AuTom-dualx: a toolkit for fully automatic ﬁducial marker-based alignment of dual-axis tilt series with simultaneous reconstruction
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Embargo End Date2019-07-13
Permanent link to this recordhttp://hdl.handle.net/10754/630747
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AbstractDual-axis electron tomography is an important 3D macro-molecular structure reconstruction technology, which can reduce artifacts and suppress the effect of missing wedge. However, the fully automatic data process for dual-axis electron tomography still remains a challenge due to three difﬁculties: (i) how to track the mass of ﬁducial markers automatically; (ii) how to integrate the information from the two different tilt series; and (iii) how to cope with the inconsistency between the two different tilt series.Here we develop a toolkit for fully automatic alignment of dual-axis electron tomography, with a simultaneous reconstruction procedure. The proposed toolkit and its workﬂow carries out the following solutions: (i) fully automatic detection and tracking of ﬁducial markers under large-ﬁeld datasets; (ii) automatic combination of two different tilt series and global calibration of projection parameters; and (iii) inconsistency correction based on distortion correction parameters and the consequently simultaneous reconstruction. With all of these features, the presented toolkit can achieve accurate alignment and reconstruction simultaneously and conveniently under a single global coordinate system.The toolkit AuTom-dualx (alignment module dualxmauto and reconstruction module volrec mltm) are accessible for general application at http://ear.ict.ac.cn, and the key source code is freely available under request.
CitationHan R, Wan X, Li L, Lawrence A, Yang P, et al. (2018) AuTom-dualx: a toolkit for fully automatic fiducial marker-based alignment of dual-axis tilt series with simultaneous reconstruction. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty620.
PublisherOxford University Press (OUP)
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License , which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.