Accurate Assembly Research Articles

Benchmarking of Hi-C tools for scaffolding plant genomes obtained from PacBio HiFi and ONT reads

The implementation of Hi-C reads in the de novo genome assembly process allows the ordering of large regions of the genome in scaffolds and the generation of chromosome-level assemblies. Several bioinformatics tools have been developed for genome scaffolding with Hi-C, and each tool has advantages and disadvantages that need to be carefully evaluated before their adoption. We generated two de novo assemblies of Arabidopsis thaliana obtained from the same raw PacBio HiFi and Oxford Nanopore Technologies data. We scaffolded the assemblies implementing Hi-C reads with the scaffolders 3D-DNA, SALSA2, and YaHS, with the aim of identifying the tool providing the most accurate assembly. The scaffolded assemblies were evaluated according to contiguity, completeness, accuracy, and structural correctness. In our analysis, YaHS proved to be the best-performing bioinformatics tool for scaffolding de novo genome assemblies in Arabidopsis thaliana.

Simple, reference-independent assessment to empirically guide correction and polishing of hybrid microbial community metagenomic assembly.

Hybrid metagenomic assembly of microbial communities, leveraging both long- and short-read sequencing technologies, is becoming an increasingly accessible approach, yet its widespread application faces several challenges. High-quality references may not be available for assembly accuracy comparisons common for benchmarking, and certain aspects of hybrid assembly may benefit from dataset-dependent, empiric guidance rather than the application of a uniform approach. In this study, several simple, reference-free characteristics-particularly coding gene content and read recruitment profiles-were hypothesized to be reliable indicators of assembly quality improvement during iterative error-fixing processes. These characteristics were compared to reference-dependent genome- and gene-centric analyses common for microbial community metagenomic studies. Two laboratory-scale bioreactors were sequenced with short- and long-read platforms, and assembled with commonly used software packages. Following long read assembly, long read correction and short read polishing were iterated up to ten times to resolve errors. These iterative processes were shown to have a substantial effect on gene- and genome-centric community compositions. Simple, reference-free assembly characteristics, specifically changes in gene fragmentation and short read recruitment, were robustly correlated with advanced analyses common in published comparative studies, and therefore are suitable proxies for hybrid metagenome assembly quality to simplify the identification of the optimal number of correction and polishing iterations. As hybrid metagenomic sequencing approaches will likely remain relevant due to the low added cost of short-read sequencing for differential coverage binning or the ability to access lower abundance community members, it is imperative that users are equipped to estimate assembly quality prior to downstream analyses.

Sequencing Strategy to Ensure Accurate Plasmid Assembly.

Despite the wide use of plasmids in research and clinical production, the need to verify plasmid sequences is a bottleneck that is too often underestimated in the manufacturing process. Although sequencing platforms continue to improve, the method and assembly pipeline chosen still influence the final plasmid assembly sequence. Furthermore, few dedicated tools exist for plasmid assembly, especially for de novo assembly. Here, we evaluated short-read, long-read, and hybrid (both short and long reads) de novo assembly pipelines across three replicates of a 24-plasmid library. Consistent with previous characterizations of each sequencing technology, short-read assemblies had issues resolving GC-rich regions, and long-read assemblies commonly had small insertions and deletions, especially in repetitive regions. The hybrid approach facilitated the most accurate, consistent assembly generation and identified mutations relative to the reference sequence. Although Sanger sequencing can be used to verify specific regions, some GC-rich and repetitive regions were difficult to resolve using any method, suggesting that easily sequenced genetic parts should be prioritized in the design of new genetic constructs.

Removal of sequencing adapter contamination improves microbial genome databases

Advances in assembling microbial genomes have led to growth of reference genome databases, which have been transformative for applied and basic microbiome research. Here we show that published microbial genome databases from humans, mice, cows, pigs, fish, honeybees, and marine environments contain significant sequencing-adapter contamination that systematically reduces assembly accuracy and contiguousness. By removing the adapter-contaminated ends of contiguous sequences and reassembling MGnify reference genomes, we improve the quality of assemblies in these databases.

