AtCircDB: a tissue-specific database for Arabidopsis circular RNAs.

Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA-circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA.

Single-layer perceptron artificial visual system for orientation detection.

Orientation detection is an essential function of the visual system. In our previous works, we have proposed a new orientation detection mechanism based on local orientation-selective neurons. We assume that there are neurons solely responsible for orientation detection, with each neuron dedicated to detecting a specific local orientation. The global orientation is inferred from the local orientation information. Based on this mechanism, we propose an artificial visual system (AVS) by utilizing a single-layer of McCulloch-Pitts neurons to realize these local orientation-sensitive neurons and a layer of sum pooling to realize global orientation detection neurons. We demonstrate that such a single-layer perceptron artificial visual system (AVS) is capable of detecting global orientation by identifying the orientation with the largest number of activated orientation-selective neurons as the global orientation. To evaluate the effectiveness of this single-layer perceptron AVS, we perform computer simulations. The results show that the AVS works perfectly for global orientation detection, aligning with the majority of physiological experiments and models. Moreover, we compare the performance of the single-layer perceptron AVS with that of a traditional convolutional neural network (CNN) on orientation detection tasks. We find that the single-layer perceptron AVS outperforms CNN in various aspects, including identification accuracy, noise resistance, computational and learning cost, hardware implementation feasibility, and biological plausibility.

ROKU: a novel method for identification of tissue-specific genes.

BACKGROUND: One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. RESULTS: We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. CONCLUSION: ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Integrative analysis of Arabidopsis thaliana transcriptomics reveals intuitive splicing mechanism for circular RNA.

A new regulatory class of small endogenous RNAs called circular RNAs (circRNAs) has been described as miRNA sponges in animals. Using 16 Arabidopsis thaliana RNA-Seq data sets, we identified 803 circRNAs in RNase R-/non-RNase R-treated samples. The results revealed the following features: Canonical and noncanonical splicing can generate circRNAs; chloroplasts are a hotspot for circRNA generation; furthermore, limited complementary sequences exist not only in introns, but also in the sequences flanking splice sites. The latter finding suggests that multiple combinations between complementary sequences may facilitate the formation of the circular structure. Our results contribute to a better understanding of this novel class of plant circRNAs.