Ml_tomo_v31

!xmipp_ml_tomo (v3.1)

Usage

Align and classify 3D images with missing data regions in Fourier space,

1. g. subtomograms or RCT reconstructions, by a 3D multi-reference refinement based on a maximum-likelihood (ML) target function. For several cases, this method has been shown to be able to both align and classify in a completely __reference-free__manner, by starting from random assignments of the orientations and classes. The mathematical details behind this approach are explained in detail in

   Scheres et al. (2009) Structure, 17, 1563-1572

2. Please cite this paper if this program is of use to you!* There also exists a standardized python script xmipp_protocol__mltomo.py for this program. Thereby, rather than executing the command line options explained below, the user can submit his jobs through a convenient GUI in the GettingStartedWithProtocols, although we still recommend reading this page carefully in order to fully understand the options given in the protocol. Note that this protocol is available from the main xmipp_protocols setup window by pressing the Additional protocols button.) See http://xmipp.cnb.csic.es/twiki/bin/view/Xmipp/Ml_tomo_v3 for further documentation

Parameters

$``: Metadata file with input images (and angles) $--nref <int0> $ or`--ref <file`> $`--oroot, -o &lt;rootname`mltomo&gt; $`--sym <symgroup`c1> $`--missing <metadata`>

Angular sampling $--ang <float10.> $--ang_search <float-1.> $``: Exhaustive psi searches when using -ang_search (only for c1 symmetry) $--limit_trans <float-1.> $`--tilt0 &lt;float`-91.&gt; $`--tiltF <float`91.> $`--psi_sampling <float`-1.>

Regularization $--reg0 <float0.> $--regF <float0.> $--reg_steps <int5>

Others $: Keep orientations from Metadata fixed, only translate and classify $: Keep orientations and tran Metadata (otherwise start from random) $: Keep orientations and classes from docfile, only output weighted averages $: Apply random perturbations to angular sampling in each iteration $--dim <int-1> $--maxres <float0.5> $--thr <int1>

Additional options: $--impute_iter <int1> $--iter <int25> $--istart <int1> $--noise <float1.> $--offset <float3.> $--frac <metadata> $: restart a run with all parameters as in the logfile $: Do not re-estimate the standard deviation in the pixel noise $: Do not re-estimate the standard deviation in the origin offsets $: Do not re-estimate the model fractions $--eps <float5e-5> $--pixel_size <float1> $--mask <maskfile> $: Use constrained cross-correlation and weighted averaging instead of ML $: Use weighted averaging, rather than imputation $--noimp_threshold <float1.>

Examples and notes

Input images file

The input metadata should contain theimage column and =missingRegionNumber, indicating the subtomogran filename and the missing region number, respectively. It canalso contains columns with angles and shift information. The output will be a metadatawith the same format. Follow is an example:

XMIPP_STAR_1 *


data_
loop_
_image
_missingRegionNumber
_angleRot
_angleTilt
_anglePsi
_shiftX
_shiftY
_shiftZ
_ref
_logLikelihood
32_000001.scl  0.00 0.000000 0.000  0.000  0.000000     0.000000  0    0.000000 1
32_000002.scl  0.00 0.000000 0.000  0.000  0.000000     0.000000  0    0.000000 1

Input missing regions file

 # XMIPP_STAR_1 *
 # Wedgeinfo
 data_
 loop_
_missingRegionNumber
  _missingRegionType
  _missingRegionThetaY0
  _missingRegionThetaYF
   1  wedge_y  -64  64

The first column`missingRegionNumber` (starting at 1) is required for each type of missing region, this number should appears in the input images metadata Here`missingRegionType` can be one of the following:

• wedge_y for a missing wedge where the tilt axis is along Y, columsmissingRegionThetaY0 andmissingRegionThetaYF are used
• wedge_x for a missing wedge where the tilt axis is along X, columsmissingRegionThetaX0 andmissingRegionThetaXF are used
• pyramid for a missing pyramid where the tilt axes are along Y and X, same columns aswedge_y andwedge_x are used
• cone for a missing cone (pointing along Z) columnmissingRegionThetaY0 is used

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