auto2d_tutorial

Anonymous (anonymous@undisclosed.example.com) — 2020-08-28T07:36:57+00:00

How to use SPHIRE's Cinderella for 2D class selection This tutorial describes how to use Cinderella to classify 2D class averages. You can either use a pretrained model (see section Classify) or train your own model (see section Training). Download & Install

auto_2d_class_selection

Anonymous (anonymous@undisclosed.example.com) — 2020-08-27T15:11:53+00:00

Cinderella: Deep learning based binary classification tool ---------- ---------- Our binary classification tool (Cinderella) is based on a deep learning network to classify class averages, micrographs or subtomograms into good and bad categories. Cinderella supports

automated_helical_picking

Anonymous (anonymous@undisclosed.example.com) — 2018-06-20T13:12:12+00:00

Helical Picking Procedure Overview The helical particle procedure Installation Get your data into ImageJ Load the raw data * File -> Import -> Image Sequence * Select the folder of your images * Type the file ending into “File name contains:

cinderella_archive

Anonymous (anonymous@undisclosed.example.com) — 2020-08-27T12:17:55+00:00

Cinderella archive Cinderella Version: 0.2.0 Uploaded: 28. May 2019 DOWNLOAD Pretrained model Version 202003 Uploaded: March 2020, DOWNLOAD (Datasets: 22) Version 201912 Uploaded: 10. December 2019, DOWNLOAD (Datasets: 20) Version 20190528 Uploaded: 28. May 2019

cinderella_micrographs

Anonymous (anonymous@undisclosed.example.com) — 2020-08-28T07:35:57+00:00

How to use SPHIRE's Cinderella for micrograph selection This tutorial describes how to use Cinderella to sort micrographs. Unfortunately, we cannot provide a pretrained model yet. Therefore the first step is to train a model (see section Training) and to apply a model (see section

cinderella_tomograms

Anonymous (anonymous@undisclosed.example.com) — 2019-12-13T16:13:09+00:00

How to use SPHIRE's Cinderella for subtomogram selection You can use Cinderella to classify Subtomograms into good/bad categories. This is useful if you want to sort Particles which were previously picked with e.g. template matching. Training To train cinderella we have to create training data. To do that, we extract the central slices from your tomogram (step 1) and select bad and good particles using eman2

cryolo_1_4_1

Anonymous (anonymous@undisclosed.example.com) — 2019-07-24T13:32:40+00:00

Testing the release candidate for 1.4.1 Changes: * Downgrade the dependencies to tensorflow 1.10.1 and numpy 1.14.5 as some users reported long initialization times. (Thanks to Shaun Rawson) * The initialization weights are not longer shipped with the package and downloaded on-the-fly (because they are big and pypi does not allow such big packages)

cryolo_archive

Anonymous (anonymous@undisclosed.example.com) — 2019-10-15T16:41:31+00:00

crYOLO No longer maintained. See pip archive Version: 1.3.6 DOWNLOAD GPU VERSIONDOWNLOAD CPU VERSION Version: 1.3.5 DOWNLOAD GPU VERSIONDOWNLOAD CPU VERSION Version: 1.3.4 DOWNLOAD GPU VERSIONDOWNLOAD CPU VERSION Version: 1.3.3 DOWNLOAD GPU VERSIONDOWNLOAD CPU VERSION Version: 1.3.2 DOWNLOAD GPU VERSIONDOWNLOAD CPU VERSION Version: 1.3.1 DOWNLOAD Version: 1.3.0 DOWNLOAD Version: 1.2.3 DOWNLOAD Version: 1.2.2 DOWNLOAD Version: 1.2.1 …

cryolo_config

Anonymous (anonymous@undisclosed.example.com) — 2019-10-11T17:51:44+00:00

crYOLO configuration file The config file is organized in the sections model, training and validation. In the following you find a description of each entry. Model section: * #01 architecture: The network used in the backend of crYOLO. Right we support

cryolo_filament_import_relion

Anonymous (anonymous@undisclosed.example.com) — 2020-04-16T08:45:06+00:00

How to import crYOLO filament coordinates into Relion After the picking of filaments with crYOLO is done, one might want to import them into Relion. In this example I assume the following: * The folder micrographs contains your images. In this example the filenames are

cryolo_license

Anonymous (anonymous@undisclosed.example.com) — 2018-06-20T14:50:28+00:00

SPHIRE-crYOLO Complimentary Science Software License End User License Agreement Scope of this Software License Agreement: This is the SPHIRE-crYOLO Complimentary Science Software License Agreement, which applies to all software products available for download from the SPHIRE-crYOLO website(s), unless labeled as something other than complimentary.

