Quality Of Maps Research Articles

A Transcriptomic Dataset of Liver Tissues from Global and Liver-Specific Bmal1 Knockout Mice

The circadian clock regulates various physiological processes in mammals. The core circadian clock gene Bmal1 is crucial for maintaining the oscillations of the circadian clock system by controlling the rhythmic expression of numerous circadian clock-controlled genes. To explore the transcriptional changes associated with Bmal1 deletion in liver tissues, we collected liver tissues from global and liver-specific Bmal1 knockout mice, along with their respective control groups, at two circadian time points (CT2 and CT14) and used them for transcriptome sequencing analysis. Genotyping, locomotor activity analysis, and comprehensive quality control analyses, including base quality scores, GC content, and mapping rates, confirmed the high quality of sequencing data. Differential expression analysis and RT-qPCR validation confirmed the reliability and validity of the dataset. These data offer a valuable resource for researchers investigating the role of BMAL1 in liver physiology, pathology, and the broader field of circadian biology.

Spatiotemporal Variability of Groundwater Quality for Irrigation: A Case Study in Mimoso Alluvial Valley, Semiarid Region of Brazil

Alluvial aquifers are vital for agricultural communities in semiarid regions, where groundwater quality is often constrained by seasonal and spatial salinity variations. This study employed geostatistical methods to analyze the spatial and temporal variability of electrical conductivity (EC) and the sodium adsorption ratio (SAR) and elaborate an indicative quality map in the Mimoso Alluvial Aquifer, Pernambuco, Brazil. Groundwater samples were collected and analyzed for cations, total hardness (TH), and the percentage of sodium (PS). Moreover, the relation between EC and the SAR was used to determine the groundwater quality for irrigation. Cation concentrations followed the order Ca2+ > Mg2+ > Na+ > K+. EC and the SAR exhibited medium to high variability, with spatial dependence ranging from moderate to strong, and presented a strong cross-spatial dependence. Results showed that sequential Gaussian simulation (SGS) provided a more reliable groundwater classification for agricultural purposes compared to kriging methods, enabling a more rigorous evaluation. Based on the strong geostatistical cross correlation between EC and RAS, a novel water quality index was proposed, properly identifying regions with lower groundwater quality. The resulting spatial indicator maps classified groundwater as suitable (64.7%), restricted use (2.08%) and unsuitable (2.38%) for irrigation. The groundwater quality maps indicated that groundwater was mostly suitable for agriculture, except in silty areas, also corresponding to regions with low hydraulic conductivity at the saturated zone. Soil texture, rainfall, and water extraction significantly influenced spatial and temporal patterns of groundwater quality. Such correlations allow a better understanding of the groundwater quality in alluvial valleys, being highly relevant for water resources management in semiarid areas.

Fast High-Resolution Metabolite Mapping in the rat Brain Using 1H-FID-MRSI at 14.1 T.

Magnetic resonance spectroscopic imaging (MRSI) enables the simultaneous noninvasive acquisition of MR spectra from multiple spatial locations inside the brain. Although 1H-MRSI is increasingly used in the human brain, it is not yet widely applied in the preclinical setting, mostly because of difficulties specifically related to very small nominal voxel size in the rat brain and low concentration of brain metabolites, resulting in low signal-to-noise ratio (SNR). In this context, we implemented a free induction decay 1H-MRSI sequence (1H-FID-MRSI) in the rat brain at 14.1 T. We combined the advantages of 1H-FID-MRSI with the ultra-high magnetic field to achieve higher SNR, coverage, and spatial resolution in the rat brain and developed a custom dedicated processing pipeline with a graphical user interface for Bruker 1H-FID-MRSI: MRS4Brain toolbox. LCModel fit, using the simulated metabolite basis set and invivo measured MM, provided reliable fits for the data at acquisition delays of 1.30 ms. The resulting Cramér-Rao lower bounds were sufficiently low (< 30%) for eight metabolites of interest (total creatine, N-acetylaspartate, N-acetylaspartate + N-acetylaspartylglutamate, total choline, glutamine, glutamate, myo-inositol, and taurine), leading to highly reproducible metabolic maps. Similar spectral quality and metabolic maps were obtained with one and two averages, with slightly better contrast and brain coverage due to increased SNR in the latter case. Furthermore, the obtained metabolic maps were accurate enough to confirm the previously known brain regional distribution of some metabolites. The acquisitions proved high reproducibility over time. We demonstrated that the increased SNR and spectral resolution at 14.1 T can be translated into high spatial resolution in 1H-FID-MRSI of the rat brain in 13 min using the sequence and processing pipeline described herein. High-resolution 1H-FID-MRSI at 14.1 T provided robust, reproducible, and high-quality metabolic mapping of brain metabolites with minimal technical limitations.

