Biology subjects

Shao, H.

Publications and source records attributed to Shao, H..

At least 19 recordsLinked to original sources

bioRxiv2026

An integrative single-cell and spatial transcriptomics atlas highlights candidate regulatory factors in the development of gerbera capitulum

In Asteraceae species, the capitulum is a compact inflorescence, featuring a characteristic reproductive structure. Despite the identification of a few key regulatory factors, the transcriptome-level information on the developing capitulum remains limited. Here, we applied single-cell and spatial transcriptome sequencing to investigate the developing Gerbera hybridas capitulum during floret differentiation. We obtained a transcriptomics atlas encompassing different stages of the Gerbera capitulum and analyzed the cellular and spatial dynamics of gene expression. Using marker gene expression and GO enrichment of cluster-specific DEGs, we annotated putative cell types and described changes in gene expression across sampled stages, potentially associated with ongoing developmental processes. We detected activity of previously undescribed MADS-box genes and defined their spatial expression patterns. Notably, the MADS-box gene GAGL12 was found to be enriched in the putative capitulum phloem cells. The GAGL12 protein was shown in yeast two-hybrid assays to interact with several other MADS-domain proteins with hypothesized functions in vasculature development, and further detailed in silico analyses supported a candidate role in the development of capitulum vasculature. Altogether, we provide integrative and dynamic transcriptomic insight into capitulum and floret development and lay a basis for future functional studies of the control and development of this intriguing reproductive structure.

plant biology↗

bioRxiv2026

Microfluidic Tissue Array Platform for Personalized Drug Screening Using Tumor Explants or Biopsies

Despite major improvements in molecular characterization of breast cancer, current biomarkers still fall short in accurate treatment prediction. Interrogating tumor tissue ex-vivo in its native conformation is a direct strategy for guiding treatment of individual patients but presents a challenge. In this study, we developed a microfluidic tissue array (FTA) using small biopsy samples (< 1mm3) mimicking physiological flow for consistent exchange of nutrients and waste, retaining the tumor native stroma. Cell/patient-derived breast cancer xenograft tissues were maintained over 2 weeks in the array and their response to therapeutic agents, doxorubicin or neratinib, were interrogated. Drug response in the uFTA showed >2-fold reduction in tumor cell viability which corroborated tumor size shrinkage in mice bearing the same tumor load. EdU/Ki67 assays indicated selective retention of cells with higher proliferative capacity after drug treatment, underscoring in vivo clinical relevance . We have also developed a valved-FTA to increase throughput and variety of treatment conditions on the same chip. Together, this FTA can be staged as a powerful, low-cost benchtop theranostic tool for personalized cancer therapeutics compatible with FDAs New Approach Methods.

cancer biology↗

bioRxiv2026

Geometric Theoretical Framework for Dynamic Protein Mutation Detection Models: Defect Awareness and Pathogenicity Prediction

Traditional protein mutation detection and pathogenicity prediction pipelines rely on static single-conformation structural modeling, inherently ignoring conformational flexibility, dynamic ensemble evolution, and the underlying manifold geometry of protein dynamics. This induces systematic detection failures in flexible regions, allosteric sites, and metastable functional domains, yet lacks a rigorous mathematical characterization of such failure mechanisms. In this work, we establish a theorem-driven geometric-algebraic framework for dynamic protein mutation modeling. Starting from a dynamic conformational Riemannian manifold, we construct the latent representation space via representation-induced completion of operator-valued observations, rather than pre-assumed embedding structures. Within this setting, algebraic constraints are not imposed axiomatically but relaxed into learnable approximate Lie algebra regularization, enabling statistical verification of structural consistency. By integrating Levi-Civita connection, geodesic deviation, and heat kernel asymptotics, we introduce a Lipschitz-stable topological spectral defect (TSD,{delta} spec) index that quantifies the intrinsic inconsistency between static representations and dynamic geometric invariants, linking it to curvature-induced instability and Lie algebra deformation. Under a functorial compatibility principle, we design a dual-branch architecture for pathogenicity prediction and defect awareness, realized via local Lie algebra encoding and low-rank spectral approximation. On multi-source datasets (108 curated PDB structures, 1060 validated residues from ClinVar, DMS, MaveDB, and gnomAD), we establish three fundamental theorems and validate key findings: TSD effectively distinguishes pathogenic/functional variants (PTM: {micro} = 0.386, OMIM: {micro} = 0.443, Clin-Var: {micro} = 0.302) from neutral ones (gnomAD: {micro} = -0.660) with high significance (P = 6.67 x 10 -18) and strong classification performance (AUC=0.82-0.86), while correlating strongly with protein stability ({Delta}{Delta}G, Spearman=0.9794, P = 5.38 x 10 -28). TSD further reveals PTM sites as topological hubs and neutral variants as evolutionary topological redundancy, enabling a paradigm shift from sequence alignment to geometric dynamics and providing a physics-based biomarker for variants of uncertain significance (VUS). These results upgrade protein mutation modeling from empirical static prediction to provable dynamic mechanism analysis. The source code of this work is publicly available at https://github.com/Harmenlv/LieFold-AI/tree/main.

