Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.

MOTIVATION: Two major challenges arise when employing next-generation sequencing methods to comprehensively identify microRNAs (miRNAs) in plants: (i) how to minimize the false-positive inheritable to computational predictions and (ii) how to minimize the computational time required for analyzing the miRNA transcriptome in plants with complex and large genomes. RESULTS: We updated miRDeep-P to miRDeep-P2 (miRDP2) by employing a new filtering strategy and overhauling the algorithm. miRDP2 has been tested against miRNA transcriptomes in plants with increasing genome sizes that included Arabidopsis, rice, tomato, maize and wheat. Compared with miRDeep-P and several other computational tools, miRDP2 processes next-generation sequencing data with superior speed. By incorporating newly updated plant miRNA annotation criteria and developing a new scoring system, the accuracy of miRDP2 outperformed other programs. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing the miRNA transcriptome in plants. AVAILABILITY AND IMPLEMENTATION: The miRDP2 are freely available from https://sourceforge.net/projects/mirdp2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The authors previously proposed an improved multiband‐structured subband adaptive filter (IMSAF) algorithm. In this contribution, they first present two delayless structures of the IMSAF algorithm to remove delay in the signal path. Then, they study the transient and steady‐state behaviour of the IMSAF algorithms based on the energy conservation arguments and paraunitary condition imposed on the analysis and synthesis filter banks. The analysis does not require a model for the input signal. Simulation results show that the proposed delayless IMSAF algorithm has a faster convergence rate than the traditional delayless subband adaptive filtering algorithms. Theoretical analysis of the IMSAF algorithm is in good agreement with computer simulation results.