Reconstructing the full-length sequence of extrachromosomal circular DNA (eccDNA) from short sequencing reads has proved challenging given the similarity of eccDNAs and their corresponding linear DNAs. Previous sequencing methods were unable to achieve high-throughput detection of full-length eccDNAs. Herein, a novel algorithm was developed, called Full-Length eccDNA Detection (FLED), to reconstruct the sequence of eccDNAs based on the strategy that combined rolling circle amplification and nanopore long-reads sequencing technology. Seven human epithelial and cancer cell line samples were analyzed by FLED and over 5000 full-length eccDNAs were identified per sample. The structures of identified eccDNAs were validated by both Polymerase Chain Reaction (PCR) and Sanger sequencing. Compared to other published nanopore-based eccDNA detectors, FLED exhibited higher sensitivity. In cancer cell lines, the genes overlapped with eccDNA regions were enriched in cancer-related pathways and cis-regulatory elements can be predicted in the upstream or downstream of intact genes on eccDNA molecules, and the expressions of these cancer-related genes were dysregulated in tumor cell lines, indicating the regulatory potency of eccDNAs in biological processes. The proposed method takes advantage of nanopore long reads and enables unbiased reconstruction of full-length eccDNA sequences. FLED is implemented using Python3 which is freely available on GitHub (https://github.com/FuyuLi/FLED).

Object grasping in cluttered scenes is a widely investigated field of robot manipulation. Most of the current works focus on estimating grasp pose from point clouds based on an efficient single-shot grasp detection network. However, due to the lack of geometry awareness of the local grasping area, it may cause severe collisions and unstable grasp configurations. In this paper, we propose a two-stage grasp pose refinement network which detects grasps globally while fine-tuning low-quality grasps and filtering noisy grasps locally. Furthermore, we extend the 6-DoF grasp with an extra dimension as grasp width which is critical for collisionless grasping in cluttered scenes. It takes a single-view point cloud as input and predicts dense and precise grasp configurations. To enhance the generalization ability, we build a synthetic single-object grasp dataset including 150 commodities of various shapes, and a complex multi-object cluttered scene dataset including 100k point clouds with robust, dense grasp poses and mask annotations. Experiments conducted on Yumi IRB-1400 Robot demonstrate that the model trained on our dataset performs well in real environments and outperforms previous methods by a large margin.

SUMMARY: Currently, most computational methods estimate the expression of circular RNAs (circRNAs) using the number of sequencing reads that support back-splicing junctions (BSJ) in RNA-seq data, which may introduce biased estimation of circRNA expression due to the uneven distribution of sequencing reads. To overcome this, we previously developed a model-based strategy for circRNA quantification, enabling consideration of sequencing reads from the entire transcript. Yet, the lack of exact transcript structure of circRNAs may limit its accuracy. Here, we proposed a substantially improved circRNA quantification tool, AQUARIUM, by introducing the full-length RNA structure of circular isoforms. We assessed its performance in circRNA quantification using both biological and simulated rRNA-depleted RNA-seq datasets, and demonstrated its superior performance at both BSJ and isoform level. AVAILABILITY AND IMPLEMENTATION: AQUARIUM is freely available at https://github.com/wanjun-group-seu/AQUARIUM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.