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Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Background and Objective: Leptin-deficient obesity is associated with various systemic diseases including diabetes and low bone mass phenotype. However, the periodontal status of leptin-deficient obese individuals is still unclear. In this study, we aimed to analyze the periodontal status, alveolar bone phenotype, and oral microbiome status in leptin-deficient obese mice (ob/ob mice). Methods: This study used 12-week-old wild-type and ob/ob male mice. The alveolar bone phenotype and periodontal status in the maxilla were analyzed by micro-CT and histological analysis. Osteoclasts in alveolar bone were visualized by TRAP staining. Expressions of inflammatory markers (MMP-9, IL-1β, and TGF-β1) and osteoclastogenic markers (RANKL and OPG) in periodontium were analyzed by immunohistochemistry and RT-qPCR. The oral microbiome was analyzed by 16 S rDNA sequencing. Results: CEJ-ABC distance in maxillary molars (M1-M3) of ob/ob mice was significantly higher compared with that of wild-type. The alveolar bone BV/TV ratio was reduced in ob/ob mice compared with wild-type. Higher numbers of osteoclasts were observed in ob/ob mice alveolar bone adjacent to the molar root. Epithelial hyperplasia in gingiva and disordered periodontal ligaments was observed in ob/ob mice. RANKL/OPG expression ratio was increased in ob/ob mice compared with wild-type. Expressions of inflammatory markers MMP-9, IL-1β, and TGF-β1 were increased in ob/ob mice compared with wild-type. Oral microbiome analysis showed that beneficial bacteria Akkermansia and Ruminococcaceae_UCG_014 were more abundant in the wild-type mice while the inflammation-related Flavobacterium was more abundant in ob/ob mice. Conclusion: In conclusion, ob/ob mice showed higher expressions of inflammatory factors, increased alveolar bone loss, lower abundance of the beneficial bacteria, and higher abundance of inflammatory bacteria in the oral cavity, suggesting leptin-deficient obesity as a risk factor for periodontitis development in ob/ob mice.

MicroRNA-155 (miR155) is overexpressed in various inflammatory diseases and cancer, in which bone resorption and osteolysis are frequently observed. However, the role of miR155 on osteogenesis and bone mass phenotype is still unknown. Here, we report a low bone mass phenotype in the long bone of Mir155-Tg mice compared with wild-type mice. In contrast, Mir155-KO mice showed a high bone mass phenotype and protective effect against inflammation-induced bone loss. Mir155-KO mice showed robust bone regeneration in the ectopic and orthotopic model, but Mir155-Tg mice showed compromised bone regeneration compared with the wild-type mice. Similarly, the osteogenic differentiation potential of bone marrow stromal stem cells (BMSCs) from Mir155-KO mice was robust and Mir155-Tg was compromised compared with that of wild-type mice. Moreover, Mir155 knockdown in BMSCs from wild-type mice showed higher osteogenic differentiation potential, supporting the results from Mir155-KO mice. TargetScan analysis predicted sphingosine 1-phosphate receptor-1 (S1pr1) as a target gene of Mir155, which was further confirmed by luciferase assay and Mir155 knockdown. S1pr1 overexpression in BMSCs robustly promoted osteogenic differentiation without affecting cell viability and proliferation. Furthermore, osteoclastogenic differentiation of Mir155-Tg bone marrow-derived macrophages was inhibited compared with that of wild-type mice. Thus, Mir155 showed a catabolic effect on osteogenesis and bone mass phenotype via interaction with the S1pr1 gene, suggesting inhibition of Mir155 as a potential strategy for bone regeneration and bone defect healing.

This repository contains all HPRC Y1 assemblies evaluated in the paper titled:Scalable telomere-to-telomere assembly for diploid and polyploid genomes with double graph.

To overcome the limitations of the conventional artificial potential field (APF) method, which is commonly used for unmanned aerial vehicle (UAV) formation obstacle avoidance control. A novel UAV formation obstacle avoidance control method based on a light transmission model (LTM) and an improved APF method is proposed. First, inspired by the flight of bird flocks, we combine the LTM with an APF function to present an improved APF model which can help UAV find feasible free space to maneuver. From this, UAV can overcome the drawbacks of non-reachable and local minima under the action of LTM. Then, the obstacle avoidance strategy based on the fixed-wing UAV motion model is proposed, and the obstacle avoidance control algorithm for UAV formation is designed. Finally, simulation results show the effectiveness and superiority of the proposed method, which can result in a dramatic improvement in the performance of UAV formation to obstacle avoidance under the complex and non-deterministic environment.

