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Developments in xia2

VIEWS: 3 PAGES: 18

									Developments in
xia2
Graeme Winter
CCP4 Dev Meeting 2008
What is xia2?
 Automated robust data reduction and
  analysis
 Thorough – takes additional steps when
  many users wouldn’t bother
 In: images from e.g. synchrotron beamline
 Out: measurements for downstream
  phasing via e.g. HAPPy, Mr BUMP,
  Phenix…
Recent changes
 Inclusion in CCP4 6.1
 Many command line options
 Integrated with AutoRickshaw (EMBL H)
 Robust lattice determination
 Support for Q270, Pilatus
 Zero input option
3 Month plans
 BioXHit ends in June => so does xia2
  development
 Include robust system to decide resolution
  limits etc (next slides)
 Finish release 0.3.0 to go with release
  version of CCP4 6.1
Chef

Let’s cook them books!
What is chef?
 A tool to help you use the best of the
  reflections you have
 Uses unmerged intensities
 Uses robust statistics to decide:
     d*minfor different functions (resolution)
     Dmax for different functions (dose)

   Additional program “doser” to add dose
    information to unmerged MTZ files
In
 MTZ files from scala with “output
  unmerged” set
 DOSE / TIME information for doser:
     BATCH 1 DOSE 2.5 TIME 2.5
     BATCH 2 DOSE 7.5 TIME 8.2
    …
Running
doser hklin TS03_12287_chef_INFL.mtz hklout infl.mtz < doser.in
doser hklin TS03_12287_chef_LREM.mtz hklout lrem.mtz < doser.in
doser hklin TS03_12287_chef_PEAK.mtz hklout peak.mtz < doser.in

chef hklin1 infl.mtz hklin2 lrem.mtz hklin3 peak.mtz << eof
isigma 2.0
resolution 1.65
range width 30 max 1500
print comp rd rdcu
anomalous on
labin BASE=DOSE
eof
Output
 Resolution vs. dose
 Completeness vs.
  dose for each data set
Methods
   Based on “new” cumulative-pairwise R
    factor RCP:




   Inspired by Rd in Diederichs (2006)
And RCP means..?
 How well do the measurements up to dose
  D agree?
 Closely related to I/σ
 Reasonably robust as it does not depend
  on sigma estimates or means
 Gets bigger when systematic variation
  contributes to spread
Requirements
   Radiation damaged MAD data – what do I
    want for:
     Substructure  determination – big anomalous /
      dispersive signal
     Phase calculation – well measured ΔF
     Phase extension & improvement – good F
     Refinement – good F

   85% Limit RCP < R(I/σ) + S(I/σ, Nm, Nu)
Example
 JCSG TB0541 – heavily radiation
  damaged…
 3 wavelength MAD – INFL + LREM, PEAK
 Massive signal
 P43212, 90 degrees * 3 => plenty of data
 Chef says “use data to 1.65A, D=~600s”
Before (INFL)
For TS03/12287/INFL
High resolution limit    1.66     7.41    1.66
Low resolution limit     52.7     52.7    1.7
Completeness             95.8     98.4    72.5
Multiplicity             6.4      5.1     4.2
I/sigma                  13.1     25.6    2.2
Rmerge                   0.085    0.045   0.654
Rmeas(I)                 0.117    0.077   0.808
Rmeas(I+/-)              0.099    0.054   0.816
Rpim(I)                  0.045    0.032   0.374
Rpim(I+/-)               0.051    0.029   0.478
Wilson B factor          19.372
Anomalous completeness   95.5     100.0   72.3
Anomalous multiplicity   3.4      3.5     2.1
Anomalous correlation    0.546    0.695   0.032
After (INFL – first 60 degrees)
For TEST001/12287/LREM
High resolution limit    1.63     7.3     1.63
Low resolution limit     52.56    52.56   1.68
Completeness             92.6     98.3    62.9
Multiplicity             4.1      3.3     2.4
I/sigma                  13.6     26.2    2.1
Rmerge                   0.052    0.033   0.317
Rmeas(I)                 0.065    0.041   0.504
Rmeas(I+/-)              0.066    0.043   0.445
Rpim(I)                  0.031    0.021   0.306
Rpim(I+/-)               0.041    0.027   0.311
Wilson B factor          18.731
Anomalous completeness   91.8     99.4    59.4
Anomalous multiplicity   2.2      2.2     1.3
Anomalous correlation    -0.227   0.071   0.01
Why improvement?
 Limit radiation damage => σF more
  meaningful
 Limit damage => ΔF better
 Without systematic damage get higher
  resolution for given I/σ
However…
 Pipe MTZ through scaleit / solve / cad /
  resolve / Arp/Warp and get very similar
  results – slight improvement though
 This is most interesting, because it means
  that 55% of the “data” did not add to the
  quality of the result
Plans
 Currently writing this up for J. Appl. Cryst
 Chef will be included in CCP4 6.1
 Next: include this as part of xia2 (makes
  0.3.0)
 Extend chef to make decisions about
  anomalous / dispersive differences

								
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