Outline of Difference Map from Helical Tubes

I.  Average data set in Fourier space
	HAVGREFN: to refine fitting parameters of individual images
	HLXAVG: to create average data set (id.avg and ref.avg)
	LLPRX2:  to edit averaged data set for averaging/fitting
	HLXEDT: to apply editting to layer line data set
notes:  use idred.2fd to start that has been adjusted to correct 2-fold origin to ultimately be used for 
map averaging (of difference calculation).  Consider using different 2-fold origin which will produce 
phi0 = 0 for hpz sections.

II.   Corrections to averaged data set
   A.  CTF  (HDIVCTF)  -> id.cor and ref.cor
   B.  MTF   (MTFCORLL)  ->  idm.cor and refm.cor
   C.  Limit resolution for initial fits (HLMTRES)  idmRES.cor and refmRES.cor

III.  Determine radial scaling factor against reference data set  (2 ways possible)
   A. Using MRDD
	HLX2FLD: calculate two-fold enforced file -> (idm.2fd and refm.2fd)
	HLXFB: calculate little g file with 1A spacing -> (idm.lg1 and refm.lg1)
	LGLST: calculate MRDD for both idm.lg1 and refm.lg1 ->  lglst.dat and lglst.gnu
	GNUPLOT lglst.gnu: plot MRDD 
	LGCOR: edit lglst.dat to use as control file -> RSCAL, RSHIFT
notes:  adjust scale for lglst so that MRDD have roughly the same scale
use cutoff amplitude for LGCOR so that only features of MRDD are used for correlation, not just 
basic square-wave shape
   B.  Using COMPMOL
	make HPZ sections (see below) using either RSCAL=1 or LGCOR values for RSCAL
	use RHOFIT control file to run COMPMOL to get RSCAL value 
notes: LGCOR doesn’t seem to work very reliably if the MRDD has different shape between the two 
data sets (i.e., lipid bilayer peak spacing smaller and distance to cytoplasmic head peak bigger as 
was our case).  So we ultimately decided COMPMOL gave a more reliable result.

IV.  Calculate HPZ sections
	HLXAVG: (fitid.cnt) to apply RSCAL to data set -> (idmr.avg)
	HLX2FLD: apply two fold 
		use RPT/RSCAL for new repeat distance -> (idmr.2fd)
		use 2fld axis coinident with vanadate peak (at least consistent with ref)
	HLXFB: calculate little g file with 3A spacing -> (idmr.lg3 and refm.lg3)
	HPSEC: calcluate HPZ sections  -> (idrm.hpz and refm.hpz)
		odd number of pixels in all dimensions of map
		choose limits for map to include only one molecule (consistent with ref)
		use RPT/RSCAL for repeat distance

V.  Define mask for test data set
	Rough mask for molecule
		XDISPMSK: use ref.bmk as template -> (idmr.bmk)
		BOXMSK: apply mask to HPZ sections (idmr.hpz -> idmr.msk)
	Close molecular mask
		IMGCHR: convert to character format (idmr.hpz -> idmr.chr)
		VOLUME: calculate density cutoff for 100% volume recovery from idmr.chr
		IMGDENS: apply mask with this density cutoff (idmr.msk -> idmr.dns)

VI.  Scale two data sets
	IMGCORAB: calculate linear regression for densities (idmr.dns vs. refm.dns) or (idmr.msk vs. 
refm.msk) 
	GNUPLOT ab.gnu: check linear regression
	PRODADD: apply density scale to data set (idmr.dns -> idmrs.dns) or (idmr.msk -> idmrs.msk)
notes:  Best scale factor should consider only densities from the molecules themselves and not 
background densities and molecules should also be aligned since it is a regression.  However, 
IMGCORAB doesn’t always give a good correlation with such a tight mask.  Should examine gnuplot 
plot of least squares regression to see if fit is acceptable.  It is even possible to use unmasked 
sections.  For RHOFIT, scale not so terribly critical.  Also, addition of two extra lines of parameters in 
control file may help (M <cr>  100 100)  (default is (M <cr> 50 50).

VII.  Align two scaled data sets
	RHOFIT: crosscorrelation to determine relative orientation (idmrs.dns vs. refm.dns  OR  
idmrs.msk vs. refm.msk depending on how density scaling was done)
	COMPOL: similar to RHOFIT except RSCAL also considered, can be used to determine radial 
scale when LGCOR seems unreliable (i.e., big enough difference in shape of MRDD)
	SKEW:  apply fitting parameters determined by RHOFIT to scaled data set  (idmrs.dns -> 
idskew.dns OR idmrs.msk -> idskew.msk)

notes:  1. density scale probably not so important for this because correlation coeff is independent of 
scale.  However, if negative densities are present in one case and not another, could be trouble.  So 
best to be about the right density scale 2. check skewed map against reference map to make sure 
you are happy with relative orientations and radial scale and density scale factors.  Also can use 
HISTO-0 to compare density distributions and unbiased mean density (ignores zero density) for two 
maps. 3.  Must skew masked molecules or else must redefine mask after applying skew.

VIII  : Add or Subtract two data sets
 	Make mask corresponding to 100% volume recovery to ref and to test file if not already done
		IMGCHR: convert to character format    idskew.chr and refm.chr
		VOLUME: specify molecular weight to determine density cutoff level
		IMGDENS:  to apply density cutoff  idskew.msk -> id.dns
	AVGIMROT:  idskew.dns +/- refm.dns = sum.dns or diff.dns
notes:  Final scaling should be done after skew and take into account real differences in molecular 
weight.  Check scaling with HISTO-0 (density histograms should overlap).  Check maps with xdisp 
for alignment and scaling.

XI.  Interpret differences

	Determine STDDEV densities in difference map:  HISTO-0 - examine gnuplot plot of densities 
in differences
	Plot densities with with contours > 2*STDDEV 
  notes:  Koji used 2.66*STDDEV for differences in Cr-ATP map
Difference Map	06/27/99
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