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5 | 6 | Cryo-EM 7 | 8 | 9 | 10 | ### Contributing 11 | 12 | Please take a quick look at the [contribution guidelines](.github/CONTRIBUTING.md) first. If you see a package or project here that is no longer maintained or is not a good fit, please submit a pull request to improve this file. Thank you to all [contributors](https://github.com/barrykui/awesome-cryoem/graphs/contributors); you rock! 13 | 14 | ### Contents 15 | 16 | - [Guides](#guides) 17 | - [Official Guides](#official-guides) 18 | - [Third party Guides](#third-party-guides) 19 | - [Softwares](#softwares) 20 | - [Technologies](#technologies) 21 | - [Computational Problems](#computational-problems) 22 | - [Validation Metrics](#validation-metrics) 23 | - [DataBases](#database) 24 | - [Active Groups](#active-groups) 25 | 26 | 27 | ## Guides 28 | *An awesome list of CryoEM related guides.* 29 | 30 | 31 | 32 | 33 | ### Official Guides 34 | [back to top](#readme) 35 | 36 | * [3 Mins Introduction of CryoEM](https://www.youtube.com/watch?v=BJKkC0W-6Qk) - 3 Mins Introduction of CryoEM for beginners. 37 | * [Single-particle cryo-electron microscopy](http://www.nature.com/nmeth/journal/v13/n1/full/nmeth.3700.html) - Nature Method Review. 38 | * [CryoEM Course](https://www.coursera.org/learn/cryo-em) 39 | * [CryoEM 101](https://cryoem101.org) 40 | * [MRC lab CryoEM](https://www2.mrc-lmb.cam.ac.uk/research/scientific-training/electron-microscopy/) 41 | 42 | ### Third party Guides 43 | [back to top](#readme) 44 | 45 | * [EMAN2 Video Tutorials](http://blake.bcm.edu/emanwiki/EMAN2/VideoTutorials) 46 | 47 | ## Methods and Softwares 48 | [back to top](#readme) 49 | 50 | * [UCSF Chimera](https://www.cgl.ucsf.edu/chimera/) - An interactive visualization and analysis of structures. 51 | * [UCSF ChimeraX](https://www.cgl.ucsf.edu/chimera/) - An interactive visualization and analysis of structures. [[code]](https://github.com/RBVI/ChimeraX) 52 | * [Relion](http://www2.mrc-lmb.cam.ac.uk/relion/index.php/Main_Page) - A Bayesian approach to refinement of 3D reconstructions or 2D class averages. 53 | * [`New` 2.1 ](ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/relion21_tutorial.pdf) - Tutorial (v2.1) (The quickest way to learning RELION) 54 | * [Nature Protocol Paper](http://www.nature.com/nprot/journal/v11/n11/full/nprot.2016.124.html) - Resolving macromolecular structures from electron cryo-tomography data using subtomogram averaging in RELION 55 | 56 | * [COOT](http://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/) - A interactive visualization model building, model completion and validation. 57 | * [EMAN2](http://blake.bcm.edu/emanwiki/EMAN2) - A scientific image processing software suite with a focus on CryoEM and CryoET. 58 | * [PHENIX](https://www.phenix-online.org/) - Automated determination of molecular structures using X-ray crystallography and other methods. 59 | * [Rosetta](https://www.rosettacommons.org/) - A software suite includes algorithms for computational modeling and analysis of protein structures. 60 | * [FREALIGN: high-resolution refinement of single particle structures](#) 61 | * [SIMPLE: Software for ab initio reconstruction of heterogeneous single-particles](#) 62 | * [PRIME: probabilistic initial 3D model generation for single-particle cryo-electron microscopy](#) 63 | * [SPIDER](http://spider.wadsworth.org) - System for Processing Image Data from Electron microscopy and Related fields. 