Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 397 records · Page 22Linked to original sources

bioRxiv2025

Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features

Type VI secretion system effectors (T6SEs) are crucial for bacterial pathogenicity, making their accurate identification essential for understanding bacterial virulence mechanisms. This study analyzed the differences in amino acid composition of N-terminal signal sequences between T6SEs and non-T6SEs, uncovering distinct positional amino acid preferences in T6SEs. Using a combination of unsupervised and supervised analysis, we evaluated feature encoding methods and developed T6CNN, an ensemble model that integrates N-terminal signal sequences, evolutionary information, and pre-trained protein language features for T6SE prediction. T6CNN demonstrated outstanding performance in independent testing, outperforming existing tools with a 7.9% accuracy increase (to 0.953), a 13.2% sensitivity improvement (to 0.964), and a 6.6% specificity enhancement (to 0.951). The T6CNN model offers a reliable and accurate solution for T6SE prediction, with significant potential to advance research on bacterial pathogenicity. ImportanceThis study introduces T6CNN, a new computational model for identifying Type VI secretion system effectors used by harmful bacteria. By analyzing early protein sequences, T6CNN uncovers unique features that reliably distinguish effector proteins. Integrating evolutionary data and pre-trained protein language features, the model outperforms existing methods in accuracy, sensitivity, and specificity. This enhanced prediction tool deepens our understanding of bacterial infection mechanisms and offers researchers a valuable resource for pinpointing key virulence factors. Ultimately, T6CNN may help drive the development of more targeted antibacterial treatments and strategies to combat bacterial diseases.

bioinformatics↗

bioRxiv2025

Identification of a neural basis for energy expenditure in the arcuate hypothalamus

Given the evolutionary instinct for caloric intake and the tendency for weight rebound after discontinuing dietary interventions or medications, increasing energy expenditure emerges as an alternative obesity treatment. However, neural regulation of energy expenditure remains poorly understood. Here, we report that a hypothalamic neuronal subtype, characterized by Crabp1 expression, establishes connections with multiple hypothalamic nuclei to regulate energy expenditure in mice. Inactivation of Crabp1 neurons reduces physical activity, body temperature, and adaptive thermogenesis, leading to an obese phenotype. Conversely, activation of these neurons increases energy expenditure and mitigates diet-induced obesity. Structural and functional analyses reveal that Crabp1 neurons promote energy metabolism through a "one-to-many" projection pattern. While Crabp1 neurons are rapidly activated by cold exposure and physical activity, prolonged light exposure abrogates their firing and may mediate light-induced metabolic disorder. Together, we reveal a neural basis that integrate various physiological and environmental stimuli to control energy expenditure and body weight.

neuroscience↗

bioRxiv2025

Automated Discovery of Therapeutic Biomaterial for Renally Impaired Hyperuricemia Patients by Natural Language Processing and Machine Learning

The exponential growth of scientific publications presents opportunities for researchers to identify valuable knowledge, especially in the highly interdisciplinary field --- biomaterials, where exploiting possible connections between unmet clinical needs and materials properties from literatures is crucial. However, with traditional literature reading, it is extremely challenging to marry unmet clinical needs with existing materials reported for different applications or other purposes. Here, to provide a not-renally cleared therapeutics for renally impaired hyperuricemia patients, we designed a multi-tiered framework MatWISE that fuses state-of-the-art natural language processing, semantic relationship mapping, and machine learning to automate the complex process of material discovery from a sea of scientific literatures published until December of 2022, and successfully identified and optimized {delta}-MnO2 into an orally administered, nonabsorbable uric acid (UA) lowering biomaterial. {delta}-MnO2 had superior serum and urine UA-lowering effect in three hyperuricemia mouse models, by comparing with a standard of care drug. {delta}-MnO2 is highly promising to serve as a safe and effective UA-lowering drug for renally impaired hyperuricemia patients. We demonstrated a new research paradigm for biomaterials that combining state-of-the-art machine learning techniques and a handful of experiments to discover a translationally relevant material from the massive existing research, for an unmet clinical need.