A Virtual Assembly Technology for Virtual–Real Fusion Interaction of Ship Structure Based on Three-Level Collision Detection

With the rapid advancement of new-generation information technology, the virtual–real fusion interaction has increasingly become a crucial technique for structural analysis to determine the strength envelope of hulls. A high-precision assembly of the experimental devices in a virtual environment is vital. A virtual assembly method for a structural virtual–real fusion test based on the oriented bounding box (OBB) algorithm, the Devillers and Guigue algorithm, and differential triangle facets algorithm are proposed in this paper. The experiment of the connector in a typical offshore floating platform is performed as a case, which indicates that the virtual assembly method proposed in this paper enables the assessment of assembly virtually prior to the actual experiment, and the assembly accuracy can reach 0.01 mm. The digitalization and virtual–real fusion interaction for the mechanical experiment of hulls are advanced to ensure efficiency, safety, and economy.

Application of deep learning in active alignment leads to high-efficiency and accurate camera lens assembly

Application of deep learning in active alignment leads to high-efficiency and accurate camera lens assembly

A piezoelectric driven amphibious microrobot capable of fast and controllable movement

Abstract Due to its excellent adaptability to the environment and flexibility in narrow spaces, amphibious microrobots have become an important research direction recently. This study proposes an amphibious microrobot driven by piezoelectric actuators with a body length of 4.5 cm and a mass of 1.4 g. The microrobot consists of two active front legs, two passive rear legs, two caudal fins, and a support frame. Each front leg and each caudal fin are designed as structures integrated with their respective piezoelectric actuators. The microrobot has a tilted body, and the ground exerts an oblique upward impact force that makes it jump forward when its front legs swing backwards. The opposite swing of the two caudal fins generates propulsion for swimming. The components of the microrobot are manufactured based on the monolithic laminate process. The monolithic front actuator-leg and monolithic actuator-fin both emerge from a multi-layer material laminate. The support frame is designed and fabricated as a monolithic structure to improve assembly accuracy and reduce redundant assembly steps. The manufactured microrobot demonstrates its flexible and fast amphibious movements. Its maximum land walking speed reaches 15.3 cm/s and its turning speed reaches 48.2 degrees per second. The microrobot has a maximum payload capacity of 5 g moving on land. When the front legs and caudal fins work simultaneously, its underwater swimming speed reaches 9.1 cm/s, and the maximum turning speed is 20.5 degrees per second. The microrobot also confirms a maximum payload of 3 g during its underwater movement.

Position and orientation estimation method based on 3D digital morphology contour registration

Abstract Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved iterative closest point method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.

Modeling of CNC machine tool line rail assembly accuracy and its control method

Aiming at the linear guide assembly process, due to the preload load caused by the assembly deformation mechanism is not clear resulting in the guide assembly straightness is difficult to predict the problem. In this paper, based on the establishment of linear guide assembly deformation model, the ideal and actual conditions of the guide assembly deformation is discussed. Secondly, considering the influence of manufacturing error of the guide rail mounting surface on the guide rail assembly accuracy, it puts forward the integrated assembly deformation and manufacturing error of the guide rail assembly straightness error prediction model, and puts forward the control method of the guide rail assembly accuracy based on the assembly deformation and the inverse repair manufacturing error. Finally, the experimental bench is built and the accuracy of the theoretical model and accuracy control method is verified through experiments , and the results show that the straightness error of the guideway assembly is reduced by .

Graph-based self-supervised learning for repeat detection in metagenomic assembly.

