Search for: All records

ABSTRACT The acoel wormHofstenia miamia (H. miamia)has recently emerged as a model organism for studying whole-body regeneration and embryonic development. Previous studies suggest that post-transcriptional mechanisms likely play important roles in whole-body regeneration. Here, we establish a resource for studyingH. miamiamicroRNA-mediated gene regulation, a major aspect of post-transcriptional control in animals. Using small RNA-sequencing samples spanning key developmental stages, we annotatedH. miamiamicroRNAs. Our analysis uncovered a total of 1,050 microRNA loci, including 479 high-confidence loci based on structural and read abundance criteria. Comparison of microRNA seed sequences with those in other bilaterian species revealed thatH. miamiaencodes the majority of known conserved bilaterian microRNA families and that several microRNA families previously reported only in protostomes or deuterostomes likely have ancient bilaterian origins. We profiled the expression dynamics of theH. miamiamiRNAs across embryonic and post-embryonic development. We observed that thelet-7andmir-125microRNAs are unconventionally enriched at early embryonic stages. To generate hypotheses for miRNA function, we annotated the 3’ UTRs ofH. miamiaprotein-coding genes and performed miRNA target site predictions. Focusing on genes that are known to function in the wound response, posterior patterning, and neural differentiation inH. miamia, we found that these processes may be under substantial miRNA regulation. Notably, we found that miRNAs in MIR-7 and MIR-9 families which have target sites in the posterior genesfz-1,wnt-3, andsp5are indeed expressed in the anterior of the animal, consistent with a repressive effect on their corresponding target genes. Our annotation offers candidate miRNAs for further functional investigation, providing a resource for future studies of post-transcriptional control during development and regeneration. 

Abstract Background Cryo-EM data generated by electron tomography (ET) contains images for individual protein particles in different orientations and tilted angles. Individual cryo-EM particles can be aligned to reconstruct a 3D density map of a protein structure. However, low contrast and high noise in particle images make it challenging to build 3D density maps at intermediate to high resolution (1–3 Å). To overcome this problem, we propose a fully automated cryo-EM 3D density map reconstruction approach based on deep learning particle picking. Results A perfect 2D particle mask is fully automatically generated for every single particle. Then, it uses a computer vision image alignment algorithm (image registration) to fully automatically align the particle masks. It calculates the difference of the particle image orientation angles to align the original particle image. Finally, it reconstructs a localized 3D density map between every two single-particle images that have the largest number of corresponding features. The localized 3D density maps are then averaged to reconstruct a final 3D density map. The constructed 3D density map results illustrate the potential to determine the structures of the molecules using a few samples of good particles. Also, using the localized particle samples (with no background) to generate the localized 3D density maps can improve the process of the resolution evaluation in experimental maps of cryo-EM. Tested on two widely used datasets, Auto3DCryoMap is able to reconstruct good 3D density maps using only a few thousand protein particle images, which is much smaller than hundreds of thousands of particles required by the existing methods. Conclusions We design a fully automated approach for cryo-EM 3D density maps reconstruction (Auto3DCryoMap). Instead of increasing the signal-to-noise ratio by using 2D class averaging, our approach uses 2D particle masks to produce locally aligned particle images. Auto3DCryoMap is able to accurately align structural particle shapes. Also, it is able to construct a decent 3D density map from only a few thousand aligned particle images while the existing tools require hundreds of thousands of particle images. Finally, by using the pre-processed particle images,Auto3DCryoMap reconstructs a better 3D density map than using the original particle images. 

Abstract Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.