biochemistry↗

bioRxiv2025

Mutations and structural variants arising during double-strand break repair

Double-strand break (DSB) repair is highly mutagenic compared to normal replication. In budding yeast, repair of an HO endonuclease-induced DSB at MAT can be repaired by using a transcriptionally silent HMR::Kl-URA3 donor. During repair, -1 deletions in homonucleotide runs are strongly favored over +1 insertions, whereas during replication, spontaneous +1 and -1 events are equal. Microhomology-bounded, repair-associated intragenic deletions (IDs) are recovered 12 times more frequently than tandem duplications (TDs). IDs have a mean length of 56 bp, while TDs average 22 bp. These data suggest a picture of the structure of the repair replication fork: IDs and TDs occur within the open structure of a migrating D-loop, where the 3 end of a partly copied new DNA strand can dissociate and anneal with a single-stranded region of microhomology that lies either [~]80 bp ahead or [~]40 bp behind the 3 end. Another major class of repair-associated mutations ([~]10%) are interchromosomal template switches (ICTS), even though the K. lactis URA3 sequence in HMR is only 72% identical (homeologous) with S. cerevisiae ura3-52. ICTS events begin and end at regions of short ([~]7 bp) microhomology; however, ICTS events are constrained to the middle of the copied sequence. Whereas microhomology usage in intragenic deletions is not influenced by adjacent homeology, we show that extensive pairing of adjacent homeology plays a critical role in ICTS. Thus, although by convention, structural variants are characterized by the precise base pairs at their junction, microhomology-mediated template switching actually requires alignment of extensive adjacent homeology. Significance statementDNA synthesis during repair of a double-strand chromosome break by homologous recombination exhibits a high rate of mutation compared to normal replication. Using a genetic system in budding yeast, we isolated thousands of mutations occurring during repair. We conclude that the repair replication fork appears to have the two DNA strands open [~]80 bp ahead of the DNA polymerase, but the strands re-anneal rapidly behind the polymerase. Additionally, we analyzed interchromosomal template switching, in which the partially copied DNA strand dissociates and pairs with a new template at a short stretch of perfectly matching bases (microhomology), and resumes copying. We show that these apparent microhomology-mediated template switching events in fact require the pairing of [~]200 bp of imperfectly matching bases (homeology).

genetics↗

bioRxiv2025

Rapaprotin is Activated by an Endopeptidase to Disassemble 26S Proteasome

The 19S regulatory particle (RP) associates with the 20S core particle (CP) to form the 26S proteasome, an evolutionarily conserved holoenzyme that plays key roles in both physiological and pathological processes. Proteasome inhibitors that target the catalytic subunits within the 20S have proven to be valuable research tools and therapeutics for various cancers. Herein we report the discovery of rapaprotin, a 26S proteasome assembly inhibitor from our natural product-inspired hybrid macrocycle rapafucin library. Rapaprotin induces apoptosis in both myeloma and leukemia cell lines. Genome-wide CRISPR-Cas9 screen identified a cytosolic enzyme, prolyl endopeptidase (PREP) that is required for the pro-apoptotic activity of rapaprotin. Further mechanistic studies revealed that rapaprotin acts as a molecular transformer, changing from an inactive cyclic form into an active linear form, rapaprotin-L, upon PREP cleavage, to block 26S proteasome activity. Time-resolved cryogenic electron microscopy (cryo-EM) revealed that rapaprotin-L induces dissociation of the 19S RP from the 26S holoenzyme, which was verified in cells. Furthermore, rapaprotin exhibits a marked synergistic effect with FDA-approved proteasome inhibitors and resensitizes drug-resistant multiple myeloma cells from patients to bortezomib. Taken together, these results suggest that rapaprotin is a new chemical tool to probe the dynamics of the 26S proteasome assembly and a promising anticancer drug lead.