Repetitive DNA (repeats) poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, in which genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from metagenomic communities. Detecting repeats is a crucial first step in overcoming these challenges. To address this issue, we propose GraSSRep, a novel approach that leverages the assembly graph's structure through graph neural networks (GNNs) within a self-supervised learning framework to classify DNA sequences into repetitive and nonrepetitive categories. Specifically, we frame this problem as a node classification task within a metagenomic assembly graph. In a self-supervised fashion, we rely on a high-precision (but low-recall) heuristic to generate pseudolabels for a small proportion of the nodes. We then use those pseudolabels to train a GNN embedding and a random forest classifier to propagate the labels to the remaining nodes. In this way, GraSSRep combines sequencing features with predefined and learned graph features to achieve state-of-the-art performance in repeat detection. We evaluate our method using simulated and synthetic metagenomic data sets. The results on the simulated data highlight GraSSRep's robustness to repeat attributes, demonstrating its effectiveness in handling the complexity of repeated sequences. Additionally, experiments with synthetic metagenomic data sets reveal that incorporating the graph structure and the GNN enhances the detection performance. Finally, in comparative analyses, GraSSRep outperforms existing repeat detection tools with respect to precision and recall.

Sequencing and phylogenetic analysis of the complete mitochondrial genome of the Eurasian Harvest Mouse (Micromys minutus) Pallas 1771 from China

This study aimed to examine the complete mitochondrial genome sequence of the Eurasian Harvest Mouse (Micromys minutus) through polymerase chain reaction. The mitochondrial genome of M. minutus was characterized as a circular, double-stranded DNA molecule, encompassing a total length of 16,239 bp. The mitochondrial genome composition of M. minutus was found to include 13 protein-coding genes, a single control region, 22 tRNA genes, 2 rRNA genes, and a origin of L-strand replication. The accurate identification, sequencing, and assembly of the M. minutus mitochondrial genome were successfully conducted in this study, providing a resource for the scientific community to accurately attribute the mitochondrial genome of this species.

A radar array expansion design method based on modular subarray layout

Abstract This paper introduces a design method for expandable subarray structures used in phased array radar arrays. Based on the structural characteristics of modular subarray layout for large radar arrays, this paper analyzes the assembly dimension chain tolerance based on subarray expansion. One third of the allowable deviation of the radar array is used as the subarray design tolerance, and the size tolerance of each installation surface of the subarray is designed according to the design tolerance. Finally, the subarray array is assembled into any size of radar array, and the probability of the radar array assembly accuracy meeting the allowable deviation is 99.73%. For large radar arrays, adopting this design method can significantly reduce the tolerance span between the sub array and the area array, greatly reducing the difficulty of design and processing. This paper provides a fundamental design method for the expansion design of radar arrays with similar systems, which has universal applicability.

Highly accurate assembly polishing with DeepPolisher.

Accurate genome assemblies are essential for biological research, but even the highest quality assemblies retain errors caused by the technologies used to construct them. Base-level errors are typically fixed with an additional polishing step that uses reads aligned to the draft assembly to identify necessary edits. However, current methods struggle to find a balance between over-and under-polishing. Here, we present an encoder-only transformer model for assembly polishing called DeepPolisher, which predicts corrections to the underlying sequence using Pacbio HiFi read alignments to a diploid assembly. Our pipeline introduces a method, PHARAOH (Phasing Reads in Areas Of Homozygosity), which uses ultra-long ONT data to ensure alignments are accurately phased and to correctly introduce heterozygous edits in falsely homozygous regions. We demonstrate that the DeepPolisher pipeline can reduce assembly errors by half, with a greater than 70% reduction in indel errors. We have applied our DeepPolisher-based pipeline to 180 assemblies from the next Human Pangenome Reference Consortium (HPRC) data release, producing an average predicted Quality Value (QV) improvement of 3.4 (54% error reduction) for the majority of the genome.

A deep learning-based method enables the automatic and accurate assembly of chromosome-level genomes.