64 | * [CCP4](http://www.ccp4.ac.uk/) - Collaborative Computational Project No. 4 Software for Macromolecular X-Ray Crystallography. 65 | * [Buccaneer](#) 66 | * [SFTOOLS](#) 67 | * [ResMap](http://resmap.sourceforge.net/) - computing the local resolution of 3D density maps. 68 | * [DeepPicker](https://arxiv.org/abs/1605.01838) - Fully Automated Particle Picking using deep learning. 69 | * [FindEM](http://www.ccpem.ac.uk/ccpem_projects.php) - CCP-EM projects, automated particle picking from electron micrographs, using Fortran 70 | * [EMfold](http://www.meilerlab.org/index.php/servers/show?s_id=18) - Meiler Lab, placement of helices is restricted to CryoEM density regions. 71 | * [De novo protein structure determination from near-atomic-resolution cryo-EM maps](http://www.nature.com/doifinder/10.1038/nmeth.3287) 72 | * [Atomic accuracy models from 4.5 Å cryo-electron microscopy data with density-guided iterative local refinement](http://www.nature.com/doifinder/10.1038/nmeth.3286) 73 | * [cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination](http://www.nature.com/nmeth/journal/v14/n3/full/nmeth.4169.html) 74 | * [Building proteins in a day: Efficient 3D molecular reconstruction(CVPR2015)](#) 75 | * [Pathwalker](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307788/pdf/nihms350767.pdf) - Constructing and Validating Initial Cα Models from Subnanometer Resolution Density Maps with Pathwalking, TSP 76 | * [EMBuilder](https://www.nature.com/articles/s41598-017-02725-w) - EMBuilder: A Template Matching-based Automatic Model-building Program for High-resolution Cryo-Electron Microscopy Maps 77 | * Cryoem-cloud-tools: A software platform to deploy and manage cryo-EM jobs in the cloud. [[paper]](https://europepmc.org/article/PMC/6091888), [[papge]](http://cryoem-tools.cloud/) 78 | 79 | ## Technologies 80 | [back to top](#readme) 81 | 82 | * [Single Particle](#) 83 | * [Tomography](#) 84 | * [MircoED](#) 85 | 86 | ## Computational Problems 87 | [back to top](#readme) 88 | 89 | ### Particle Picking 90 | * Fully Automatic 91 | * [DeepPicker](https://arxiv.org/abs/1605.01838) - Fully Automated Particle Picking using deep learning. 92 | * [FindEM](http://www.ccpem.ac.uk/ccpem_projects.php) - CCP-EM projects, automated particle picking from electron micrographs, using Fortran 93 | * [DeepEM](http://arxiv.org/pdf/1605.05543v1.pdf) - A deep learning approach to single-particle recognition in cryo-electron microscopy,Yanan Zhu, Qi Ouyang, Youdong Mao. 94 | * [SPHIRE-crYOLO](https://www.biorxiv.org/content/early/2018/06/26/356584) - SPHIRE-crYOLO: A fast and well-centering automated particle picker for cryo-EM. 95 | * [PIXER](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2614-y) - PIXER: an automated particle-selection method based on segmentation using a deep neural network. 96 | * [A fast method for particle picking in cryo-electron micrographs based on fast R-CNN](https://aip.scitation.org/doi/pdf/10.1063/1.4982020) 97 | * [Real-time cryo-EM data pre-processing with warp](https://www.biorxiv.org/content/10.1101/338558v1) 98 | * [Topaz](https://arxiv.org/pdf/1803.08207) - Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs, [Nature Method Version](https://www.ncbi.nlm.nih.gov/pubmed/29707703) 99 | * [DRPnet](https://www.biorxiv.org/content/biorxiv/early/2019/05/05/616169.full.pdf) - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression. 