Dataset Information

Cryo electron micrographs of RNA polymerase II transcribing a nucleosome

ABSTRACT:

SUBMITTER: Haruhiko Ehara

PROVIDER: EMPIAR-10244 | biostudies-other |

REPOSITORIES: biostudies-other

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Publications

Structural basis of the nucleosome transition during RNA polymerase II passage.

Kujirai Tomoya T Ehara Haruhiko H Fujino Yuka Y Shirouzu Mikako M Sekine Shun-Ichi SI Kurumizaka Hitoshi H

Science (New York, N.Y.) 20181004 6414

Genomic DNA forms chromatin, in which the nucleosome is the repeating unit. The mechanism by which RNA polymerase II (RNAPII) transcribes the nucleosomal DNA remains unclear. Here we report the cryo-electron microscopy structures of RNAPII-nucleosome complexes in which RNAPII pauses at the superhelical locations SHL(-6), SHL(-5), SHL(-2), and SHL(-1) of the nucleosome. RNAPII pauses at the major histone-DNA contact sites, and the nucleosome interactions with the RNAPII subunits stabilize the pau ...[more]

PMID: 30287617

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Project description:BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.

Dataset Information

Cryo electron micrographs of RNA polymerase II transcribing a nucleosome

Publications

Structural basis of the nucleosome transition during RNA polymerase II passage.

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