pharmacology and toxicology↗

bioRxiv2025

Housekeeping genes are enriched in rheumatoid arthritis-related genes

Housekeeping genes are often considered to be stably expressed in any tissue, cell, or disease state, and the relationship between housekeeping genes and rheumatoid arthritis (RA) has never been systematically analyzed. Therefore we aimed to explore whether housekeeping genes are abnormally expressed in RA by analyzing 15 datasets that were filtered from 140 gene expression profiles. By cumulative hypergeometric test, for 73.3% (11/15) of the datasets, housekeeping genes were significantly (p < 0.05) enriched in differentially expressed genes (DEGs) between RA patients and healthy controls. Using the relative enrichment factor (Re) as an effect size, a Meta-analysis strategy was adopted to integrate the individual results. The logarithmic Re estimation for multiple studies was 0.48 with the 95% confidence interval ranging from 0.17 to 0.80. The combined effect size was significantly higher than that of the randomized case (p = 0.0012) suggesting a consistent result with the hypergeometric test. The robustness of results was verified by sensitivity and subgroup analyses. Furthermore, the expression fluctuation of housekeeping genes was higher in RA patients than healthy controls and the correlation between DNA methylation and gene expression of housekeeping genes was significantly higher than that of non-housekeeping genes, which indicates the potential epigenetic regulation relationship between housekeeping genes and the pathogenesis of RA. This study identified for the first time a significant enrichment of housekeeping genes in DEGs of RA, which might provide new perspectives for understanding the potential role of housekeeping genes in RA.

bioinformatics↗

bioRxiv2025

LivecellX: A Deep-learning-based, Single-Cell Object-Oriented Framework for Quantitative Analysis in Live-Cell Imaging

Live-cell imaging uniquely captures single-cell dynamics in space and time, but robust analysis is limited by segmentation and tracking errors that accumulate across frames. We present LivecellX, a deep-learning-based pipeline that integrates instance-level segmentation error correction with trajectory refinement, leveraging temporal context to recover accurate cell tracks. LivecellX also introduces a benchmark dataset with detailed annotations of common error classes, providing a resource for method development and evaluation. Beyond error correction, the framework incorporates modules for classifying biological processes, reconstructing cell lineages, and analyzing dynamic behaviors. Users can interact with the system programmatically or through a Napari-based graphical interface, enabling flexible integration into diverse workflows. By coupling error-aware correction with comprehensive lineage and dynamics analysis, LivecellX establishes an open, extensible platform that advances the accuracy and scalability of live-cell imaging studies.

biophysics↗

bioRxiv2025

Effects of Inflammatory Factors on the Migration and Repair of Intestinal Mucosal Cells at Pancreaticojejunostomy Anastomosis: An Experimental Study in Beagle Dogs

ObjectiveTo investigate the effects of inflammatory factors on the migration and repair of intestinal mucosal cells at the site of pancreaticojejunostomy (PJ) anastomosis and to observe the safety and feasibility of PJ with mucosal-priority healing (PM-PJ). Materials and methodsThirty-six Beagle dogs were randomly divided into two groups: the PM-PJ group (n=18) and the control group (n=18), which included classic end-to-side invagination pancreaticojejunostomy (IPJ). Six Beagles were sacrificed on the 7th, 14th, and 21st days after surgery to obtain pancreaticojejunostomy anastomosis tissues. The primary outcomes were postoperative mortality, morbidity, and pathological changes in the anastomosis. The secondary outcomes were the expression levels of the cytokines TGF-{beta}1, IL-10, TNF-, IL-6, collagen, and -SMA. ResultsAll surgeries were successfully performed. In the study group, the incidences of anastomotic leakage and pancreatitis were 0% and 11.1% (2/18), respectively, whereas in the control group, the incidences were 5.6% (1/18) and 16.7% (3/18), respectively. The levels of serum TGF-{beta}1, IL-10, TNF-, and IL-6 were increased at all time points after surgery in both groups, and these increases were more significant at certain time points in the study group. Pathological observation revealed that the degree of anastomotic healing in the study group was greater than that in the control group at all time points. The protein expression levels of collagen I/III and -SMA in the anastomoses of both groups changed over time, and the changes were more significant at certain time points in the study group. ConclusionInflammatory factors play a positive role in the early stage of PJ anastomosis healing by stimulating collagen secretion and synthesis. PM-PJ is a safe and simple surgery with good anastomotic healing and a low incidence of complications.

animal behavior and cognition↗

bioRxiv2025

D-LMBmapX: Generalized Deep-Learning Pipeline for Automated Whole-brain Neural Circuitry Profiling Across Any Developmental Stages