An approach to automating the construction of 3D girihs (3D Islamic geometric patterns) on NURBS surfaces

Islamic geometric patterns known as “girihs” are typically seen as 2D designs. However, in traditional Iranian architecture, girihs have been used to decorate curved and 3D surfaces. While there have been many studies on 2D girihs and methods for creating them, there has been less focus on 3D girihs. Additionally, there isn’t a comprehensive digital technique for constructing 3D girihs. This paper aims to fill this gap by proposing an automated approach for constructing girihs on curved surfaces to create 3D girihs. We use the UV coordinate relative to the NURBS (Non-Uniform Rational B-Spline) surfaces to identify the corresponding generative points of 2D girihs on 3D surfaces, then connect the points to construct 3D girihs. With this method, any geometric girih can be mapped onto a 3D surface, and under the right conditions, any 2D designs and motifs can also be mapped. We implement the proposed method in Grasshopper for Rhino 8 for Win, controlling the density and mapping quality of 3D girihs through parameters NMU and DL. By integrating this algorithm with a 2D girih generation algorithm, we provide the possibility of real-time customization and interactive construction of 3D girihs.

A copy number variation detection method based on OCSVM algorithm using multi strategies integration

Copy number variation (CNV) is an important part of human genetic variations, which is associated with various kinds of diseases. To tackle the limitations of traditional CNV detection methods, such as restricted detection types, high error rates, and challenges in precisely identifying the location of variant breakpoints, a new method called MSCNV (copy number variations detection method for multi-strategies integration based on a one-class support vector machine model) is proposed. MSCNV establishes a multi-signal channel that integrates three strategies: read depth, split read, and read pair. First, a one-class support vector machine algorithm is used to detect abnormal signals in read depth and mapping quality values to determine the rough CNV region. Then, the rough CNV region is filtered by using paired read signals to improve the precision of MSCNV method. Finally, MSCNV explores and recognizes tandem duplication regions, interspersed duplication regions, and loss regions. It uses split read signals to determine the precise location of mutation points and to determine the type of variation. Compared with Manta, FREEC, GROM-RD, Rsicnv, and CNVkit, MSCNV significantly improves the sensitivity, precision, F1-score, and overlap density score of CNV detection while reducing the boundary bias of the detection results.

Groundwater quality mapping based on the Wilcox Classification method for agricultural purposes: Qazvin Plain aquifer case

Water quality is an essential component in managing surface and groundwater resources and for various uses; it is considered a necessary principle in planning. This study aims to map the groundwater quality of the Qazvin Plain aquifer in Iran for agricultural use based on the Wilcox classification method. For this purpose, the parameters of electrical conductivity (EC) and sodium adsorption ratio (SAR) of wells in the years 2015-2018 have been used. For interpolation, the inverse distance weighting (IDW) method with optimal power and Kriging geostatistical techniques were used based on spherical, exponential, and Gaussian semicircle algorithms. Electrical conductivity and SAR maps were drawn in the GIS platform after selecting the best interpolation method due to minor errors. The Wilcox method was used to classify the water quality of the studied wells. In most cases, the IDW method with optimal power was selected as the superior interpolation method. During the study, the results obtained from the water quality maps showed that the level attributed to the "high salinity" water class increased from 25.98 to 36.44, and the level attributed to the "slightly salty" water class decreased from 12.54 to 3.14. Finally, the results showed that the quality of underground water for agricultural purpose in the Qazvin Plain aquifer became more unfavourable during the studied period

Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke

To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driven MRA) collateral map in acute ischemic stroke. We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. Two raters graded the collateral perfusion scores of both conventional and DL-driven MRA collateral maps and measured the grading time. They also qualitatively assessed the image quality of both collateral maps. Interrater and inter-method agreements for collateral perfusion grading between the two collateral maps were analyzed, along with a comparison of grading time and image quality. In the analysis of the 296 acute ischemic stroke patients, the inter-method agreement for collateral perfusion grading was almost perfect (κ = 0.96, 95% CI: 0.95–0.98). Compared to conventional MRA collateral maps, the time taken for collateral perfusion grading on DL-driven MRA collateral maps was shorter (P < 0.001 for rater 1 and P = 0.003 for rater 2), and the image quality of the DL-driven MRA collateral maps was superior (P < 0.001 for rater 1 and P = 0.002 for rater 2). The DL-driven MRA collateral map demonstrates clinical feasibility for collateral perfusion grading in acute ischemic stroke, with the added benefits of reduced generation and interpretation time, along with improved image quality of the MRA collateral map.

Texture mapping of warp knitted shoe upper based on ARAP parameterization method

Abstract In order to realize the design of the warp knitted upper based on the three-dimensional shoe model, this study investigates the texture mapping technique based on angle-preserving and area-regularizing as-rigid-as-possible (ARAP) parameterization. The ARAP algorithm, based on both partial and global considerations, is utilized to parameterize the 3D mesh. Moreover, detailed methods for calculating 3D vertex adjustments and selecting seam lines during the unfolding process are provided. Additionally, discrete harmonic and parametric optimization unfolding algorithms are employed to enhance texture mapping quality. To reduce mesh distortion, the results of discrete harmonic parameterization are used as inputs for ARAP parameterization, making the mapping effect more natural and reducing the number of required optimizations. Experimental results demonstrate that the proposed method maintains a balanced distribution of angles and areas while minimizing conformal distortions, thereby achieving superior texture mapping.

Recognition and quality mapping of traditional herbal drugs: way forward towards artificial intelligence

Recognition and quality mapping of traditional herbal drugs: way forward towards artificial intelligence

Mapping nutrient pollution in inland water bodies using multi-platform hyperspectral imagery and deep regression network.

Inland waters face multiple threats from human activities and natural factors, leading to frequent water quality issues, particularly the significant challenge of eutrophication. Hyperspectral remote sensing provides rich spectral information, enabling timely and accurate assessment of water quality status and trends. To address the challenge of inaccurate water quality mapping, we propose a novel deep learning framework for multi-parameter estimation from hyperspectral imagery. A deep convolutional spatial-spectral joint learning method incorporating high-dimensional attention-weighted differences is proposed to optimize the deep features. The model was used to accurately estimate the distribution of three key eutrophication-related water quality parameters: total nitrogen, total phosphorus, and ammonia nitrogen. Through scale analysis, ablation experiments, and model comparisons, the results demonstrate stable regression performance with the proposed model. Specifically, the coefficient of determination (R2) values are 0.8315, 0.8137, and 0.8245, the mean absolute error (MAE) values are 0.2035, 0.0056 and 0.0134, and the mean squared error (MSE) values are 0.0733, 0.00008 and 0.0003 for the three parameters in the test set, respectively. Compared to the traditional feature analysis and regression methods, the R² values are improved by approximately 30 %, while the MAE and MSE values are reduced by approximately 60 % and 80 %, respectively. The model was applied to airborne hyperspectral imagery for nutrient pollution mapping. To assess the model's generalizability, we applied the trained model to multi-temporal satellite hyperspectral imagery and validated against in situ monitoring data, where the proposed model demonstrated promising cross-platform and temporal transferability.

EVALUATION OF THE GROUNDWATER QUALITY IN GISHIRI VILLAGE – KATAMPE, ABUJA USING WATER QUALITY INDEX

Water quality is inherently linked with human health, poverty reduction, food security, livelihoods, preservation of ecosystems, economic growth, and social development of societies. This study evaluated the groundwater quality of Gishiri-Katampe, Abuja-Nigeria using statistical and geospatial techniques for water quality indexing. The study also used hydro-chemical parameters, geographical information, and statistical analysis to assess groundwater pollution potential; identify the most vulnerable areas, and generate a groundwater quality map. The Canadian Water Quality Index, the GIS mapping of the water quality of Gishiri indicates that the Water Quality Index is within the range of 76.87 to 92.53. Similarly, the WQI is predominantly good (62%), indicating a minor degree of threat. However, 38% of the area is occasionally threatened (fair) on the Canadian scale. However, some areas are occasionally threatened (fair) with the corresponding WQI of 28% within the study area. Moreover, out of the 11 water quality parameters analyzed, 6 parameters (dissolved oxygen DO, turbidity, chemical oxygen demand COD, NO3, Na, and biological oxygen demand BOD) were identified as significant parameters as indicated by the correlation and regression analysis. This suggested that they strongly influenced the variability of the water quality.

