Dear Liz,
Don't apologize for this excellent and well-documented question!
The rings you observe are a typical sign of Einstein-from-noise artifacts
that can occur with (any) template-based particle picking. I attach a PDF
of the manuscript I wrote on the auto-picking procedure in RELION. Note
this hasn't been back from peer-review yet, so for the moment just
consider it as my own opinion! :-) We can observe the same effects with
many more data sets. The cause is a too low threshold for the auto-picking
algorithm, which then reproduces the templates from noise areas in the
micrographs. I suppose you used the smaller diameter to calculate the 2D
classes that you used as templates for auto-picking? The normalization in
relion (and many other packages) makes that the average background is
often slightly negative (i.e. black); this is caused by the presence of
particles (with white signal) in the background area. This results in 2D
class averages that have a white ribosome and a slightly negative (black)
background. When you use those for auto-picking and you are too "greedy",
i.e. you set the threshold too low, then you start up picking noise that
also describes your templates. 2D class averaging with those large, noisy
data sets then gives rise to the typical black rings in the class averages
(see also Fig 5 in the attached paper). The good class averages (e.g.
first row, number 5 and second row number 1 &2 in your figure) do not show
these template-induced artefacts.
You can now do 2 things: 1) hope 2D classification deals with this, and
just throw away all Einstein-from-noise classes; or 2) (probably better):
re-run auto-picking with a higher threshold and then re-run 2D
classification.
Many of your good classes seem to represent a similar view, so you may
have a strong preferred orientation, but this is better assessed once
you've cleaned up your data from the false positives of the auto-picking
approach.
The Einstein-from-noise artifacts are an important reason why you should
ALWAYS use low-pass filtered templates for auto-picking. Probably using
beyond 30A information is not useful anyway, and information that did not
go into the process can also not come out as an Einstein-from-noise
artifact. Using high-resolution templates for auto-picking is probably
what went wrong in the HIV trimer structure that caused such a stir-up
last year. Both Richard Henderson and Marin van Heel wrote excellent
commentaries in PNAS on that issue. See
http://www.ncbi.nlm.nih.gov/pubmed/24106306 and
http://www.ncbi.nlm.nih.gov/pubmed/24106301.
HTH,
Sjors
> Hi All
>
> I hope this is not too bone-headed of a question. I am new to using Relion
> (1.3), and I am doing 2D classification and I have been getting some
> results that look suspicious. I am using my own DD data set of yeast 80S
> particles. I followed the steps outlined in "Tutorial (The quickest way to
> learning RELION-1.3) ", for ctf-correction, autopicking, etc. - Now I am
> trying to do some particle pruning with the 2D classification. There are
> two things going on:
> 1) There are two highly populated classes (40% of ~60k particles) that
> look the same - they seem to be neither junk nor good classes.
>
> 2) There are two visible rings for each class - one is the soft mask, as I
> would expect, but the other seems to be related to the rotation of the
> particle during alignment - I've attached an image - it seems to exist on
> both good and bad classes - these are sorted by class distribution. I
> can't figure out what causes this, but I feel like its a good indicator
> I've picked some bad parameter. When I did 2D Classification on a very
> small manual picked set with a very slightly smaller mask (350 A vs 360 A)
> I did not see the same thing. My box size is 300 pix = 396 Ang.
>
> Thanks for any help - please excuse such a possible inane question!
> Liz Montabana
>
> Here is the command:
> mpirun -np 240 --bynode `which relion_refine_mpi` --o
> Class2D/autopick_sort2 --i particles_autopick_rd2.star --particle_diameter
> 360 --angpix 1.32 --ctf --iter 25 --tau2_fudge 2 --K 300
> --flatten_solvent --oversampling 1 --psi_step 10 --offset_
> range 5 --offset_step 2 --norm --scale --j 1 --memory_per_thread 8 >
> Class2D/autopick_sort2.out
>
> and settings:
> Class2D/autopick_sort2.gui_class2d.settings
> is_continue == false
> Output rootname: == Class2D/autopick_sort2
> Continue from here: ==
> Input images STAR file: == particles_autopick_rd2.star
> Number of classes: == 300
> Do CTF-correction? == Yes
> Have data been phase-flipped? == No
> Ignore CTFs until first peak? == No
> Number of iterations: == 25
> Regularisation parameter T: == 2
> Mask individual particles with zeros? == No
> Limit resolution E-step to (A): == -1
> Perform image alignment? == Yes
> In-plane angular sampling: == 5
> Offset search range (pix): == 5
> Offset search step (pix): == 1
> Number of MPI procs: == 240
> Number of threads: == 1
> Available RAM (in Gb) per thread: == 8
> Submit to queue? == Yes
>
>
>
--
Sjors Scheres
MRC Laboratory of Molecular Biology
Francis Crick Avenue, Cambridge Biomedical Campus
Cambridge CB2 0QH, U.K.
tel: +44 (0)1223 267061
http://www2.mrc-lmb.cam.ac.uk/groups/scheres
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