Understanding whole-brain circuitry development is essential for uncovering the origins of disorders linked to abnormal neural wiring, yet progress has been limited by the lack of tools for accurate, comprehensive analysis. Brain development is highly dynamic and region-specific, with pronounced inter-individual variability during early stages, and no existing method achieves whole-brain profiling at arbitrary stages. The rapidly changing morphology of developing axons further complicates segmentation. Here, we present D-LMBmapX, a deep learning pipeline for automated whole-brain circuitry profiling across postnatal development. D-LMBmapX constructs sample-inferred atlases from flanking anchor stages, enabling accurate registration at any time point. By developing a foundation model for generalised axon and soma segmentation, it supports quantitative mesoscale developmental connectomics, demonstrated through spatial-temporal profiling of catecholaminergic projections. D-LMBmapX also enables robust cross-modality and cross-dimensional registration, including precise alignment of single 2D slices to 3D references, and offers generalizable strategies for 5D analysis of diverse biomedical spatial datasets.

neuroscience↗

bioRxiv2025

MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics

Designing drugs that can restore a diseased cell to its healthy state is an emerging approach in systems pharmacology to address medical needs that conventional target-based drug discovery paradigms have failed to meet. Single-cell transcriptomics can comprehensively map the differences between diseased and healthy cellular states, making it a valuable technique for systems pharmacology. However, single-cell omics data is noisy, heterogeneous, scarce, and high-dimensional. As a result, no machine learning methods currently exist to use single-cell omics data to design new drug molecules. We have developed a new deep generative framework named MolGene-E to tackle this challenge. MolGene-E combines two novel models: 1) a cross-modal model that can harmonize and denoise chemical-perturbed bulk and single-cell transcriptomics data, and 2) a contrastive learning-based generative model that can generate new molecules based on the transcriptomics data. MolGene-E consistently outper-forms baseline methods in generating high-quality, hit-like molecules on gene expression profiles from two evaluation settings: CRISPR knock-out perturbation profiles from L1000toRNAseq dataset, and single-cell gene expression profiles from Sciplex-3 dataset, both in zero-shot molecule generation setting. This superior performance is demonstrated across diverse de novo molecule generation metrics. Extensive evaluations demonstrate that MolGene-E achieves state-of-the-art performance for zero-shot molecular generations. This makes MolGene-E a potentially powerful new tool for drug discovery.

bioinformatics↗

bioRxiv2025

ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and a multi-database search strategy

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions. Protein complexes identified by CF-MS using different databases are typically integrated. However, this integration uses varying cutoffs, leading to inconsistencies in protein complex identification. Here, we present ProteoAutoNet, a robotic experimental platform integrating multi-database search strategy into machine learning models, for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. We then applied this workflow to map protein interaction networks in thyroid cancer cell lines, identifying significantly upregulated proteasome and prefoldin complexes in lung metastatic follicular thyroid carcinoma cell line FTC238 compared to normal thyroid cell line Nthy-ori 3-1. Notably, we identified a novel protein interaction network comprising PFAS, TGM2, and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved approach for investigating protein-protein interactions and uncovering novel networks, driving advancements in high-throughput proteomics research.

biochemistry↗

bioRxiv2025

scWGBS-GPT: A Foundation Model for Capturing Long-Range CpG Dependencies in Single-Cell Whole-Genome Bisulfite Sequencing to Enhance Epigenetic Analysis

Single-cell DNA methylomes are challenging to interpret because of sparse CpG coverage and the complexity of genome-wide sequences. We present scDNAm-GPT, a universal foundation model that uses context-aware CpG tokenization, a Mamba backbone, and cross-attention to capture both local and global DNA methylation patterns. Trained on over one million single cells from 35 human and mouse tissues, scDNAm-GPT enables accurate cell clustering, zero-shot prediction of CpG effects on gene expression, improved trajectory inference, and reference-free deconvolution of cell types from cell-free DNA. The model hierarchically learns regulatory features, and its attention maps highlight functionally relevant regions, demonstrating high biological interpretability. These results establish scDNAm-GPT as a scalable and generalizable framework for single-cell epigenomic analysis, offering new opportunities to dissect epigenetic regulation in health and disease. Code is available at GitHub (https://github.com/ChaoqiLiang/scDNAm-GPT).

bioinformatics↗

bioRxiv2025

Spatial Total RNA Sequencing of Formalin-Fixed Paraffin Embedded Tissues by spRandom-seq

