IceBreaker: Software for high-resolution single-particle cryo-EM with non-uniform ice.
IceBreaker: Software for high-resolution single-particle cryo-EM with non-uniform ice.
复制标题
DOI:
10.1016/j.str.2022.01.005
复制
发表时间:
2022-04-07
期刊:
影响因子:
5.7
通讯作者:
Zhang, Peijun
中科院分区:
文献类型:
--
作者:
Olek, Mateusz;Cowtan, Kevin;Webb, Donovan;Chaban, Yuriy;Zhang, Peijun
Despite the abundance of available software tools, optimal particle selection is still a vital issue in single-particle cryoelectron microscopy (cryo-EM). Regardless of the method used, most pickers struggle when ice thickness varies on a micrograph. IceBreaker allows users to estimate the relative ice gradient and flatten it by equalizing the local contrast. It allows the differentiation of particles from the background and improves overall particle picking performance. Furthermore, we introduce an additional parameter corresponding to local ice thickness for each particle. Particles with a defined ice thickness can be grouped and filtered based on this parameter during processing. These functionalities are especially valuable for on-the-fly processing to automatically pick as many particles as possible from each micrograph and to select optimal regions for data collection. Finally, estimated ice gradient distributions can be stored separately and used to inspect the quality of prepared samples. Develop a software tool for image segmentation based on estimated ice thickness Present a method to detect and annotate ice contamination in the dataset Show a procedure to equalize contrast on the micrographs with the non-uniform ice Demonstrate a workflow to identify optimal ice for data collection/particle selection Olek et al. present a software tool, IceBreaker, for handling non-uniform ice thickness in cryo-EM micrographs. Ice thickness is believed to be a crucial factor that affects the quality of cryo-EM reconstructions. IceBreaker provides empirical estimation of the ice distribution and introduces an ice thickness parameter to the cryo-EM processing pipeline.
登录
查看更多内容
影响因子:
48
作者:
Kucukelbir, Alp;Sigworth, Fred J.;Tagare, Hemant D.
通讯作者:
Tagare, Hemant D.
影响因子:
64.8
作者:
Nakane T;Kotecha A;Sente A;McMullan G;Masiulis S;Brown PMGE;Grigoras IT;Malinauskaite L;Malinauskas T;Miehling J;Uchański T;Yu L;Karia D;Pechnikova EV;de Jong E;Keizer J;Bischoff M;McCormack J;Tiemeijer P;Hardwick SW;Chirgadze DY;Murshudov G;Aricescu AR;Scheres SHW
通讯作者:
Scheres SHW
影响因子:
64.8
作者:
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者:
Oliphant TE
影响因子:
48
作者:
Li, Xueming;Mooney, Paul;Zheng, Shawn;Booth, Christopher R.;Braunfeld, Michael B.;Gubbens, Sander;Agard, David A.;Cheng, Yifan
通讯作者:
Cheng, Yifan
影响因子:
3
作者:
Pettersen, EF;Goddard, TD;Ferrin, TE
通讯作者:
Ferrin, TE