This repository contains all plant assemblies evaluated in the paper titled:Scalable telomere-to-telomere assembly for diploid and polyploid genomes with double graph.

De novo assembly of metagenome samples is a common approach to the study of microbial communities. Current metagenome assemblers developed for short sequence reads or noisy long reads were not optimized for accurate long reads. We thus developed hifiasm-meta, a new metagenome assembler that exploits the high accuracy of recent data. Evaluated on seven empirical datasets, hifiasm-meta reconstructed tens to hundreds of complete circular bacterial genomes per dataset, consistently outperforming other metagenome assemblers.

Since its initial release in 2000, the human reference genome has covered only the euchromatic fraction of the genome, leaving important heterochromatic regions unfinished. Addressing the remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium presents a complete 3.055 billion–base pair sequence of a human genome, T2T-CHM13, that includes gapless assemblies for all chromosomes except Y, corrects errors in the prior references, and introduces nearly 200 million base pairs of sequence containing 1956 gene predictions, 99 of which are predicted to be protein coding. The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.

Routine haplotype-resolved genome assembly from single samples remains an unresolved problem. Here we describe an algorithm that combines PacBio HiFi reads and Hi-C chromatin interaction data to produce a haplotype-resolved assembly without the sequencing of parents. Applied to human and other vertebrate samples, our algorithm consistently outperforms existing single-sample assembly pipelines and generates assemblies of similar quality to the best pedigree-based assemblies.

This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

Motivation Oxford Nanopore Technologies sequencing devices support adaptive sequencing, in which undesired reads can be ejected from a pore in real time. This feature allows targeted sequencing aided by computational methods for mapping partial reads, rather than complex library preparation protocols. However, existing mapping methods either require a computationally expensive base-calling procedure before using aligners to map partial reads or work well only on small genomes. Results In this work, we present a new streaming method that can map nanopore raw signals for real-time selective sequencing. Rather than converting read signals to bases, we propose to convert reference genomes to signals and fully operate in the signal space. Our method features a new way to index reference genomes using k-d trees, a novel seed selection strategy and a seed chaining algorithm tailored toward the current signal characteristics. We implemented the method as a tool Sigmap. Then we evaluated it on both simulated and real data and compared it to the state-of-the-art nanopore raw signal mapper Uncalled. Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4× speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49% more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). Availability and implementation Sigmap code is accessible at https://github.com/haowenz/sigmap. Supplementary information Supplementary data are available at Bioinformatics online.

The Human Pangenome Reference Consortium (HPRC) presents a first draft human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals. These assemblies cover more than 99% of the expected sequence and are more than 99% accurate at the structural and base-pair levels. Based on alignments of the assemblies, we generated a draft pangenome that captures known variants and haplotypes, reveals novel alleles at structurally complex loci, and adds 119 million base pairs of euchromatic polymorphic sequence and 1,529 gene duplications relative to the existing reference, GRCh38. Roughly 90 million of the additional base pairs derive from structural variation. Using our draft pangenome to analyze short-read data reduces errors when discovering small variants by 34% and boosts the detected structural variants per haplotype by 104% compared to GRCh38-based workflows, and by 34% compared to using previous diversity sets of genome assemblies.

Transfer, particularly far transfer through analogic mapping and reasoning, is considered one of the mechanisms for creative problem solving. However, the cognitive paradigm of training for analogi...

Haplotype-resolved de novo assembly is the ultimate solution to the study of sequence variations in a genome. However, existing algorithms either collapse heterozygous alleles into one consensus copy or fail to cleanly separate the haplotypes to produce high-quality phased assemblies. Here we describe hifiasm, a de novo assembler that takes advantage of long high-fidelity sequence reads to faithfully represent the haplotype information in a phased assembly graph. Unlike other graph-based assemblers that only aim to maintain the contiguity of one haplotype, hifiasm strives to preserve the contiguity of all haplotypes. This feature enables the development of a graph trio binning algorithm that greatly advances over standard trio binning. On three human and five non-human datasets, including California redwood with a ~30-gigabase hexaploid genome, we show that hifiasm frequently delivers better assemblies than existing tools and consistently outperforms others on haplotype-resolved assembly.

