EMAN supports a lot of different formats, and it does it transparently. That is, in general, any EMAN program can read any image in a wide variety of formats. EMAN currently supports reading SPIDER, IMAGIC, MRC, Gatan DM2, Gatan DM3, PIF, ICOS. TIFF images are now natively supported using libtiff. You should now be able to directly read 16 bit tiffs. Most generic image formats like TIFF, GIF, PGM, BMP, PNG, etc. are also supported if you have the IMAGEMAGICK package installed on your machine. Due to Gatan constantly changing things, we cannot guarentee that DM3 file reading will be perfect.
For image writing, EMAN supports most of the above formats as well. However, most EMAN programs default to IMAGIC format (for 2D) and MRC format (for 3D). To convert to a different format, use 'proc2d' for 2D images and 'proc3d' for 3D images.
Some of you may also be aware of the 'byte ordering' issue. Different machines (SGI vs Intel, for example) store their numbers in the opposite byte-order. Often this means files generated on one machine will be unreadable on machines using the opposite convention. However, EMAN handles this problem as well. Any supported image can be read regardless of byte-order. When writing images EMAN uses the native byte order of the machine the software is being run on.
Not exactly. The curve from your data (the power spectrum) is equal to noise + ctf * envelope * structure factor. The curve you're fitting with is just noise + ctf * envelope. The structure factor is missing, and is very important. This problem can be tackeled three ways, described in : http://ncmi.bioch.bcm.tmc.edu/~stevel/EMAN/doc/ctfc/ctfc.html If you're in the situation where you don't have x-ray data (which you presumably don't), the best results are generally achieved via the following process:
1) you will need several data sets at different defocuses
2) read the first data set into CTFIT
3) set the 'amp' to zero, then use the 4 noise sliders to fit the
background by passing through the zeros of the the ctf, and matching the
high-resolution end of the curve (where the zeros are no longer visible).
4) Increase the amplitude and adjust the defocus and envelope function as
best you can. You should be able to determine the defocus quite
accurately. The envelope function is somewhat arbitrary.
5) Read in the second data set, and repeat this process for it.
6) Bring up a second plot window, and set it to display the structure
factor. This will show the structure factor for each displayaed data set,
calculated from the data. This is not a very accurate calculation, but
it's generally good enough.
7) Now, without spoiling the fitting you've just done, adjust all of the
parameters of the 2 data sets such that the structure factor curves match
as well as you can. Don't worry too much about the divergence at high
frequency. Work on getting a match out to the first or second zero, then
just try to get the general trend at high frequency to be the same.
8) continue to add the other data sets in the same way.
9) When you've got them all fitted satisfactorally, use the 'phase
correct' option as described in the instructions.
Gosh, I hope so! Seriously, there are a few issues to be aware of. First, one factor often of concern is the fact that EMAN generally keeps the entire set of particles in a single image file stack A really big problem might cause this file to become bigger than 2 gigabytes, which is a problem for many programs. Never fear, EMAN uses techniques which should be able to handle single image files of virtually any size.
There are other issues. Some are of concern when there are a lot of pixels in each image and others are of concern when there are a lot of images. In the first case, memory on the computer is the biggest problem. For example, if you were trying to reconstruct a 512x512x512 volume, each volume dataset requires 512 MB. Several programs require enough memory for 2 or 3 3D models. So any machine used to process this dataset would need to have at least 2 GB of RAM. There are too many issues involved to cover all possibilities here. In general, I'd say yes, EMAN can handle really big problems. If you run into problems, email me, and I'll try to help you resolve them.
The answer depends on the source images. EMAN currently knows how to read MRC, IMAGIC, Spider and PGM/GIF/TIFF (with some restrictions). If you have each particle in a separate image file, for example, img001.img, img002.img, etc., then the following command would do it (zsh):
foreach i (img*.img)
proc2d $i start.hed
end
-or- (csh)
for i in img*.imgIt doesn't matter what file format the source images are in. Any EMAN program transparently reads any supported image type (byte order doesn't matter either). If the images are already in a Spider stack file called, for example, part.spi, the following would do it:
proc2d $i start.hed
proc2d part.spi part.hed
All of the 2D EMAN commands currently write Imagic files by default.3D
commands write MRC format by default. There are options in proc2d and proc3d
for writing to other file formats.
Tricky. There is a possibility that the answer is "you can't". In most cases, however, it's possible to get a pretty accurate answer. In cases where the symmetry of the particle is unknown, the ability to distinguish between different symmetries is proportional to the overall contrast in the image. In cryo-EM there is always a tradeoff between contrast and resolution, so the best thing to do if you're trying to determine symmetry is exactly the opposite of what you'd do for high resolution. That is, take some micrographs in negative stain, or in ice, fairly far from focus at low voltage. This will provide the best overall contrast for an attempt to determine symmetry.
Once you've collected high-constrast data, there are a number of techniques to try to determine symmetry. for particles with a suspected Cn or Dn symmetry, startcsym is a good starting point. By running it several times with each possible symmetry you can see how well each one fits the data. Frequently comparing the symmetrized model in sym.hed with the class-averages in classes.hed will give the first indications of the true symmetry.
The next step is to try to refine each of the possible initial models and see if they 'fall apart' during refinement. This should resolve the symmetry question IF you have sufficient contrast, and IF your particles are in fairly random orientations. If the contrast is too low, or there is a strongly preferred orientation, however, an answer may not be possible.
If the first technique fails, there are other possibilities, like using multivariate statistical analysis on an aligned set of raw particles. These issues are too complicated for discussuion in this FAQ.
No, the docs don't really explain all of the text output at this point. I can tell you what the numbers are, but I don't think it's going to help you very much. While you may be able to judge the quality of an individual particle when compared to a good model using the quality factor, they really won't tell you what you're trying to find out. There are just too many variables involved.
If you're anxious that things are going too slow, the best approach is to increase the angular step for the first couple of refinement iterations. For an asymmetric model you could go as high as 15 or 18 degrees for the first round or two. That should be enough to tell you if the model is reasonable.
As we tried to impress in the documentation, asymmetric models can be very tricky. It depends on their overall shape. If, for example you have something 'L' shaped, then getting a good starting model shouldn't be difficult at all. However, if you have something that's basically round with a few lumps, it may actually be impossible to generate an unabiguous accurate starting model. It is actually possible to have a set of random projections which can produce several DIFFERENT models, all of which are consistent with the data at some resolution.
StartAny uses c1startup, so no, there's no difference. The routine it uses isn't all that great. For 'easy' models it will work pretty well, but in tough cases, it may just come up with something completely wrong. In these cases, there are really only two good solutions in EMAN right now:
1) if your model may have a pseudosymmetry, ie - it's vaguely cylindrical in shape or something, you can often use the startcsym routine and get something that's good enough to start.
2) Final resort. Use tomography. If you're comfortable with it, then you might actually start here. Anyway, the idea is simple enough, take a tilt series (probably have to use stain or glucose for this). EMAN has a few experimental programs for generating a 3D model from the tilt series and aligning/averaging several such 3D models to generate a starting model. Even this approach isn't perfect (at least the simple implementation EMAN uses isn't), but we have used is with some success on a few projects.
To answer your question anyway: the output from classesbymra looks like:
0 -> 256 (506.86)
1 -> 296 (508.74)
2 -> 278 (502.86)
3 -> 273 (504.82)
The first line is saying that particle 0 (the first one in start.hed) looked the most like projection number 256. The quality factor was 506.86. The interpretation of the quality factors can depend on the shape of your model and the box size, etc.
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