Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.
Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80% of the particles that an expert would select with less than 10% false positives.
Adiga
Umesh
U
Physical Biosciences Division, Lawrence Berkeley National Laboratory, 1, Cyclotron Road, Berkeley, CA 94720, USA. UPAdiga@lbl.gov
Baxter
William T
WT
Hall
Richard J
RJ
Rockel
Beate
B
Rath
Bimal K
BK
Frank
Joachim
J
Glaeser
Robert
R
D000465:Algorithms; D000626:Aminopeptidases; D020285:Cryoelectron Microscopy / Q000379:methods*; D004152:Dipeptidyl-Peptidases and Tripeptidyl-Peptidases; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D020407:Internet; D010316:Particle Size; D012270:Ribosomes / Q000648:ultrastructure; D012697:Serine Endopeptidases / Q000648:ultrastructure; D012984:Software; D012986:Software Validation
D000626:Aminopeptidases; D004152:Dipeptidyl-Peptidases and Tripeptidyl-Peptidases; C048001:tripeptidyl-peptidase 2; D012697:Serine Endopeptidases