Current oligo(dT) primers-based spatial transcriptomic methods are limited to fresh or fresh-frozen samples due to the low efficiency of oligo(dT) primers in capturing RNAs in degraded or microbial samples. Here, we have developed a random primer-based spatial total RNA sequencing (spRandom-seq) technology for simultaneously capturing whole host and microbial RNAs in formalin-fixed paraffin-embedded (FFPE) tissues. spRandom-seq eliminated 3 or 5 gene-body biases and outperformed oligo(dT)-based 10X Visium with an 8-fold higher capturing rate for lncRNA and other non-polyadenylated RNA biotypes, including miRNA, snRNA and miscRNA. In the clinical FFPE sections of breast cancer, we revealed the inherent heterogeneity within the tumor region. We also simultaneously captured host and microbial RNAs in Klebsiella pneumoniae-infected sections. Totally, spRandom-seq provided a versatile tool for both clinical pathology and infection biology with established spatial platforms, ensuring ease of operation and large-scale applications.

genomics↗

bioRxiv2025

Single-cell multiomics of neuronal activation reveals context-dependent genetic control of brain disorders

Most causal variants for neuropsychiatric disorders (NPD) remain unknown. A major hurdle is that disease variants may act in specific contexts, such as during neuronal activation, which is difficult to study in vivo at the population level. We profiled single-nucleus neuron-activation multiomics in human induced pluripotent stem cell (iPSC)-derived neurons from 100 donors, revealing the NPD-relevant transcriptomic/epigenomic landscape of neuronal activation. We identified abundant genetic variants associated with activity-dependent gene expression (eQTL) and chromatin accessibility (caQTL), the latter explaining larger proportions of NPD heritability. Integrating multiomic data with genome-wide association studies (GWAS) further revealed NPD risk variants and genes with effects detected only upon stimulation, such as activity-dependent cholesterol metabolism. Our work highlights the power of cell stimulation to reveal context-specific "hidden" genetic effects.

neuroscience↗

bioRxiv2025

Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis

In an era of increasing resistance, new and effective strategies are needed for antibiotic discovery. Whole-cell active screens yield candidate compounds lacking mechanism-of-action (MOA) information and thus do not provide biological insight for prioritization. We previously reported PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), an antimicrobial discovery strategy that measures chemical-genetic interactions between small molecules and a pool of Mycobacterium tuberculosis mutants, each depleted of a different essential protein target. PROSPECT facilitates efficient hit prioritization by simultaneously identifying whole-cell active compounds with high sensitivity and providing early insights into their MOA. Here, we report a reference-based approach to infer MOA from often complex PROSPECT data. For this aim, we curated a reference set of 437 compounds with published, annotated MOA and known or suspected antitubercular activity, and applied PROSPECT to it. We then developed Perturbagen CLass (PCL) analysis, a computational method that predicts MOA by comparing chemical-genetic interaction profiles of unknown compounds to those of this reference set. In leave-one-out cross-validation, PCL analysis correctly predicted MOA with 70% sensitivity and 75% precision. When applied to 75 antitubercular leads with known MOA previously reported by GlaxoSmithKline (GSK), PCL analysis similarly achieved 69% sensitivity and 87% precision. We also analyzed 98 GSK compounds lacking MOA information, predicting 60 of them to act via a reference MOA, and followed up with functional validation of 29 compounds predicted to target respiration-related MOAs. Finally, we applied PROSPECT and PCL analysis to [~]5,000 compounds from larger unbiased libraries that had not been preselected for antitubercular activity. PCL analysis identified a novel scaffold lacking wild-type activity but predicted to inhibit respiration via QcrB, and we confirmed this prediction while chemically optimizing this scaffold to achieve wild-type activity. PCL analysis of PROSPECT data thus enables rapid MOA assignment and hit prioritization, advancing the discovery of new, potent antitubercular compounds.

microbiology↗

bioRxiv2025

DNA G-quadruplex Profiling Reveals Functional and Mechanistic Role of G-quadruplexes in Skeletal Muscle Stem Cells