bioinformatics↗

bioRxiv2026

EA-PheWAS: Integrating Phenotype Embeddings with PheWAS for Enhanced Gene-Phenotype Discovery

Phenome-wide association studies (PheWAS) enable systematic exploration of relationships between genetic variants and clinical phenotypes derived from electronic health records (EHRs). Conventional regression-based PheWAS treats phenotypes separately and relies on binary phenotype representations, which limits statistical power for rare variants and rare phenotypes and reduces the ability to detect associations with phenotypes that are distributed across clinical codes. To address this limitation, we first developed EmbedPheScan, a phenotype embedding-based prioritization framework that summarizes the phenotypic profiles of rare loss-of-function variant carriers in a continuous embedding space. We then proposed EA-PheWAS by combining these embedding-derived signals with conventional regression-based PheWAS results using the aggregated Cauchy association test. Using the UK Biobank whole-exome sequencing and EHR data, we show that the proposed methods maintain appropriate false-positive control. We then performed genome-wide phenome scans across all genes and across biologically defined gene classes to evaluate EA-PheWAS relative to conventional PheWAS and EmbedPheScan, consistently finding that EA-PheWAS outperformed the other two methods. We illustrate the utility of EA-PheWAS focusing on four genes representing distinct scenarios, including strong-effect disease genes (PKD1, PKD2), genes with large numbers of rare LoF carriers (NF1), and genes with extremely sparse carrier counts (FBN1).

genetics↗

bioRxiv2026

PD1-induced Shp2 condensation organizes inhibitory signalosomes through selective substrate partitioning

The formation of microclusters is a hallmark of PD1 engagement with its ligands, yet the physical basis and functional significance of this phenomenon remain unclear. Here we show that ligand-bound PD1 licenses Shp2 self-association and liquid-liquid phase separation (LLPS), producing dynamic PD1:Shp2 condensates whose liquidity depends on Shp2 catalytic activity. Mutations that selectively disrupt Shp2 self-association weaken PD1 microcluster formation and impair PD1 inhibitory function. Mechanistically, PD1-induced Shp2 LLPS promotes the co-compartmentalization of signaling substrates such as CD3{zeta} and CD28, thereby facilitating its dephosphorylation. These findings identify Shp2 LLPS as an intrinsic organizing principle of the PD1 inhibitory pathway that links enzymatic activation, substrate selectivity and mesoscale assembly to suppress T cell activation.

