Aimless does indeed calculate the point at which CC1/2 falls below 0.5 but I would not necessarily suggest that as the "best" cutoff" point. Personally I would also look at I/sigI, anisotropy and completeness, but as I said at that point I don't think it makes a huge difference
Phil On 28 Aug 2013, at 10:00, Arka Chakraborty <[email protected]> wrote: > Hi all, > If I am not wrong, the Karplus & Diederich paper suggests that data is > generally meaningful upto CC1/2 value of 0.20 but they suggest a paired > refinement technique ( pretty easy to perform) to actually decide on the > resolution at which to cut the data. This will be the most prudent thing to > do I guess and not follow any arbitrary value, as each data-set is different. > But the fact remains that even where I/sigma(I) falls to 0.5 useful > information remains which will improve the quality of the maps, and when > discarded just leads us a bit further away from truth. However, as always, > Dr Diederich and Karplus will be the best persons to comment on that ( as > they have already done in the paper :) ) > > best, > > Arka Chakraborty > > p.s. Aimless seems to suggest a resolution limit bases on CC1/2=0.5 criterion > ( which I guess is done to be on the safe side- Dr. Phil Evans can explain if > there are other or an entirely different reason to it! ). But if we want to > squeeze the most from our data-set, I guess we need to push a bit further > sometimes :) > > > On Wed, Aug 28, 2013 at 9:21 AM, Bernhard Rupp <[email protected]> > wrote: > >Based on the simulations I've done the data should be "cut" at CC1/2 = 0. > >Seriously. Problem is figuring out where it hits zero. > > > > But the real objective is – where do data stop making an improvement to the > model. The categorical statement that all data is good > > is simply not true in practice. It is probably specific to each data set & > refinement, and as long as we do not always run paired refinement ala KD > > or similar in order to find out where that point is, the yearning for a > simple number will not stop (although I believe automation will make the KD > approach or similar eventually routine). > > > > >As for the "resolution of the structure" I'd say call that where |Fo-Fc| > >(error in the map) becomes comparable to Sigma(Fo). This is I/Sigma = 2.5 if > >Rcryst is 20%. That is: |Fo-Fc| / Fo = 0.2, which implies |Io-Ic|/Io = 0.4 > >or Io/|Io-Ic| = Io/sigma(Io) = 2.5. > > > > Makes sense to me... > > > > As long as it is understood that this ‘model resolution value’ derived via > your argument from I/sigI is not the same as a <I/sigI> data cutoff (and that > Rcryst and Rmerge have nothing in common)…. > > > > -James Holton > > MAD Scientist > > > > Best, BR > > > > > > > On Aug 27, 2013, at 5:29 PM, Jim Pflugrath <[email protected]> wrote: > > I have to ask flamingly: So what about CC1/2 and CC*? > > > > Did we not replace an arbitrary resolution cut-off based on a value of Rmerge > with an arbitrary resolution cut-off based on a value of Rmeas already? And > now we are going to replace that with an arbitrary resolution cut-off based > on a value of CC* or is it CC1/2? > > > > I am asked often: What value of CC1/2 should I cut my resolution at? What > should I tell my students? I've got a course coming up and I am sure they > will ask me again. > > > > Jim > > > > From: CCP4 bulletin board [[email protected]] on behalf of Arka > Chakraborty [[email protected]] > Sent: Tuesday, August 27, 2013 7:45 AM > To: [email protected] > Subject: Re: [ccp4bb] Resolution, R factors and data quality > > Hi all, > > does this not again bring up the still prevailing adherence to R factors and > not a shift to correlation coefficients ( CC1/2 and CC*) ? (as Dr. Phil > Evans has indicated).? > > The way we look at data quality ( by "we" I mean the end users ) needs to be > altered, I guess. > > best, > > > > Arka Chakraborty > > > > On Tue, Aug 27, 2013 at 9:50 AM, Phil Evans <[email protected]> wrote: > > The question you should ask yourself is "why would omitting data improve my > model?" > > Phil > > > > > -- > Arka Chakraborty > ibmb (Institut de Biologia Molecular de Barcelona) > BARCELONA, SPAIN