DNA G-quadruplexes (G4s) are non-canonical secondary structures formed in guanine-rich DNA sequences and play important roles in modulating biological processes through a variety of gene regulatory mechanisms. Emerging G4 profiling permits global mapping of endogenous G4 formation. Here in this study, we map the G4 landscapes in adult skeletal muscle stem cells (MuSCs) which are essential for injury induced muscle regeneration. Throughout the myogenic lineage progression of MuSCs from quiescent to activated and further differentiated cells, we uncover dynamic endogenous G4 formation with a pronounced G4 induction when MuSCs become activated and proliferating. We further demonstrate that the G4 induction promotes MuSC activation thus the regeneration process. Mechanistically, we found that promoter associated G4s regulate gene transcription through facilitating chromatin looping. Furthermore, we found that G4 sites are enriched for transcription factor (TF) binding events in activated MuSCs; MAX binds to G4 structures to synergistically facilitate chromatin looping and gene transcription thus promoting MuSC activation and regeneration. The above uncovered global regulatory functions/mechanisms are further dissected on the paradigm of Ccne1 promoter, demonstrating Ccne1 is a bona fide G4/MAX regulatory target in activated MuSCs. Altogether, our findings for the first time demonstrate the prevalent and dynamic formation of G4s in adult MuSCs and the mechanistic role of G4s in modulating gene expression and MuSC activation/proliferation.

molecular biology↗

bioRxiv2025

A novel virus vB_PshM_Y4 with low similarity to both cultured and uncultured viruses

Viruses are the abundant and diverse biological entities on the Earth. During past decades, our knowledge about genomic diversity of viruses has been greatly expanded through high-throughput sequencing. However, whether traditional isolation method could detect virus that could not be identified by high-throughput sequencing is still unknown. In this study, a novel virus, vB_PshM_Y4, infecting Pseudoalteromonas shioyasakiensis, which is an opportunistic pathogen of economically important marine species, was isolated. After comparing vB_PshM_Y4 with the over 15 million genomes of both cultivated and uncultivated viruses in the NCBI and IMG/VR v4 datasets, no similar genome was identified. This suggests that viral isolation methods can detect novel viruses that may not be identified through high-throughput sequencing. Transmission electron microscopy showed that vB_PshM_Y4 exhibits the myoviral morphology. The virus possesses a double-stranded DNA genome that is 158,231 bp in length, with a G+C content of 30.92%, and contains 226 open reading frames (ORFs). Six ORFs were identified as putative auxiliary metabolic genes (AMGs). Phylogenetic tree and gene content-based network analyses indicated that vB_PshM_Y4 may represent a novel viral species within an unknown family of the Caudoviricetes class. Additionally, biogeographical distribution analysis showed that vB_PshM_Y4 were mainly detected in the Arctic and temperate and tropical epipelagic zones with low abundance. ImportanceThis study suggests that viral isolation could detect novel virus that could not be identified through high-throughput sequencing, contributed to a deeper understanding of the genetic diversity and distribution of Pseudoalteromonas viruses, and offered valuable insights into the prevention and control of opportunistic pathogenic bacteria affecting economically important marine organisms.

microbiology↗

bioRxiv2025

Haplotype-based Parallel PBWT for Biobank Scale Data

Durbins positional Burrows-Wheeler transform (PBWT) enables algorithms with the optimal time complexity of O(MN) for reporting all vs all haplotype matches in a population panel with M haplotypes and N variant sites. However, even this efficiency may still be too slow when the number of haplotypes reaches millions. To further reduce the run time, in this paper, a parallel version of the PBWT algorithms is introduced for all versus all haplotype matching, which is called HP-PBWT (haplotype-based parallel PBWT). HP-PBWT parallelly executes the PBWT by splitting a haplotype panel into blocks of haplotypes. HP-PBWT algorithms achieve parallelization for PBWT construction, reporting all versus all L-long matches, and reporting all versus all set-maximal matches while maintaining memory efficiency. HP-PBWT has an [Formula] time complexity in PBWT construction, and an [Formula] time complexity for reporting all versus all L-long matches and reporting all versus all set-maximal matches, where T is the number of threads and c* is the maximum number of matches (of length L or maximum divergence value for L-long matches and set-maximal matches, re-spectively) per haplotype per site. HP-PBWT achieves 4-fold speed-up in UK Biobank genotyping array data with 30 threads in the IO-included benchmarks. When applying HP-PBWT to a dataset of 8 million randomized haplotypes (random binary strings of equal length) in the IO-excluded benchmarks, it can achieve a 22-fold speed-up with 60 cores on the Amazon EC2 server. With further hardware optimization, HP-PBWT is expected to handle billions of haplotypes efficiently.

bioinformatics↗