To detect large-variance code clones (i.e. clones with many modifications) in large-scale code repositories is difficult because most current tools can only detect almost identical or very similar clones. It has an important impact on downstream software applications such as bug detection, code completion, software analysis, etc. Recently, CCAligner made an attempt to detect the code clones with insertions or deletions in one place, which were called large-gap clones . Our contribution is to develop a novel and effective detection approach of large-variance clones to more general cases for not only the concentrated code modifications but also the scattered code modifications. A detector named LVMapper is proposed, borrowing and changing the approach of sequencing alignment in bioinformatics which can find two similar sequences with more differences. The ability of LVMapper was tested on 8 open source projects datasets, and the results show that LVMapper detected more than 5 times of large-variance clones compared with other state-of-the-art tools including CCAligner. Furthermore, our new tool also presents comparable recall for general Type-1, Type-2 and Type-3 clones with precision of 88.5% on the widely used benchmarking dataset BigCloneBench.

This repository contains all non-human assemblies evaluated in the paper titled:Haplotype-resolved assembly of diploid genomes without parental data. Allassemblies were generated by Cheng et al.

This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper.

The problem of intelligent L2-L∞ consensus design for leader-followers multiagent systems (MASs) under switching topologies is investigated based on switched control theory and fuzzy deep Q learning. It is supposed that the communication topologies are time-varying, and the model of MASs under switching topologies is constructed based on switched systems. By employing linear transformation, the problem of consensus of MASs is converted into the issue of L2-L∞ control. The consensus protocol is composed of the dynamics-based protocol and learning-based protocol, where the robust control theory and deep Q learning are applied for the two parts to guarantee the prescribed performance and improve the transient performance. The multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to give the scheduling interval, which ensures stability and prescribed attenuation performance. The sufficient existing conditions of consensus protocol are given, and the solutions of the dynamics-based protocol are derived based on linear matrix inequalities (LMIs). Then, the online design of the learning-based protocol is formulated as a Markov decision process, where the fuzzy deep Q learning is utilized to compensate for the uncertainties and achieve optimal performance. The variation of the learning-based protocol is modeled as the external compensation on the dynamics-based protocol. Therefore, the convergence of the proposed protocol can be guaranteed by employing the nonfragile control theory. In the end, a numerical example is given to validate the effectiveness and superiority of the proposed method.

The problem of intelligent

This paper is concerned with the issue of weighted ${\mathcal{H}_\infty }$ consensus for discrete-time multi-agent systems with time-varying delay and switching topologies. The objective is designing a dynamic output feedback protocol in the presence of external perturbations such that the system achieves consensus in mean square and has a prescribed ${\mathcal{H}_\infty }$ performance level. Firstly, the ${\mathcal{H}_\infty }$ consensus design is converted to a standard ${\mathcal{H}_\infty }$ control problem by reconstructing the closed-loop system as a reduced-order system. Then, by means of the multiple Lyapunov-Krasovskii functional method, the delay-dependent bounded real lemma is derived to guarantee the stability of closed-loop system. Based on these, sufficient conditions for the existence of the required dynamic output feedback protocol is developed in the form of a series of solvable linear matrix inequalities. Finally, numerical example and simulation results are given to illustrate the effectiveness of the obtained results.

The problem of H ∞ consesus control for asynchronously switched continuous-time multi-agent systems is investigated in this paper. There always exists asynchronous switching due to time delay and packet losses of network transmission which means that the switching of controllers will lag behind the switching of system modes, which will lead to performance degradation. First, the model of switched multi-agent systems is established. The H ∞ consensus issue can be converted as a H ∞ control problem via system reduction. Considering asynchronous switching phenomenon, the mean-square asymptotic stability is analyzed by the aid of multiple Lyapunov functional method and average dwell time (ADT) method. The sufficient conditions to ensure the closed-loop stable with prescribed performance are given in the form of linear matrix inequalities (LMIs). In the end , an example is given to verify the feasibility of the proposed approach.

The repetitive nature and complexity of multiple medically important genes make them intractable to accurate analysis, despite the maturity of short-read sequencing, resulting in a gap in clinical applications of genome sequencing. The Genome in a Bottle Consortium has provided benchmark variant sets, but these excluded some medically relevant genes due to their repetitiveness or polymorphic complexity. In this study, we characterize 273 of these 395 challenging autosomal genes that have multiple implications for medical sequencing. This extended, curated benchmark reports over 17,000 SNVs, 3,600 INDELs, and 200 SVs each for GRCh37 and GRCh38 across HG002. We show that false duplications in either GRCh37 or GRCh38 result in reference-specific, missed variants for short- and long-read technologies in medically important genes including CBS, CRYAA, and KCNE1. Our proposed solution improves variant recall in these genes from 8% to 100%. This benchmark will significantly improve the comprehensive characterization of these medically relevant genes and guide new method development.