cryolo_nets

Anonymous (anonymous@undisclosed.example.com) — 2019-04-26T22:07:06+00:00

crYOLO Networks Introduction The original YOLO architecture looks like this: The main components are convolutional operations and max pooling operations: * Convolutional layer: A convolutional learn local patterns. For 2D images, these are patterns in a small region of the input image. In the YOLO architecture, these are

cryolo_picking_unlabeled

Anonymous (anonymous@undisclosed.example.com) — 2019-03-16T18:19:13+00:00

Train crYOLO on sparsely labeled data In the first preprint of crYOLO we wrote the following sentence without any comments: “Ideally, each micrograph should be picked to completion.” However, you don't have to pick all particles in a micrograph to train crYOLO. Here I want to show how crYOLO performs with only sparsely labeled micrographs.

cryolo_reference_example

Anonymous (anonymous@undisclosed.example.com) — 2020-05-08T11:43:08+00:00

crYOLO reference example Here we provide quick run through example for training and picking with crYOLO. The main purpose is to check if your setup is running as expected. I will not provide detailed explanations in this text. Please note that there is

cryolo_release_note_110

Anonymous (anonymous@undisclosed.example.com) — 2018-08-24T08:19:14+00:00

crYOLO 1.1.0 release notes Changes crYOLO * crYOLO now supports filaments (more here) * New evaluation tool (more here) * Supports empty box files for training on particle-free images * Extended data augmentation: Horizontal flip and flip along both axes

cryolo_train_general_model

Anonymous (anonymous@undisclosed.example.com) — 2019-04-19T17:53:12+00:00

Train your own general model Training a model for a specific dataset is very easy with crYOLO. However, you might have multiple data collections of the same particle with different settings, a different camera or another microscope. A model trained on the data of one data collection, might not perform very good on a dataset from another data collection.

gpu_isac

Anonymous (anonymous@undisclosed.example.com) — 2021-05-08T00:41:41+00:00

Overview ISAC (Iterative Stable Alignment and Clustering) is a 2D classification algorithm. It sorts a given stack of cryo-EM particles into different classes that share the same view of a target protein. ISAC is based around iterations of alternating equal size k-means clustering and repeated 2D alignment routines.

gpu_isac_chimera

Anonymous (anonymous@undisclosed.example.com) — 2020-08-03T13:42:29+00:00

Overview ISAC (Iterative Stable Alignment and Clustering) is a 2D classification algorithm to sort cryo-EM particles into classes depicting the same view of a target protein. It is based around iterations of alternating equal size k-means clustering and repeated 2D alignment routines.

janni

Anonymous (anonymous@undisclosed.example.com) — 2022-07-25T11:19:25+00:00

Just Another Noise 2 Noise Implementation (JANNI) JANNI implements a neural network denoising tool described in NVIDIA's noise2noise paper: Noise2Noise: Learning Image Restoration without Clean Data - arXiv It can be trained on your data without the need of ground truth images. It supports MRC and TIFF format. JANNI can be used a command line tool but also provides an simple interface to integrate into other programs (see

janni_tutorial

Anonymous (anonymous@undisclosed.example.com) — 2020-09-24T14:46:39+00:00

Just Another Noise 2 Noise Implementation (JANNI) JANNI implements a neural network denoising tool described in NVIDIA's noise2noise paper: Noise2Noise: Learning Image Restoration without Clean Data - arXiv Besides a simple GUI and a commandline interface, JANNI also provides an simple python interface to be integrated into other programs.

