BackgroundMicroRNAs (miRNAs) are a group of short (~22 nt) non-coding RNAs that play important regulatory roles. MiRNA precursors (pre-miRNAs) are characterized by their hairpin structures. However, a large amount of similar hairpins can be folded in many genomes. Almost all current methods for computational prediction of miRNAs use comparative genomic approaches to identify putative pre-miRNAs from candidate hairpins. Ab initio method for distinguishing pre-miRNAs from sequence segments with pre-miRNA-like hairpin structures is lacking. Being able to classify real vs. pseudo pre-miRNAs is important both for understanding of the nature of miRNAs and for developing ab initio prediction methods that can discovery new miRNAs without known homology.ResultsA set of novel features of local contiguous structure-sequence information is proposed for distinguishing the hairpins of real pre-miRNAs and pseudo pre-miRNAs. Support vector machine (SVM) is applied on these features to classify real vs. pseudo pre-miRNAs, achieving about 90% accuracy on human data. Remarkably, the SVM classifier built on human data can correctly identify up to 90% of the pre-miRNAs from other species, including plants and virus, without utilizing any comparative genomics information.ConclusionThe local structure-sequence features reflect discriminative and conserved characteristics of miRNAs, and the successful ab initio classification of real and pseudo pre-miRNAs opens a new approach for discovering new miRNAs.

Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.

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CRISPR-Cas9 has emerged as a versatile genome-editing platform. However, due to the large size of the commonly used CRISPR-Cas9 system, its effective delivery has been a challenge and limits its utility for basic research and therapeutic applications. Herein, a multifunctional nucleus-targeting "core-shell" artificial virus (RRPHC) was constructed for the delivery of CRISPR-Cas9 system. The artificial virus could efficiently load with the CRISPR-Cas9 system, accelerate the endosomal escape, and promote the penetration into the nucleus without additional nuclear-localization signal, thus enabling targeted gene disruption. Notably, the artificial virus is more efficient than SuperFect, Lipofectamine 2000, and Lipofectamine 3000. When loaded with a CRISPR-Cas9 plasmid, it induced higher targeted gene disruption efficacy than that of Lipofectamine 3000. Furthermore, the artificial virus effectively targets the ovarian cancer via dual-receptor-mediated endocytosis and had minimum side effects. When loaded with the Cas9-hMTH1 system targeting MTH1 gene, RRPHC showed effective disruption of MTH1 in vivo. This strategy could be adapted for delivering CRISPR-Cas9 plasmid or other functional nucleic acids in vivo.

Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and category extremely imbalance. In this paper, we propose an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity. Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features. The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through pixel-wise feature interpolation to realize feature reconstruction and alignment. For more accurate rotation estimation, an approximate SkewIoU loss is proposed to solve the problem that the calculation of SkewIoU is not derivable. Experiments on three popular remote sensing public datasets DOTA, HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the effectiveness of our approach. The source code is available at https://github.com/Thinklab-SJTU/R3Det_Tensorflow and is also integrated in our open source rotation detection benchmark: https://github.com/yangxue0827/RotationDetection.