biochemistry↗

bioRxiv2025

Ecology of hydrocarbon degradation in widespread viruses

Hydrocarbons are vital for energy production and lead to serious environmental issues due to pollution. Microorganisms largely drive the production and degradation of hydrocarbons, yet little is known about viral contributions to hydrocarbon degradation. Here we identified 786 viral contigs from IMG/VR(v4), encoding five aerobic (alkB, ladA, almA, ndoB and dszC) and three anaerobic (bssA, ebdA and abcA) hydrocarbon-degrading genes (HDGs). vOTUs encoding HDGs span 26 distinct viral families, including 249 are mainly associated with host-associated environment and 463 are derived from aquatic habitat, respectively, implying that these viruses are broadly engaged in hydrocarbon-degradation processes across the entire biosphere. alkB (35%) and almA (28%) was tended to be encoded by Schizomimiviridae, bssA was tended to be encoded by T5-like bacteriophages (29%) and SPO1-like bacteriophages (22%), suggesting that the carriage of HDGs by viruses exhibits taxonomic specificity. With respect to host associations, Pseudomonadota and Bacillota constitute the two main potential host lineages, associated with 268 and 71 vOTUs, respectively. The profiling of ecological footprint reveals that viruses contribute a total of up to 30% gene abundance and 32% transcribing activity to hydrocarbon degradation in the global ocean. This study systematically revealed the distribution, diversity, virus-host correlation and activity of virus-encoded HDGs, underscoring the significant role of viruses in hydrocarbon metabolism on a global scale.

ecology↗

bioRxiv2025

CellBin:a generalist framework to process spatial omics data to cell level

Spatial omics has rapidly expanded with increasingly diverse imaging modalities, molecular targets, and chip sizes. However, no general framework currently exists to construct cell level matrices that are robust across platforms and omics types. Here we present CellBin, a universal and scalable frame-work that unifies image stitching, cell segmentation, and spot-to-cell mapping for multiple spatial omics technologies. CellBin integrates a multi-field weighted stitching algorithm for large-area images, a family of U-Net-based models trained across diverse staining modalities, and an optimized computational architecture for high-throughput processing. Across five technological platforms and three omics data types, CellBin achieves robust segmentation and accurate single-cell matrix construction, consistently outperforming seven state-of-the-art methods in F1-score, cell size precision, and annotation accuracy. By providing a generalizable, cross-platform solution, CellBin bridges multiple spatial omics, enabling unified, high-resolution cell level analyses across technologies.

bioinformatics↗

bioRxiv2025

Histology-informed spatial domain identification through multi-view graph convolutional networks

Identifying spatial domains is crucial in spatial transcriptomics, yet effectively integrating gene expression, spatial location, and histology remains challenging. We present STESH, a Spatial Transcriptomics clustering method that combines Expression, Spatial information and Histology. STESH extracts histological features using a convolutional neural network and generates expression, histology, spatial, and collaborative convolution modules for a multi-view graph convolutional network with a decoder and attention mechanism. We evaluated STESH on multiple tissue types and technology platforms. STESH consistently outperformed ten state-of-the-art methods, achieving superior clustering accuracy with the highest scores in adjusted Rand index, normalized mutual information, and Fowlkes-Mallows index.

bioinformatics↗

bioRxiv2025

Mechanical, Biochemical, and Multicellular Effects on Vessel Network Morphometrics in a Microfluidic Vasculature-on-a-Chip

Microvascular networks (MVNs) formed via endothelial cell self-assembly in 3D hydrogels have emerged as a widely used platform for modeling vascularized tissues and studying vascular pathophysiology. Conventional MVN systems incorporate supporting fibroblasts and may include biochemical cues such as VEGF, FGF, or S1P, as well as mechanical stimuli like luminal flow, yet the impact of these variables on MVN morphology and function remains incompletely understood. Here, we systematically investigated the effects of fibroblast concentration, fibroblast-conditioned media, angiogenic factors, and luminal flow on the morphology, perfusability, and vessel wall integrity of MVNs cultured in a microfluidic vasculature-on-a-chip. In addition to standard branch-based metrics such as vessel coverage area and diameter, we developed and applied novel void-based morphological parameters that quantify the size, shape, and distribution of vessel-free spaces to capture subtle differences across MVN culture conditions. Our results demonstrate that high fibroblast-to-endothelial cell ratios accelerate MVN formation but promote excessive vessel fusion, while MVNs cultured without fibroblasts--using only conditioned media or soluble factors--exhibited patch-like, non-physiological morphology with reduced branch formation. Direct inclusion of fibroblasts proved essential for promoting the thin, interconnected vascular structures characteristic of in vivo microvasculature and could not be substituted by soluble cues alone. Furthermore, the presence or absence of fibroblasts modulated MVN responsiveness to luminal flow. Overall, our void-based analysis method enabled more sensitive discrimination of MVN morphological features than traditional branch-based metrics and offers a reduced-data, high-content approach suitable for integration with machine learning and AI-assisted image analysis pipelines. This platform provides a new framework for optimizing MVN culture protocols and advancing vascular tissue engineering studies, particularly for the advancement of organ-on-a-chip (OOC) and microphysiological systems.