The application of high-throughput chromosome conformation capture (Hi-C) technology enables the construction of chromosome-level assemblies. However, the correction of errors and the anchoring of sequences to chromosomes in the assembly remain significant challenges. In this study, we developed a deep learning-based method, AutoHiC, to address the challenges in chromosome-level genome assembly by enhancing contiguity and accuracy. Conventional Hi-C-aided scaffolding often requires manual refinement, but AutoHiC instead utilizes Hi-C data for automated workflows and iterative error correction. When trained on data from 300+species, AutoHiC demonstrated a robust average error detection accuracy exceeding 90%. The benchmarking results confirmed its significant impact on genome contiguity and error correction. The innovative approach and comprehensive results of AutoHiC constitute a breakthrough in automated error detection, promising more accurate genome assemblies for advancing genomics research.

Lightweight structure and unequal length flexible support design of a 1.3×1.2 m rectangular, horizontally supported mirror

A lightweight rectangular mirror designed for a space telescope features a lightweight structure and an innovative unequal length flexible support design. This design incorporates a three-point back support structure, which maintains the surface accuracy of the mirror assembly in a horizontal optical testing layout. The topology optimization design method is applied for the lightweight design of a SiC mirror. According to the principle that optimal gravity surface accuracy of the mirror is achieved when the pivot center of the flexible support coincides with the neutral plane of the mirror, an unequal length flexible support scheme is proposed. Furthermore, a “neck-shrinking” flexible support structure is designed to enhance the comprehensive surface quality of the mirror assembly, achieving better than λ/140 (RMS = 4.5 nm, λ=632.8nm). Following the completion of mirror polishing, an optical test system is established. The test results confirm that the surface shape accuracy satisfies the requirements of the design index. In addition, the mechanical design has been corroborated through dynamic testing.

Progressive bearing failure analysis and strength prediction method for the initial assembly and tensile process of composite bolted joints

Composite bolted joints have extensive application in aerospace and marine equipment. Failure analysis and strength prediction are crucial steps in the design of composite bolted joints. This study proposes a method that can accurately analyze the progressive bearing failure and static strength of composite bolted joints under different initial assembly parameters. This method first obtains an accurate initial assembly state based on the high-fidelity finite element model with detailed thread structure. Then it uses this as an input to analyze the progressive bearing failure process and the bearing strength value. Using the above method, the formation and evolution mechanism of the tightening yielding phenomenon during the assembly process are analyzed. And the influence of the two key assembly parameters, washer type and initial bolt tension, on the bearing strength of composite bolted joints is analyzed. The experimental results verify the effectiveness and accuracy of the simulation methods and results.

A stacking sequence optimization strategy for process‐induced deformation of multi‐layer thick asymmetric laminates

AbstractThe occurrence of process‐induced deformations of composites laminates is challenging for assembly accuracy and may lead to a service life reduction of parts. However, it can be obviously mitigated through different optimization strategies on the basis of the accurate curing process simulation. In this study, a stacking sequence optimization strategy is proposed and applied to multi‐layer thick asymmetric laminates. The shapes of deformed laminate plates are experimentally investigated in virtue of the three‐dimensional coordinate measuring machine. The thermo‐chemical–mechanical behaviors of plates are first verified through the comparisons of model predicted and experimental process‐induced deformations. Then the nonlinear control formula achieved through the regression model is proposed for the direct relationship between stacking sequences and process‐induced deformations. Finally, the required solutions are generated by solving the control formula. With the comparisons between the average deformations before and after optimizations, it is found that the magnitudes of deformations are significantly reduced, especially when the unoptimizated deformations are large.Highlights The thermo‐chemical–mechanical behavior can be described in the curing process simulation model. The deformed shapes of multi‐layer thick asymmetric laminate plates are measured by a three‐dimensional coordinate measuring machine. The stacking sequence optimization strategy is implemented by introducing a nonlinear control formula.