100 | * [AutoCryoPicker](https://www.biorxiv.org/content/10.1101/561928v1) - AutoCryoPicker: An Unsupervised Learning Approach for Fully Automated Single Particle Picking in Cryo-EM Images 101 | * [DeepCryoPicker](https://www.biorxiv.org/content/10.1101/763839v1.full.pdf) - DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM 102 | 103 | * Semi Automatic 104 | * [AutoPicker](https://www.sciencedirect.com/science/article/pii/S1047847714002615) - Semi-automated selection of cryo-EM particles in RELION-1.3. 105 | 106 | ### Pre-processing and Denoising 107 | * **GAN-Denosing** - Generative adversarial networks as a tool to recover structural information from cryo-electron microscopy data. [[paper]](https://www.biorxiv.org/content/early/2018/02/12/256792). 108 | * **Warp** - Real-time cryo-EM data pre-processing with Warp. [[paper]](https://www.biorxiv.org/content/10.1101/338558v1). 109 | * **Topaz-Denoise**: general deep denoising models for cryoEM. [[paper]](), [[bioRxiv]](https://www.biorxiv.org/content/10.1101/838920v1) 110 | * **DeepEMhacer**: a deep learning solution for cryo-EM volume post-processing. [[paper]](https://www.biorxiv.org/content/10.1101/2020.06.12.148296v1?rss=1) 111 | * **TranSPHIRE**: Automated and feedback-optimized on-the-fly processing for cryo-EM. [[paper]](https://www.biorxiv.org/content/10.1101/2020.06.16.155275v1?rss=1) 112 | * **Phenix.auto_sharpen**: Automated map sharpening by maximization of detail and connectivity. [[paper]](https://www.biorxiv.org/content/10.1101/247049v1.full.pdf) 113 | * **Phenix.density_modification**: Automated map sharpening by maximization of detail and connectivity. [[paper]](https://www.biorxiv.org/content/10.1101/845032v1) 114 | * **Deepsharpen**: Deep-Learning Based Sharpening Of 3D Reconstruction Map From Cryo-Electron Microscopy. [[paper]](https://ieeexplore.ieee.org/abstract/document/9153369/) 115 | * **SuperEM**: Super-Resolution Cryo-EM Maps With 3D Deep Generative Networks. [[paper]](https://www.biorxiv.org/content/10.1101/2021.01.12.426430v1) [[code]](https://github.com/kiharalab/SuperEM) [[webpage]](https://kiharalab.org/emsuites/superem.php) 116 | 117 | 118 | ### Motion Correction 119 | * Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM, [[paper]](http://www.nature.com/nmeth/journal/v10/n6/full/nmeth.2472.html). 120 | 121 | ### 3D Reconstruction 122 | * **Relion** - A Bayesian approach to refinement of 3D reconstructions or 2D class averages. [[webpage]](http://www2.mrc-lmb.cam.ac.uk/relion/index.php/Main_Page), [[code]](https://github.com/3dem/relion) 123 | * [Nature Protocol Paper](http://www.nature.com/nprot/journal/v11/n11/full/nprot.2016.124.html) - Resolving macromolecular structures from electron cryo-tomography data using subtomogram averaging in RELION 124 | * **2.1** [[code]](https://github.com/3dem/relion/releases/tag/2.1.0), [Tutorial (v2.1)](ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/relion21_tutorial.pdf) 125 | * **3.0** - New tools for automated high-resolution cryo-EM structure determination in RELION-3. [[paper]](https://elifesciences.org/articles/42166) 126 | * **externprior** - Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination. [[paper]](https://www.biorxiv.org/content/10.1101/2020.03.25.007914v1.full.pdf), [[code]](https://github.com/3dem/externprior) RELION external reconstruct functionality with a convolutional neural network. 