Improved deep learning-based IVIM parameter estimation via the use of more "realistic" simulated brain data.

Due to the low signal-to-noise ratio (SNR) and the limited number of b-values, precise parameter estimation of intravoxel incoherent motion (IVIM) imaging remains an open issue to date, especially for brain imaging where the relatively small difference between D and D* easily leads to outliers and obvious graininess in estimated results. To propose a synthetic data driven supervised learning method (SDD-IVIM) for improving precision and noise robustness in IVIM parameter estimation without relying on real-world data for neural network training. On account of the absence of standard IVIM parametric maps from real-world data, a novel model-based method for generating synthetic human brain IVIM data was introduced. Initially, the parameter values of synthetic IVIM parametric maps were sampled from the complex distributions composed of a series of simple and uniform distributions. Subsequently, these parametric maps were modulated with human brain texture to imitate brain tissue structure. Finally, they were used to generate synthetic human brain multi-b-value diffusion-weighted (DW) images based on the IVIM bi-exponential model. With the proposed data synthesis method, an ordinary U-Net with spatial smoothness was employed for IVIM parameter mapping within a supervised learning framework. The performance of SDD-IVIM was evaluated on both numerical phantom and 20 glioma patients. The estimated IVIM parametric maps were compared to those derived from five state-of-the-art methods. In numerical phantom experiments, SDD-IVIM method produces IVIM parametric maps with lower mean absolute error, lower mean bias, and higher structural similarity compared to the other five methods, especially when the SNR of DW images is low. In glioma patient experiments, SDD-IVIM method offers lower coefficient of variation and more reasonable contrast-to-noise ratio between tumor and contralateral normal appearing white matter than the other five methods. Our method owns superior performance in parametric map quality, parameter estimation precision, and lesion characterization in IVIM parameter estimation, with strong resistance to noise.

Free-Breathing Ungated Radial Simultaneous Multi-Slice Cardiac T1 Mapping.

Modified Look-Locker imaging (MOLLI) T1 mapping sequences are acquired during breath-holding and require ECG gating with consistent R-R intervals, which is problematic for patients with atrial fibrillation (AF). Consequently, there is a need for a free-breathing and ungated framework for cardiac T1 mapping. To develop and evaluate a free-breathing ungated radial simultaneous multi-slice (SMS) cardiac T1 mapping (FURST) framework. Retrospective, nonconsecutive cohort study. Twenty-four datasets from 17 canine and 7 human subjects (4 males, years; 3 females, years). Canines were from studies involving AF induction and ablation treatment. The human population included separate subjects with suspected microvascular disease, acute coronary syndrome with persistent AF, and transthyretin amyloidosis with persistent AF. The remaining human subjects were healthy volunteers. Pre- and post-contrast T1 mapping with the free-breathing and ungated SMS inversion recovery sequence with gradient echo readout and with conventional MOLLI sequences at 1.5 T and 3.0 T. MOLLI and FURST were acquired in all subjects, and American Heart Association (AHA) segmentation was used for segment-wise analysis. Pre-contrast T1, post-contrast T1, and ECV were analyzed using correlation and Bland-Altman plots in 13 canines and 7 human subjects. T1 difference box plots for repeated acquisitions in four canine subjects were used to assess reproducibility. The PIQUE image quality metric was used to evaluate the perceptual quality of T1 maps. Paired t-tests were used for all comparisons between FURST and MOLLI, with indicating statistical significance. There were no significant differences between FURST and MOLLI pre-contrast T1 reproducibility ( and , ), FURST and MOLLI ECV ( and , ), or FURST and MOLLI PIQUE scores ( and , ). The ECV mean difference was with . FURST had similar quality pre-contrast T1, post-contrast T1, and ECV maps and similar reproducibility compared to MOLLI. 3 TECHNICAL EFFICACY: 1.

