A bit late to this thread.
1.
Juergen: Jim was not actually adopting CC*, he was asking how to make
practical use of it when faced with actual datasets fading into noise.
If I understand correctly from later responses, paired refinement is
what K&D suggest should be best practice?
2.
I'm struck by how small the improvements in R/Rfree are in Diederichs &
Karplus (ActaD 2013,http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/
<http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/>); the authors
don't discuss it, but what's current thinking on how to estimate the
expected variation in R/Rfree - does the Tickle formalism (1998) still
apply for ML with very weak data?
I'm puzzled by Table 4 (and discussion): do I read correctly that
discarding negative unique reflections led to higher CCwork/CCfree?
Wasn't the point of the paper that massaging data always shows up in
worse refinement stats? Is this a corner case, and how would one know?
Cheers
phx
On 28/08/2013 01:48, Bosch, Juergen wrote:
Hi Jim,
all data is good data - the more data you have the better (that's what
they say anyhow)
Not everybody is adopting to the Karplus Diederich paper as quickly as
you do. And not to be confused with the Diederichs and Karplus paper :-)
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3689524/
http://www.ncbi.nlm.nih.gov/pubmed/22628654
My models get better by including the data I had been omitting before,
that's all that counts for me.
Jürgen
P.S. reminds me somehow of those guys collecting more and more data -
PRISM greetings
On Aug 27, 2013, at 8:29 PM, Jim Pflugrath 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]
<mailto:[email protected]>] on behalf of Arka Chakraborty
[[email protected] <mailto:[email protected]>]
*Sent:* Tuesday, August 27, 2013 7:45 AM
*To:* [email protected] <mailto:[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]
<mailto:[email protected]>> wrote:
The question you should ask yourself is "why would omitting data
improve my model?"
Phil
......................
Jürgen Bosch
Johns Hopkins University
Bloomberg School of Public Health
Department of Biochemistry & Molecular Biology
Johns Hopkins Malaria Research Institute
615 North Wolfe Street, W8708
Baltimore, MD 21205
Office: +1-410-614-4742
Lab: +1-410-614-4894
Fax: +1-410-955-2926
http://lupo.jhsph.edu
