for. I’m not necessarily looking for an absolutely perfect dotplot, but some things I like to see are an absence of what I think of as “shadow lines” running parallel to the lines we want to see (which suggest that two sections of the sequence may mispair rather than just a single bond), and not too much variance between the dotplot for the design as is, the design with AAAA loops, and the design with GAAA loops.
If the designer has given RNAfold stats I’ll look at them, otherwise I rarely run sequences through RNAfold or other servers anymore. If I do, I’m looking for an MFE% of over 80% for designs with AAAA tetraloops or over 90% for designs with “stabilized” tetraloops, ensemble diversity under about 0.5, and entropy range under about 0.3.
Finally I look at colorpatterns as Eli Fisker has described, and quad energies. I don’t like to see any quad energies over -0.9 kcal (a UA UA or AU AU quad). If there are GU bonds used, I like to see them stabilized on one side with a GC in a configuration that give -2.1 or -2.5 kcal. I also don’t like more than 3 AU pairs in a row, even if alternating orientation (though if we ever have another shape with significantly longer stems this may change). I also look for GCs at the beginning and end of all stems, though I won’t rule out a design that has one or two stems closed with AU especially in early rounds when I think we’re still testing the tolerance of the shape.
In deciding whether to vote for a design or not, I base the decision on both the above considerations and what I know about other designs that have received votes. If for instance most of the designs being voted up are at the high end of what I consider “desirable” GC content, I’m more likely to vote for a lower-GC design (again, especially in rounds one and two of a new shape). In later rounds, if we already have four or five modifications of a successful shape voted up to the top, I’m more likely to vote for a new modification of a different design, or an entirely new design.
edit to add: in all the above I left out two important things that are harder to quantify. First: the “neck” of the design (the stack nearest the open loop). We’re still having a lot of trouble forming this in all shapes, so I go by my most recent gut feeling about it. Second, the comment section. Especially for designs that are modifications of previous designs this is important – I like to see what the designer is trying to correct and how.