127 | * **3.1** [[code]](https://github.com/3dem/relion/releases/tag/3.1.0), [Tutorial (v3.1)](ftp://ftp.mrc-lmb.cam.ac.uk/pub/scheres/relion31_tutorial.pdf) 128 | 129 | * **cryoSPARC**: algorithms for rapid unsupervised cryo-EM structure determination. Nature Methods, 2017. [[paper]](http://www.nature.com/nmeth/journal/v14/n3/full/nmeth.4169.html) 130 | * **THUNDER**: A particle-filter framework for robust cryo-EM 3D reconstruction. Nature Methods, 2018. [[paper]](https://www.nature.com/articles/s41592-018-0223-8) 131 | * **CryoDRGN** - Reconstructing continuously heterogeneous structures from single particle cryo-EM with deep generative models. ICLR 2020(spotlight). [[paper]](https://arxiv.org/pdf/1909.05215). 132 | * **CryoGAN**: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning. [[paper]](https://www.biorxiv.org/content/10.1101/2020.03.20.001016v1). 133 | 134 | 135 | ### Model Building 136 | 137 | * **PHENIX** - Automated determination of molecular structures using X-ray crystallography and other methods. [[webpage]]((https://www.phenix-online.org/)). 138 | * Map_to_model - A fully automatic method yielding initial models from high-resolution electron cryo-microscopy. Nature Methods, 2018. [[paper]](https://www.nature.com/articles/s41592-018-0173-1), [[bioRxiv]](https://www.biorxiv.org/content/biorxiv/early/2018/02/16/267138.full.pdf). 139 | * **Rosetta** - A software suite includes algorithms for computational modeling and analysis of protein structures. [[webpage]](https://www.rosettacommons.org/). 140 | * **RosettaCM** - High-Resolution Comparative Modeling with RosettaCM. [[paper]](http://www.sciencedirect.com/science/article/pii/S0969212613002979?via%3Dihub). 141 | * De novo protein structure determination from near-atomic-resolution cryo-EM maps. Nature Methods, 2015. [[paper]](http://www.nature.com/doifinder/10.1038/nmeth.3287). 142 | * Atomic accuracy models from 4.5 Å cryo-electron microscopy data with density-guided iterative local refinement. Nature Methods, 2015. [[paper]](http://www.nature.com/doifinder/10.1038/nmeth.3286). 143 | * RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps. Nature Methods, 2017. [[paper]](http://www.nature.com/nmeth/journal/v14/n8/full/nmeth.4340.html). 144 | * **ISOLDE** - Ease the task of model building at low resolution. [[webpage]](https://isolde.cimr.cam.ac.uk/). 145 | * **EMfold** - Placement of helices is restricted to CryoEM density regions. [[webpage]]((http://www.meilerlab.org/index.php/servers/show?s_id=18) ) 146 | * **Pathwalker** - Constructing and Validating Initial Cα Models from Subnanometer Resolution Density Maps with Pathwalking. [[paper]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3307788/pdf/nihms350767.pdf). 147 | * **EMBuilder**: A Template Matching-based Automatic Model-building Program for High-resolution Cryo-Electron Microscopy Maps. [[paper]](https://www.nature.com/articles/s41598-017-02725-w). 148 | * Tools for Model Building and Optimization into Near-Atomic Resolution Electron Cryo-Microscopy Density Maps. [[Book chapter]](https://www.sciencedirect.com/science/article/pii/S0076687916301136?via%3Dihub). 149 | * **MAINMAST** - De novo main-chain modeling for EM maps using MAINMAST. [[paper]](https://www.nature.com/articles/s41467-018-04053-7), [[webpage]](http://kiharalab.org/mainmast/). 150 | * **A^2-Net**: Molecular Structure Estimation from Cryo-EM Density Volumes. The 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019. [[paper]](https://arxiv.org/abs/1901.00785), [[webpage]](http://zhanglab.net/A-2-Net). 151 | * **Cascaded-CNN**: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps. [[paper]](https://www.biorxiv.org/content/10.1101/572990v3), [[code]](https://github.com/DrDongSi/Ca-Backbone-Prediction). 