Differences between revisions 40 and 41
 ⇤ ← Revision 40 as of 2015-10-08 10:22:55 → Size: 4095 Editor: penczek Comment: ← Revision 41 as of 2017-05-07 11:48:11 → ⇥ Size: 0 Editor: penczek Comment: Outdated Deletions are marked like this. Additions are marked like this. Line 1: Line 1: = Sequence of steps necessary to detemine single particle structure using cryo data (with CTF estimation) ==== Step 1 === 1. Convert scanned tiff micrographs into '''hdf''' files. The files can be examined using '''e2display.py'''.  . i. Run [[sxcopyfromtif|sxcopyfromtif.py]] .  . ii. Set foc=f if micrographs are scanned from films.  . iii. At this stage, one has to know the pixel size {{{`"""Pixel_size"""` }}} of scanned micrograph, or CCD frames.  . {{{`"""Pixel_size"""=("""Scan_step_size""")/("""Magnification""")`}}}  . where Scan_step_size is the scan step size of the scanner, or physical pixel size of CCD camera (Scan_step_size of CCD in Polara is {{{`15*10^(-6)m`}}}). Nicon scanners have pixel size : {{{`6.35*10^(-6) m`}}}.  1. Calculate power spectra of all micrographs, examine them, delete micrographs that show drift, have no Thon rings and so on...  . i. Calculate power spectrum using Welch's periodogram: Run [[sxwelch_psp|sxwelch_psp.py]] .  . ii.Properly set mask radius for exclusion of the central peak in the power spectrum (10 pixels or more ) will help to identify drift or astigmatism in the power spectra.  . iii. 1-D rotationally averaged power spectra (have roo_ as prefix) can be used for visualization. 1. Calculate defocus of each micrograph from 1D rotationally averaged power spectra, and store the defocus value in the header of micrographs by running [[sxdefocus_calc|sxdefocus_calc.py.]] 1. Run the automated particle program, particles from each micrograph are in a separate stack file in '''spider''' format.  . i. Run [[sxauto_picking|sxauto_picking.py]]  . ii.The template file has to be created before using this command.  . iii. Properly setting n_plt helps to reduce the number of bad particles.  . iv. Select a noise image from an arbitary micrograph. Save the coordinate file in text tile as noisedoc.spi.  . v. The program will fill in headers of each particle with particle coordinates, micrograph name, and defocus and accociated CTF paramters ''Pixel_size'', ''defocus'', ''voltage'', ''Cs'', ''amp_contrast'', ''B_factor''. 1. Screen all stack files and delete windows that do not contain particles. This is done using program '''v2''':  . i. v2   . ii. middle-click on the image display  . iii. select 'del' mode from the instpector window  . iv. click on the particles you want to get rid of  . v. when done, write the results to a new stack file  . vi. delete input stack file.   . '''Warning''': deleted particles vanish permanently. However, assuming the micrographs are still around and one knows the parameters of the automated particle program in #3, the original stack files can be easily recreated. 1. Convert individual stack files into one stack file that has to be in '''hdf''' format and should have the following attributes set (read more in [[I_O]], examples are found in sparx/templates):  . 1. For 2-D alignment: set ''alpha'', ''sx'', ''sy'', and ''mirror'' (all to zero).  . 2. For projection alignment (structure refinement): ''phi'', ''theta'', ''psi'', ''s2x'', and ''s2y'' have to be set in the header of each file. If their values are not known, all should be set to zero.   . 3. CTF parameters (always): ''Pixel_size'', ''defocus'', ''voltage'', ''Cs'', ''amp_contrast'', ''B_factor''. In addition, if data was premultiplied by the CTF, ''ctf_applied'' has to be set to one, otherwise, set to zero. ''B_factor'' has default value of 0 and amplitude contrast 0.1. The defition of the latter may vary from one package to the other, for the definition used in SPARX, see [[filt_ctf]]. 1. Run 2-D alignment programs, first [[sxali2d c]], for high resolution work next [[sxali2d e]]. 1. Run 2-D classification. First apply alignment parameters to the images in the stack file used in step 3 by running [[sxtransform2d]] and on the resulting stack run [[sxk_means]] and [[sxk_means_groups]] 1. Determine the initial structure using class averages from step 4. 1. Run 3-D projection alignment [[sxali3d d]].