privacy_policy

Anonymous (anonymous@undisclosed.example.com) — 2018-06-20T13:12:09+00:00

sidebar

Anonymous (anonymous@undisclosed.example.com) — 2021-02-19T10:27:19+00:00

SPHIRE Workflow start start start start start start start start start start start How-to * SPHIRE: Getting started * crYOLO: Getting started * GPU ISAC: Getting started * Cinderella: Getting started * JANNI: Getting started * TranSPHIRE: Getting started * Submit a cluster job * Import a star file * FAQ * Mailing list Release history * Stable releases About * SPHIRE Team * Penczek lab * Raunser lab * SPHIRE Twitter * Imprint

sp_bfactor_plot

Anonymous (anonymous@undisclosed.example.com) — 2020-07-23T10:19:55+00:00

sp_bfactor_plot Plot B-factor : Plots number of particles versus resolution. Usage Usage in command line: sp_bfactor_plot.py search_directory output_directory --mode=mode --groups=groups --flo=flo --display=display_exe --verbosity=verbosity --sharpendir=sharpendir --selectpattern=selectpattern --resfile=resfile --apix=apix

sp_ctf_punish

Anonymous (anonymous@undisclosed.example.com) — 2020-07-22T19:17:24+00:00

sp_ctf_punish Apply trapped CTF : Multiplies images by the contrast transfer function only after the first extremum. Usage Usage in command line: sp_ctf_punish.py input_stack output_directory --plot --singlehdf --verbosity --debug Typical usage

sp_ctf_punisher

Anonymous (anonymous@undisclosed.example.com) — 2020-07-22T18:09:24+00:00

sp_ctf_punisher Apply trapped CTF : Multiplies images by the contrast transfer function only after the first extremum. Usage Usage in command line: sp_ctf_punish.py input_stack output_directory --plot --singlehdf --verbosity --debug Typical usage

sphire_1_4

Anonymous (anonymous@undisclosed.example.com) — 2021-07-08T12:01:39+00:00

SPHIRE 1.4 / EMAN 2.9 We are happy to announce the release of SPHIRE 1.4! This release adds GUI support for GPU ISAC and makes SPHIRE fully compatible with TranSPHIRE, allowing you to create on-the-fly reconstructions during data acquisition. As usual, this release is a combined release of SPHIRE, EMAN2, and SPARX. What's new

sphire_information

Anonymous (anonymous@undisclosed.example.com) — 2018-06-20T13:12:12+00:00

sphire_information

sphire_people

Anonymous (anonymous@undisclosed.example.com) — 2020-08-06T15:32:39+00:00

sphire_people

sphire_tutorial

Anonymous (anonymous@undisclosed.example.com) — 2021-02-17T12:04:05+00:00

Here we will put the sphire tutorial that is right now in available as pdf General 1. Software Installation 1.1 Install sphire and MPI support 1.2 Install crYOLO 1.3 Installation of accociated EM Software Packages 2. Project 3. Movie 3.1 Micrograph Movie Alignement

start

Anonymous (anonymous@undisclosed.example.com) — 2021-02-19T10:26:40+00:00

SPHIRE Wiki If you want to dig deep into the SPHIRE pipeline then you are in the right place. This Wiki will provide you with information about the major programs within SPHIRE and how to use them. Of course, you will also find information about installation, additional utilities, frequently asked questions and known issues. In addition, you can find usage tutorials and SPHIRE's version history. Just follow the links in the sidebar.

striper

Anonymous (anonymous@undisclosed.example.com) — 2020-03-02T11:45:49+00:00

Striper STRIPER you to strip your filaments out of the micrograph automatically. Overview Striper is the filament particle picking procedure used in our lab. Installation Activate the “biomedgroup” update site and add a new update site: http://sites.imagej.net/Mpi-do-stripper/

stripper

Anonymous (anonymous@undisclosed.example.com) — 2018-07-04T15:50:36+00:00

Stripper STRIPPER: faST Robust fIlament Picking ProcEduRe Helps you to strip your filaments out of the micrograph automatically :-) Overview Stripper is the filament particle picking procedure used in our lab. Installation Activate the “biomedgroup

sxproj_compare

Anonymous (anonymous@undisclosed.example.com) — 2019-03-19T13:50:34+00:00

sxproj_compare Compares re-projections to class averages. Usage Usage in command line sxproj_compare.py stack input_volume outdir --mode=viper --classangles=angles_file --classselect=img_selection_file --prjmethod=interpolation_method --delta=angular_increment --matchshift=shift_range --matchrad=outer_radius --matchstep=ring_step --symmetry=optional_symmetry --partangles=refinement_params --partselect=substack_select --refineshift=shift_range --outliers=max_angle_diff --refinerad=outer_ra…