bioengineering↗

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

Single-cell and spatial transcriptomics uncover the role of B chromosomes in driving plant invasiveness

Invasive plants pose a major threat to global biodiversity, yet the molecular and genomic mechanisms underlying their success remain poorly understood. Here, we investigate the common reed (Phragmites australis), a grass species that became invasive in North America after introduction from Europe, to unravel the molecular mechanisms of its invasiveness. By integrating single-cell RNA sequencing, spatial transcriptomics, and comparative genomics, we constructed a single-cell atlas of P. australis and identified 19 transcriptionally distinct cell types, including shoot apical meristem, epidermal, and vascular tissues. Comparative analysis of common garden-grown native (European) and invasive (North American) populations revealed a significant proportion of differentially expressed genes (DEGs) located on B chromosomes, which underwent copy number expansion in invasive genomes. The proportion of B chromosome genes in DEGs varies across cell types, with the highest proportions observed in the epidermis and mesophyll, and lower proportions in the vascular tissues. Gene IMPA-3, a B chromosome gene likely derived from transposable element activity, exhibited an elevated mutation rate compared to its ancestral counterparts, potentially enhancing adaptive evolution in invasive populations. Invasive individuals also displayed molecular regulatory networks related to photosynthetic efficiency, stress tolerance, and growth-defense trade-offs. Together, our findings provide a cell-type-resolved molecular atlas of a non-model invasive plant and offer key insights into the cellular and genomic architecture of plant invasiveness, with implications for ecological management.

evolutionary biology↗

bioRxiv2024

Nucleosome placement and polymer mechanics explain genomic contacts on 100kbp scales

The 3d organization of the genome -- in particular, which two regions of DNA are in contact with each other -- plays a role in regulating gene expression. Several factors influence genome 3d organization. Nucleosomes (where [~] 100 basepairs of DNA wrap around histone proteins) bend, twist and compactify chromosomal DNA, altering its polymer mechanics. How much does the positioning of nucleosomes between gene loci influence contacts between those gene loci? And, to what extent are polymer mechanics responsible for this? To address this question, we combine a stochastic polymer mechanics model of chromosomal DNA including twists and wrapping induced by nucleosomes with two data-driven pipelines. The first estimates nucleosome positioning from ATACseq data in regions of high accessibility. Most of the genome is low-accessibility, so we combine this with a novel image analysis method that estimates the distribution of nucleosome spacing from electron microscopy data. There are no fit parameters in the biophysical model. We apply this method to IL6, IL15, CXCL9, and CXCL10, inflammatory marker genes in macrophages, before and after inflammatory stimulation, and compare the predictions with contacts measured by conformation capture experiments (4C-seq). We find that within a 500 kilo-basepairs genomic region, polymer mechanics with nucleosomes can explain 71% of close contacts. These results suggest that, while genome contacts on 100kbp-scales are multifactorial, they may be amenable to mechanistic, physical explanation. Our work also highlights the role of nucleosomes, not just at the loci of interest, but between them, and not just the total number of nucleosomes, but their specific placement. The method generalizes to other genes, and can be used to address whether a contact is under active regulation by the cell (e.g., a macrophage during inflammatory stimulation).

biophysics↗

bioRxiv2024

How a pathogenic mutation impairs Hsp60 functional dynamics from monomeric to fully assembled states