Surface roughness prediction model and surface topography analysis of 2.5D-Cf/SiC in two-dimensional ultrasonic assisted grinding based on GA-BP neural network

Because of the anisotropy and non-uniformity, the grinding surface quality of Cf/SiC composites is difficult to be accurately predicted. The quality of the surface directly determines the assembly accuracy. To better predict the surface quality of 2.5D-Cf/SiC composites, the BP neural network was optimized by genetic algorithm (GA), and the surface roughness prediction model of fiber orientation, ultrasonic amplitude, and machining parameters was established. The empirical formula prediction model of surface roughness was established by the method of multiple linear regression analysis. The results show that the prediction accuracy of GA optimized BP neural network is the best, followed by the empirical formula, and the prediction effect of the BP neural network is the worst. The average absolute percentage error of these three models is 10.045%, 16.912% and 26.6245% respectively. The kinematic models of conventional and two-dimensional ultrasonic-assisted grinding of single particles were established respectively. Based on the kinematic model, the reasons for the reduction of surface roughness by 2D ultrasonic-assisted grinding are explained. According to the orthogonal test results, the surface roughness decreases the most when vs is 0.66m/s, vw is 100mm/min, ap is 150μm along the fiber orientation of 0°, and the maximum percentage of reduction is 53.18%. Increasing the linear speed, reducing the feed speed, reducing the grinding depth, and applying ultrasound can reduce the extrusion pressure along the axis of 0° fiber, and then reduce the length and depth of cracks and thus reduce the surface defects. The axial shear force of the 90° oriented abrasive particles on the fiber is reduced, thus reducing the surface damage caused by torsional deformation. Reduce 3D surface profile and improve surface quality. The maximum percentage reduction of each parameter index is as follows: root mean square height decreased by 28.8%, skew decreased by 88.61%, kurtosis decreased by 48.97%, maximum crest height decreased by 48.55%, maximum sag height decreased by 40.09%, maximum height decreased by 43.93%, and arithmetic mean height decreased by 26.06%.

A Draft Pacific Ancestry Pangenome Reference.

Individuals of Pacific ancestry suffer some of the highest rates of health disparities yet remain vastly underrepresented in genomic research, including currently available linear and pangenome references. To begin addressing this, we developed the first Pacific ancestry pangenome reference using 23 individuals with diverse Pacific ancestry. We assembled 46 haploid genomes from these 23 individuals, resulting in highly accurate and contiguous genome assemblies with an average quality value of 55.0 and an average N50 of 40.7 Mb, marking the first de novo assembly of highly accurate Pacific ancestry genomes. We combined these assemblies to create a pangenome reference, which added 30.6 Mb of novel sequence missing from the Human Pangenome Reference Consortium (HPRC) reference. Mapping short reads to this pangenome reduced variant call errors and yielded more true-positive variants compared to the HPRC and T2T-CHM13 references. This Pacific ancestry pangenome reference serves as a resource to enhance genetic analyses for this underserved population.

A prediction method for the backlash error of robot precision reducers based on optimal assembly

Abstract The kinematic accuracy and service life of robot precision reducers are directly affected by their assembly accuracy, and the backlash error is one of the most important performance indicators for evaluating the assembly accuracy. A prediction method for the backlash error of robot precision reducers is proposed, which can quickly and accurately calculate the backlash error of the optimal assembly reducers. Based on the optimal assembly process, the sensitive relationship between the errors of the crucial components and the assembly accuracy of the reducer is analysed, and the crucial errors affecting the backlash error are identified. The cumulative backlash of the crucial error along the dimensional chain is clarified, and the corresponding relationship between the backlash and the backlash error is determined. On this basis, backlash prediction models are established for the involute planetary gear and cycloidal-pin gear transmission parts, respectively. The mapping relationship between dimensional errors and backlash is also derived. Furthermore, the backlash error of the reducer is obtained by calculating the backlash error caused by each error term of the high-speed stage and low-speed stage transmission parts. Finally, the effectiveness and correctness of the prediction method are verified by the optimal assembly of RV reducers and the measurement of the backlash error. The theory and method proposed in this paper can quickly and accurately predict the backlash error of the reducer for robots, effectively predict the assembly accuracy of the selective reducers, significantly improve the assembly efficiency of the reducers, and then provide theoretical guidance and technical support for enhancing the assembly quality and kinematic accuracy of robot reducers.