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Dear all,

Thanks for the helpful comments!

The crystal was not too sensitive to radiation, the scattered intensity
decreased slightly over time. I do not think that the truncated dataset is
better I just wanted to show that the data is noisy even at lover
multiplicity therefore it is not a direct consequence of the high
redundancy. The truncation reduced the Rmeas but actually it decreased the
CC1/2 too. So in one hand the data is more conventional for publication
(but still really weak) but in other hand removing most of the useful data
is not a good idea.

I have got many opinion and basically everyone said that I can use this
data for refinements and publication despite the obvious weakness.

Best,
Gergo

2016-07-21 10:19 GMT+02:00 <[log in to unmask]>:

> Dear Gergo
>
>
>
> If you have high multiplicity I would recommend you ignore Rmerge and
> Rmeas and instead focus on Rpim which tells you the precision of the
> average data
>
>
>
> http://strucbio.biologie.uni-konstanz.de/ccp4wiki/index.php/R-factors
>
>
>
> If this **increases** as you add more data then adding the data is making
> the average worse, if this decreases then you are improving the
> measurements. Simply removing perfectly good data to make reviewers happy
> seems like a bad idea to me.
>
>
>
> You mention below that the refinement gives you a good structure – this is
> a much better indication of the quality of the data than the Rmerge!
>
>
>
> There is a very good argument (certainly for pixel array detectors) for
> recording massive multiplicity low transmission data, since you can
> consider radiation damage a postori and work out where you should have
> stopped collecting after the experiment, but still have a complete data set
> (in essence, you are uniformly spreading the “useful photons” around
> reciprocal space) – this may require careful treatment in the processing
> however.
>
>
>
> Your CC-1/2 statistics indicate that the data in the outer shell agree
> well, so I would argue strongly for keeping the entire data set, and in my
> opinion you will be doing the community a favour arguing your case with the
> reviewers if they complain about the Rmerge being “too high”
>
>
>
> Best wishes Graeme
>
>
>
>
>
> *From:* CCP4 bulletin board [mailto:[log in to unmask]] *On Behalf Of *Gergo
> Gógl
> *Sent:* 20 July 2016 16:07
> *To:* ccp4bb
> *Subject:* [ccp4bb] weak low resolution data with high R and good CC1/2
>
>
>
> Dear all,
>
> I am trying to process a weak low resolution data which was crystallized
> and collected in an other lab but unfortunately with suboptimal crystal
> handling (cryo...) and data collection strategy (1° oscillation, close
> detector distance...). The data is highly redundant but the Rmeas is really
> bad. We already suggested them to collect better data from a better crystal
> but it seems to be difficult for them...
>
> The overall data has a redundancy of 40 (43 in the highest bin) with an
> overall Rmerge 75% (365% in the highest bin) while the overall CC1/2 is 99%
> (83% in the highest bin). (The XSCALE.LP is attached for the whole
> dataset.) I was able to decrease the overall Rmerge to 36% by discarding
> ~80% of the collected frames but it is still a marginal data (with a
> redundancy ~9). On the other hand the refinement gave us a reasonable
> structure with good Rfactors (Rwork 22% Rfree 26%). (It is a
> protein-peptide complex where we are interested in the bound state of the
> peptide.)
>
> We are in a disagreement in our lab and already asked a few
> crystallographer but did no reached a clear consensus answer. Is this data
> acceptable to publication? Can you trust a data like this?
>
> Best,
>
> Gergo Gogl
>
>
>
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