Spatial Sequential Matching Enhanced Underwater Single-Photon Lidar Imaging Algorithm

Traditional LiDAR and air-medium-based single-photon LiDAR struggle to perform effectively in high-scattering environments. The laser beams are subject to severe medium absorption and multiple scattering phenomena in such conditions, greatly limiting the maximum operational range and imaging quality of the system. The high sensitivity and high temporal resolution of single-photon LiDAR enable high-resolution depth information acquisition under limited illumination power, making it highly suitable for operation in environments with extremely poor visibility. In this study, we focus on the data distribution characteristics of active single-photon LiDAR operating underwater, without relying on time-consuming deep learning frameworks. By leveraging the differences in time-domain distribution between noise and echo signals, as well as the hidden spatial information among echo signals from different pixels, we rapidly obtain imaging results across various distances and attenuation coefficients. We have experimentally verified that the proposed spatial sequential matching enhanced (SSME) algorithm can effectively enhance the reconstruction quality of reflection intensity maps and depth maps in strong scattering underwater environments. Through additional experiments, we demonstrated the algorithm’s reconstruction effect on different geometric shapes and the system’s resolution at different distances. This rapidly implementable reconstruction algorithm provides a convenient way for researchers to preview data during underwater single-photon LiDAR studies.

A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging.

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.

Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera.

This paper presents a novel method to enhance ground truth disparity maps generated by Semi-Global Matching (SGM) using Maximum a Posteriori (MAP) estimation. SGM, while not producing visually appealing outputs like neural networks, offers high disparity accuracy in valid regions and avoids the generalization issues often encountered with neural network-based disparity estimation. However, SGM struggles with occlusions and textureless areas, leading to invalid disparity values. Our approach, though relatively simple, mitigates these issues by interpolating invalid pixels using surrounding disparity information and Bayesian inference, improving both the visual quality of disparity maps and their usability for training neural network-based commercial depth-sensing devices. Experimental results validate that our enhanced disparity maps preserve SGM's accuracy in valid regions while improving the overall performance of neural networks on both synthetic and real-world datasets. This method provides a robust framework for advanced stereoscopic camera systems, particularly in autonomous applications.

Non-target feature filtering for weakly supervised semantic segmentation

Weakly supervised semantic segmentation (WSSS) utilizes weak labels to learn semantic segmentation models, significantly reducing reliance on pixel-level annotations. WSSS typically employs a multi-label classification network to extract image features for constructing localization maps. The quality of the localization map critically influences the performance of WSSS. However, non-target semantic noise within the features impedes the improvement of localization map quality. To address this issue, we propose a non-target feature filtering class activation mapping (NFF-CAM) for WSSS, which can reduce non-target semantic signals and generate higher-quality localization maps. Specifically, the class-constrained dual cosine clustering (CDCC) and channel identification (CI) modules are introduced in NFF-CAM. CDCC effectively addresses the issue of unsuitability in the clustering group relationships of the original features under specified class conditions. CI can efficiently identify channel features containing non-target semantic information. We conduct extensive evaluations of NFF-CAM on popular datasets, including PASCAL VOC 2012 and MS COCO 2014. Experimental results show that NFF-CAM can effectively improve the segmentation performance of off-the-shelf methods.

Analytical Performance of a Novel Nanopore Sequencing for SARS-CoV-2 Genomic Surveillance.