Heat Shock Protein 60 kDa (Hsp60) is a mitochondrial chaperonin that cooperates with Hsp10 to drive the correct folding of client proteins. Monomers M of Hsp60 (featuring equatorial, intermediate, and apical domains) first assemble into 7-meric Single rings (S), then pairs of S interface equatorially to form 14-meric Double rings (D) that accommodate clients into their lumen. Recruitment of 7 Hsp10 molecules per pole turns D into a 28-meric Football-shaped complex (F). Sequential hydrolysis of ATP present in each Hsp60 unit of F finally drives client folding and F disassembly. Equatorial domain mutation V72I occurs in SPG13, a form of hereditary spastic paraplegia: while distal to the active site, this severely impairs the chaperone cycle and stability. To understand the molecular bases of this impairment we have run atomistic molecular dynamics (MD) simulations of M, S, D, and F for both WT and mutant Hsp60, with two catalytically relevant Hsp60 aspartates in D and F modelled in three different protonation states. Additionally, D in one protonation state was modelled post-hydrolysis (total production time: 36 {micro}s). By combining complementary experimental and computational approaches for the analysis of functional dynamics and allosteric mechanisms, we consistently find that mutation V72I significantly rewires allosteric routes present in WT Hsp60 across its complexes, from isolated M units right up to F, rigidifying them--as observed experimentally--by introducing a direct allosteric link between equatorial and apical Hsp60 domains that bypasses the ATP binding site (wherein we observe the alteration of mechanisms driving reactivity). Our results reveal a multiscale complexity of functional mechanisms for Hsp60 and its pathogenic mutant, and may lay the foundation for the design of experiments to fully understand both variants.

biochemistry↗

bioRxiv2024

Decoding protein glycosylation by an integrative mass spectrometry-based de novo sequencing strategy

Glycoproteins, representing over 50% of human proteins and most biopharmaceuticals, are crucial for regulating various biological processes. The complexity of multiple glycosylation sites, often leading to incomplete sequence coverage and ambiguous glycan modification profiles. Here, we developed an integrative mass spectrometry-based approach for decoding unknown glycoproteins, which is featured with the combination of deglycosylation-mediated de novo sequencing with glycosylation site characterization. We utilized enzymatic deglycosylation of N-/ O-glycan to achieve comprehensive sequence coverage. Additionally, EThcD fragmentation enables the identification of high-quality long peptides, facilitating precise protein assembly. We subsequently applied this method to de novo sequencing of the highly glycosylated therapeutic fusion protein Etanercept (Enbrel(R)). We also sequenced three new tumor necrosis factor receptor (TNFR): Fc-fusion biologics with largely unknown sequences, unveiling subtle distinctions in the primary sequences. Furthermore, we characterized N- and O-glycosylation modifications of these proteins at subunit, glycopeptide, and glycan levels. This strategy bridges the gap between the de novo sequencing and glycosylation modification, providing complete information of the primary structure and glycosylation modifications for glycoproteins. Notably, our method could be a robust solution for accurate sequencing of the glycoproteins and has practical value not only in basic research but also in the biopharmaceutical industry.

biochemistry↗

bioRxiv2024

Insomnia score: predictive ability of insomnia, high-diagnostic and prognostic value for cancer.

ObjectiveThis study utilized bioinformatics methods to investigate the association between insomnia and cancer. MethodsWe identified key insomnia-related genes and constructed an insomnia score (riskScore = {sum} Expressiongene * Coefficient) based on an insomnia dataset using differential analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression. The predictive ability of the insomnia score was validated in the insomnia dataset using Receiver Operating Characteristic (ROC) analysis. Finally, the diagnostic and prognostic value of the insomnia score was assessed in a pan-cancer cohort. ResultsDifferential analysis, WGCNA, and LASSO screening of the insomnia dataset yielded three key insomnia-related genes (PTMA, NLRP8, and CCBE1). An insomnia score was then constructed using the formula: riskScore = -0.8039667 * ExpressionPTMA + - 3.1975230 * ExpressionNLRP8 + -0.4957560 * ExpressionCCBE1. The ROC analysis demonstrated that the insomnia score has excellent predictive ability for insomnia. Furthermore, pan-cancer analysis illustrated the diagnostic and prognostic value of the insomnia score across various cancers. ConclusionWe successfully constructed an insomnia score and demonstrated its diagnostic and prognostic value in multiple cancers.