152 | * **DeepTracer**: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Density Maps of Protein Complexes. [[paper]](https://www.biorxiv.org/content/10.1101/2020.02.12.946772v1), [[paper2]](https://www.biorxiv.org/content/10.1101/2020.07.21.214064v2), [[web service]](https://deeptracer.uw.edu/). 153 | * **MSTree** - Automatic building of protein atomic models from cryo-EM density maps using residue co-evolution. [[paper]](https://www.biorxiv.org/content/10.1101/2020.01.03.893669v1.full.pdf). 154 | * **Haruspex** - Automatic annotation of Cryo-EM maps with the convolutional neural network. [[paper]](https://www.biorxiv.org/content/10.1101/644476v3.full.pdf). 155 | * Scipion - Integration of Cryo-EM Model Building Software in Scipion, 2020. [[paper]](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b01032), [[webpage]](http://scipion.i2pc.es), [[code]](https://github.com/I2PC/scipion). 156 | 157 | ### Refinement 158 | [back to top](#readme) 159 | 160 | * Phenix.real_space_refinement 161 | * REFMAC 162 | * ProSMART - reference restraints for proteins and nucleic acids 163 | * LIBG - base-pair and parallelization restraints 164 | * Rosetta 165 | * EM-fit 166 | * MDFF, molecular dynamics flexible fitting 167 | * DireX 168 | 169 | ## Structure Validation 170 | [back to top](#readme) 171 | 172 | * **Molprobity** 173 | * **EMRinger**: side chain–directed model and map validation for 3D cryo-electron microscopy. [[paper]](https://www.nature.com/articles/nmeth.3541), [[code]](https://github.com/fraser-lab/EMRinger). 174 | * [RMSD](https://en.wikipedia.org/wiki/Root-mean-square_deviation_of_atomic_positions) - Root Mean Square Deviation 175 | * [FSC](https://en.wikipedia.org/wiki/Fourier_shell_correlation) - Fourier shell correlation. 176 | * [B-factor](http://www.cmbi.ru.nl/bdb/theory/) - A measure of (local) mobility in the (macro)molecule. 177 | * [GDT-HA](http://onlinelibrary.wiley.com/doi/10.1002/prot.21753/full) - The percentage of correctly aligned residues in the 5 Å LGA sequence-independent superposition of the model and experimental structure of the target. 178 | * [GDT-TS](#) 179 | * [AL0](#) 180 | 181 | ### Other Related Research/Tools 182 | * **ResMap** - computing the local resolution of 3D density maps, 2013. [[paper]](https://www.nature.com/articles/nmeth.2727), [[code]](http://resmap.sourceforge.net/),[[code-python3+variant-shape]](https://github.com/kuixu/ResMap) 183 | *Automated Threshold Selection for Cryo-EM Density Maps. [[paper]](https://www.biorxiv.org/content/biorxiv/early/2019/06/02/657395.full.pdf) 184 | * Extraction of Protein Dynamics Information Hidden in Cryo-EM Map Using Deep Learning, 2020. [[paper]](https://www.biorxiv.org/content/10.1101/2020.02.17.951863v1), [[code]](https://github.com/clinfo/DEFMap) 185 | * **MicrographCleaner**: a python package for cryo-EM micrograph cleaning using deep learning, [[paper]](https://www.biorxiv.org/content/10.1101/677542v3) - 186 | * Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps. [[paper]](https://doi.org/10.3390/molecules24061181) 187 | 188 | ### Tomography 189 | * **EMAN2** - A scientific image processing software suite with a focus on CryoEM and CryoET. [[webpage]]((http://blake.bcm.edu/emanwiki/EMAN2)), [[code]](https://github.com/cryoem/eman2). 