The genomic analysis of SARS-CoV-2 has served as a crucial tool for generating invaluable data that fulfils both epidemiological and clinical necessities. Long-read sequencing technology (e.g., ONT) has been widely used, providing a real-time and faster response when necessitated. A novel nanopore-based long-read sequencing platform named QNome nanopore has been successfully used for bacterial genome sequencing and assembly; however, its performance in the SARS-CoV-2 genomic surveillance is still lacking. Synthetic SARS-CoV-2 controls and 120 nasopharyngeal swab (NPS) samples that tested positive by real-time polymerase chain reaction were sequenced on both QNome and MGI platforms in parallel. The analytical performance of QNome was compared to the short-read sequencing on MGI. For the synthetic SARS-CoV-2 controls, despite the increased error rates observed in QNome nanopore sequencing reads, accurate consensus-level sequence determination was achieved with an average mapping quality score of approximately 60 (i.e., a mapping accuracy of 99.9999%). For the NPS samples, the average genomic coverage was 89.35% on the QNome nanopore platform compared with 90.39% for MGI. In addition, fewer consensus genomes from QNome were determined to be good by Nextclade compare with MGI (p < 0.05). A total of 9004 mutations were identified using QNome sequencing, with substitutions being the most prevalent, in contrast, 10 997 mutations were detected on MGI (p < 0.05). Furthermore, 23 large deletions (i.e., deletions≥ 10 bp) were identified by QNome while 19/23 were supported by evidence from short-read sequencing. Phylogenetic analysis revealed that the Pango lineage of consensus genomes for SARS-CoV-2 sequenced by QNome concorded 83.04% with MGI. QNome nanopore sequencing, though challenged by read quality and accuracy compared to MGI, is overcoming these issues through bioinformatics and computational advances. The advantage of structure variation (SV) detection capabilities and real-time data analysis renders it a promising alternative nanopore platform for the surveillance of the SARS-CoV-2.

Methods for refining the depth map obtained from depth sensors

Depth maps are essential in applications such as robotics, augmented reality, autonomous vehicles, and medical imaging, providing critical spatial information. However, depth maps from sensors like time-of-flight (ToF) and structured light systems often suffer from low resolution, noise, and missing data. Addressing these challenges, this study presents an innovative method to refine depth maps by integrating high-resolution color images. The proposed approach employs both hard- and soft-decision pixel assignment strategies to adaptively enhance depth map quality. The hard-decision model simplifies edge classification, while the soft-decision model, integrated within a Markov Random Field framework, improves edge consistency and reduces noise. By analyzing discrepancies between edges in depth maps and color images, the method effectively mitigates artifacts such as texturecopying and blurred edges, ensuring better alignment between the datasets. Key innovations include the use of the Canny edge detection operator to identify and categorize edge inconsistencies and anisotropic affinity calculations for precise structural representation. The soft-decision model introduces advanced noise reduction techniques, improving depth map resolution and preserving edge details better than traditional methods. Experimental validation on Middlebury benchmark datasets demonstrates that the proposed method outperforms existing techniques in reducing Mean Absolute Difference values, especially in high-upscaling scenarios. Visual comparisons highlight its ability to suppress artifacts and enhance edge sharpness, confirming its effectiveness across various conditions. This approach holds significant potential for applications requiring high-quality depth maps, including robotics, augmented reality, autonomous systems, and medical imaging. By addressing critical limitations of current methods, the study offers a robust, versatile solution for depth map refinement, with opportunities for real-time optimization in dynamic environments.

Phasing nanopore genome assembly by integrating heterozygous variations and Hi-C data.

Haplotype-resolved genome assemblies serve as vital resources in various research domains, including genomics, medicine, and pangenomics. Algorithms employing Hi-C data to generate haplotype-resolved assemblies are particularly advantageous due to its ready availability. Existing methods primarily depend on mapping quality to filter out uninformative Hi-C alignments which may be susceptible to sequencing errors. Setting a high mapping quality threshold filters out numerous informative Hi-C alignments, whereas a low mapping quality threshold compromises the accuracy of Hi-C alignments. Maintaining high accuracy while retaining a maximum number of Hi-C alignments can be challenging. In our experiments, heterozygous variations play an important role in filtering uninformative Hi-C alignments. Here, we introduce Diphase, a novel phasing tool that harnesses heterozygous variations to accurately identify the informative Hi-C alignments for phasing and to extend primary/alternate assemblies. Diphase leverages mapping quality and heterozygous variations to filter uninformative Hi-C alignments, thereby enhancing the accuracy of phasing and the detection of switches. To validate its performance, we conducted a comparative analysis of Diphase, FALCON-Phase, and GFAse on various human datasets. The results demonstrate that Diphase achieves a longer phased block N50 and exhibits higher phasing accuracy while maintaining a lower hamming error rate. The source code of Diphase is available at https://github.com/zhangjuncsu/Diphase.