bioinformatics↗

bioRxiv2024

Diversity and ecological potentials of viral assemblages from the seamount sediments of the Northwest Pacific Ocean

Viruses are the most abundant life forms in the sea, influencing the community structure and metabolism of host cells as well as biogeochemical cycles. However, the diversity and ecological roles of viruses within seamount ecosystems, natural microbiota havens characterized by high biodiversity, remain unknown. Here, the first seamount viral genome (SMVG) dataset, based on a metagenomic analysis of twelve seamount sediment samples collected from the seamount regions of the Northwest Pacific Ocean, is established. A total of 78,069 viral operational taxonomic units (vOTUs) were found, spanning 18 viral classes and 63 viral families. The detection of sixteen viral auxiliary metabolic genes (vAMGs) suggests that viruses may participate in both the complex metabolic dynamics associated with sediment microbial communities and also biogeochemical cycles, including carbon, sulfur, metal, heme, and cobalamin cycling. vAMGs involved in the metabolism of heme, cobalamin and metals, in particular, are more often detected in seamount sediments than in trenches, cool seeps, and hydrothermal vents. This investigation of the viral communities in these seamount sediments provides new insights into the high diversity and ecological potential of the viruses and establishes a foundation for the future study of benthic viruses from deep-sea seamounts.

ecology↗

bioRxiv2024

Structures of transcription-translation coupling complexes at each stage of the translation cycle

In the bacterium Escherichia coli, efficient expression of protein-coding genes is achieved through physical coupling of RNA polymerase (RNAP) synthesizing an mRNA and the first ribosome translating the mRNA, with transcription elongation factors NusG and NusA bridging RNAP and ribosome in a transcription-translation complex (TTC). Recent structural studies suggest that the flexibility in NusG and NusA accommodates the changes in ribosome conformation that occur during the translation cycle, but details have been unclear. Here, we report cryo-EM structures of TTCs at each stage of the translation cycle as an actively translating ribosome approaches to a halted transcription complex. We observe sequential conversion of a loosely coupled TTC (TTC-LC) to a tightly coupled TTC (TTC-B) to a collided TTC (TTC-A) as the ribosome approaches the transcription complex. We show that flexibility of the NusG linker and NusA "pantograph" enable TTC-LC and TTC-B to accommodate ribosome conformation at each stage of the translation cycle. We show that, in TTC-A, steric clash between ribosome and RNAP upon ribosome 30S head swiveling inhibits 30S head swiveling, consistent with translation slowdown, and applies mechanical force to the trailing edge of RNAP, causing transcription termination. Our results define the structures and properties of loosely coupled, tightly coupled, and collided TTCs at each stage of the translation cycle.

molecular biology↗

bioRxiv2024

BSAlign: a library for nucleotide sequence alignment

Increasing the accuracy of the nucleotide sequence alignment is an essential issue in genomics research. Although classic dynamic-programming algorithms (e.g., Smith-Waterman and Needleman-Wunsch) guarantee to produce the optimal result, their time complexity hinders the application of large-scale sequence alignment. Many optimization efforts that aim to accelerate the alignment process generally come from three perspectives: re-designing data structures (e.g., diagonal or striped Single Instruction Multiple Data (SIMD) implementations), increasing the number of parallelisms in SIMD operations (e.g., difference recurrence relation), or reducing searching space (e.g., banded dynamic programming). However, no methods combine all these three aspects to build an ultra-fast algorithm. We have developed a Banded Striped Aligner(library) named BSAlign that delivers accurate alignment results at an ultra-fast speed by knitting a series of novel methods together to take advantage of all of the aforementioned three perspectives with highlights such as active F-loop in striped vectorization and striped move in banded dynamic programming. We applied our new acceleration design on both regular and edit-distance pairwise alignment. BSAlign achieved 2-fold speed-up than other SIMD based implementations for regular pairwise alignment, and 1.5 to 4-fold speedup in edit distance based implementations for long reads. BSAlign is implemented in C programing language and is available at https://github.com/ruanjue/bsalign.

bioinformatics↗