190 | * CryoET Segmentation - Convolutional Neural Networks for Automated Annotation of Cellular CryoElectron Tomograms. [[paper]](https://www.nature.com/nmeth/journal/v14/n10/full/nmeth.4405.html), [[arxiv]](https://arxiv.org/pdf/1701.05567.pdf) 191 | * Subtomogram Subdivision, Deep learning based subdivision approach for large scale macromolecules 192 | structure recovery from electron cryo tomograms. [[paper]](https://arxiv.org/pdf/1701.08404.pdf) 193 | * **pytom**. [[webpage]](http://pytom.org/), [[Tutorial]](http://pytom.org/doc/pytom/tutorial.html) 194 | * **emClarity**: software for high-resolution cryo-electron tomography and subtomogram averaging. [[paper]](http://dx.doi.org/10.1038/s41592-018-0167-z), [[code]](https://github.com/bHimes/emClarity), [[wiki]](https://github.com/bHimes/emClarity/wiki) 195 | 196 | 197 | ## DataBases 198 | [back to top](#readme) 199 | 200 | * [EMDB](https://www.ebi.ac.uk/pdbe/emdb/index.html) - The Electron Microscopy Data Bank (EMDB) 201 | * [EMPIAR](https://www.ebi.ac.uk/pdbe/emdb/empiar) - EMPIAR, the Electron Microscopy Pilot Image Archive, is a public resource for raw, 2D electron microscopy images. 202 | * [EMPIAR: a public archive for raw electron microscopy image data](http://www.nature.com/doifinder/10.1038/nmeth.3806) 203 | * [PDB](http://www.rcsb.org/pdb/home/home.do) - Protein Data Bank 204 | * [PDBe](http://www.ebi.ac.uk/pdbe) - Protein Data Bank in Europe 205 | * [PDBj](http://www.pdbj.org) - Protein Data Bank Japan 206 | * [wwPDB](http://www.wwpdb.org) - WorldWide Protein Data Bank 207 | * [sbkb](http://www.sbkb.org) - Structural Biology Knowledgebase, A comprehensive resource for developments both in structural genomics and structural biology. 208 | 209 | 210 | ## Active Groups 211 | 212 | * [MRC](http://www2.mrc-lmb.cam.ac.uk/). 213 | * [Richard Henderson](http://www2.mrc-lmb.cam.ac.uk/group-leaders/h-to-m/richard-henderson/). 214 | * [Scheres](http://www2.mrc-lmb.cam.ac.uk/groups/scheres/). 215 | * [Joachim Frank](http://franklab.cpmc.columbia.edu/franklab). 216 | * [Bob Glaeser](http://mcb.berkeley.edu/faculty/all/glaeserr). 217 | * [Yifan Cheng](http://cryoem.ucsf.edu/). 218 | * [Yigong Shi](http://ygshi.life.tsinghua.edu.cn/home.htm). 219 | * [Eva Nogales](http://cryoem.berkeley.edu/). 220 | * [David Baker](http://www.ipd.uw.edu/people/ipd-faculty-staff/david-baker/). 221 | * [Frank DiMaio](https://faculty.washington.edu/dimaio/wordpress/). 222 | * [Xueming Li](http://life.tsinghua.edu.cn/faculty/faculty/2730.html). 223 | * [Hong-wei Wang](http://cryoem.life.tsinghua.edu.cn). 224 | * [Marcus Brubakero](http://www.cs.toronto.edu/~mbrubake/). 225 | * [Meiler Lab](http://www.meilerlab.org/index.php). 226 | * [Sriram Subramaniam](https://electron.med.ubc.ca/). 227 | * [Michael Cianfrocco Lab](http://www.lsi.umich.edu/labs/michael-cianfrocco-lab). 228 | * [Kihara Lab](http://kiharalab.org/mainmast/). 229 | * [Bonnie Berger](http://people.csail.mit.edu/bab/). 230 | 231 | [3D-EM Laboratories](http://3dem.ucsd.edu/labs_a_c.shtm) 232 | 233 | 234 | ## Workshop Docs 235 | 236 | * [EMAN2 ](http://blake.bcm.edu/emanwiki/EMAN2/Tutorials) 237 | * [Resource from Meiler Lab](http://www.meilerlab.org/index.php/jobs/resources) - Rosetta Tutorials, Teaching Resources, etc. 238 | 239 | ## Websites 240 | 241 | * [Software Tools For Molecular Microscopy](http://en.wikibooks.org/wiki/Software_Tools_For_Molecular_Microscopy) 242 | 243 | 244 | ## License 245 | 246 | [![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/) 247 | 248 | --------------------------------------------------------------------------------