XMIPP introductory demo

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1.2.5 Other file formatsWe briefly mention some of the other file formats encountered in XMIPP.• data-format: An ASCII text format, used by many classification programs, so that besidesimages one may also classify rotational spectra, or any numerical array. See xmipp_convert_img2data.• sym-format: There are two ways of describing symmetry in XMIPP. The recommendedone is via point group acronyms (see the symmetry Wiki), the second one is via a symmetryparameter file (see xmipp_symmetrize).• ctfparam-format: ASCII files describing the CTF parameters of individual images or micrographs.See xmipp_fourier_filter.• ctfdat-format: A two-column ASCII-file with the names of images in the first column andthe names of CTF-parameter file in the second column. See xmipp_ctf_correct_phase.1.3 Standardized protocolsThe multitude of stand-alone Xmipp programs allows a broad functionality. Just by typingcommand line instructions, the user may devise its own data processing strategy. However, fornovice users it may be hard to find out which program to use at each stage, while the more experiencedusers may repeat certain sequences of command-line instructions many times. Therefore,a higher-level layer has recently been added to Xmipp in the form of executable pythonscripts for the more popular image processing strategies in XMIPP [Scheres et al., Nature Protocols,submitted]. To further ease their use, a graphical user-interface (GUI) has been developed(xmipp_protocols). Also see the GettingStartedWithProtocols Wiki page for more detailedinformation.Clicking the buttons for the distinct processing tasks will copy the standardized python scriptsto your directory, and open a GUI-window that allows to edit the parameters in the script headersand subsequent script execution. (The GUI actually functions as a limited editor of the scriptheader. If you don’t like GUI’s or you don’t have the required python-tk installation, you mayalso edit the script headers using a normal text editor, and execute the script via the commandline.) Besides scripts for job execution, also scripts that help in visualizing their results havebeen developed.In this demo, we will mainly work with the standardized python scripts. If you are planningon using XMIPP on a regular basis, it is probably a good idea to keep track of which individualXMIPP commands were executed by inspecting the log-files that are output by the differentscripts (by default in a directory called Logs/).6
2 Your first 3D reconstruction in XMIPPRandom Conical Tilt reconstruction: To make a three-dimensional reconstruction ofelectron microscopy images one needs to combine projections from a large number of identicalobjects in different orientations. In this demo, the experimental projections have been obtainedby tilting the specimen inside the electron microscope following a collection geometry knownas random conical tilt (Radermacher, 1987). The only constraint imposed by this technique isthat the macromolecules must tend to lay onto the grid showing one or a few preferential views.Although this requirement may seem restrictive, it is fairly usual for biological molecules tointeract with the film support producing a collection of preferential views.The random azimuthal (in-plane) rotations make that the imaged macromolecules presentmany different views when we tilt the micrograph in the microscope. This set of views isformally equivalent to a conical tilt series with the cone angle equal to the tilt angle and theazimuthal angle set at random (Fig. 1). Two micrographs of each area of interest are taken. Thefirst one, which is tilted, provides the views used for the three-dimensional reconstruction, whilethe second one, which is not tilted, is employed to calculate the relative in-plane rotation anglesby cross-correlation techniques. Implicit to the method is the assumption that all projectionsderive from copies of identical three-dimensional objects, or that it is possible to classify themprior to the 3D-reconstruction.Therefore our task is to: select pairs of images in the different micrographs, align and classifythe untilted views, and make a 3D reconstruction with the tilted views.2.1 What are we working withIn this practical session we will work with images of the bacteriophage SPP1 hexameric helicaseG40P. Núñez-Ramírez et al. (2006). This protein interacts with the carbon support, resultingin a strong preference for frontal views, which makes it a suitable sample to be analysed byRandom Conical Tilt. From the literature we know that this protein is able to arrange into twodifferent states with different rotational symmetry, 3-fold and 6-fold. After hours of work in themicroscope (RCT data collection may be cumbersome!) you have pairs of tilted and untiltedmicrographs. That is, pairs of micrographs of the same area taken with different angles, one at0º and the other at 55º. We collected our data on photographic film, which was developed andsubsequently scanned yielding our micrographs in TIFF format. If you have downloaded andextracted the tarball mentioned on the first page of this document, you should have two of thesepairs (called pair1 and pair2) in the directory called my_experiment/Micrographs.2.2 Preprocess micrographsWe typically use a separate directory for each project/experiment (called the projectdir). Go tothe projectdir, in this case my_experiment:cd my_experimentto launch the GUI type:7
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XMIPP introductory demo

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