Biology subjects

Tang, G.

Publications and source records attributed to Tang, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Impact of Trastuzumab and Pertuzumab on HER2 Localization and the Estrogen Receptor Cistrome in ER+/HER2+ Breast Cancer

Background: Although anti-HER2 monoclonal antibody therapy with trastuzumab and pertuzumab is highly effective for HER2-positive breast cancer, co-expression of estrogen receptor (ER) significantly reduces pathologic complete response rates. Objective: We investigated how HER2-targeted inhibition affects HER2 cellular localization and ER genomic binding in ER-positive, HER2-positive breast cancer. Methods: We evaluated HER2 localization in a tissue microarray of treatment-naive patients. Using cellular fractionation, immunofluorescence, chromatin immunoprecipitation, and genome-wide profiling (CUT&RUN), we evaluated HER2 localization and ER genomic binding following acute anti-HER2 treatment and in models of treatment resistance. The treatment-induced ER-bound gene signature was assessed for associations with pathologic complete response and survival in breast cancer clinical cohorts. Results: In treatment-naive primary breast tumors, nuclear HER2 inversely correlated with ER levels. In cell models, treatment with trastuzumab and pertuzumab induced nuclear and chromatin accumulation of HER2. Concurrently, acute anti-HER2 treatment displaced ER from canonical estrogen response elements at classical target genes, yet genome-wide profiling revealed redistribution of ER binding toward non-canonical zinc finger motifs adjacent to pro-survival Wnt and RAGE pathway genes (FZD8, RELA). Expression of a drug-induced ER-bound gene signature was significantly higher in tumors of individuals who did not achieve pathologic complete response following neoadjuvant anti-HER2 therapy and significantly correlated with reduced distant metastasis-free survival in clinical cohorts. Conclusions: Anti-HER2 targeted therapy causes dynamic cistromic reprogramming of ER from classical estrogen pathways toward alternative pro-survival networks. These findings implicate ER redistribution as a possible mediator of resistance and highlight novel therapeutic targets in ER-positive, HER2-positive breast cancer.

cancer biology↗

bioRxiv2026

Anthocyanin-associated cellular programs underlying terroir variation in Cabernet Sauvignon grape berry revealed by SEED-based deconvolution

Plant tissues consist of diverse cell populations that collectively contribute to development, metabolism, environmental responses, and phenotype formation. Although single-cell and single-nucleus RNA sequencing have greatly advanced the study of plant cellular heterogeneity, their application to large sample cohorts remains limited by cost, technical complexity, tissue dissociation constraints, and throughput. In contrast, bulk RNA-seq datasets have accumulated extensively across plant species, tissues, developmental stages, and environmental conditions, yet the celltype-level information embedded in these datasets remains difficult to resolve because plant-oriented deconvolution frameworks are still lacking. Existing deconvolution methods have largely been developed in mammalian systems and have not been systematically optimized for plant transcriptomic features, leaving their applicability under plant-specific constraints unclear. Here, we present SEED, an adaptive deconvolution framework optimized for plant transcriptomic data. SEED integrates candidate reference-template construction with seven deconvolution strategies and automatically identifies an optimal combination for a given dataset. In grapevine simulated benchmarking, SEED showed its clearest advantage under low-replication conditions and remained broadly competitive, rather than uniformly dominant, when larger pseudo-bulk sample sizes were evaluated. SEED further performed robustly in public Arabidopsis thaliana and Nicotiana tabacum datasets. Finally, we applied SEED to bulk RNA-seq data generated in this study from Vitis vinifera cv. Cabernet Sauvignon berries collected from Yinchuan and Yantai, identifying terroir-associated cell subtypes and coordinated celltype interaction patterns. Together, these results establish SEED as a practical framework for plant transcriptome deconvolution and provide a new tool for dissecting cellular heterogeneity associated with environmental adaptation and phenotype formation in plants.

bioinformatics↗

bioRxiv2026

Scaling Multiplex qPCR Primer Design to 1000-plex using the Degenerate Incomplete Multiplex Primer List Extension (DIMPLE) Algorithm

Massively multiplexed qPCR is primarily constrained by increasing primer dimer formation as the number of distinct primers in a single reaction increases. Previous multiplex primer design algorithms either fail to sufficiently suppress primer dimers at 100+ plex, or take exceedingly high amounts of computational resources to complete. Here, we present DIMPLE, a linear-runtime primer design algorithm that effectively generates 10,000+ primers to amplify thousands of potential amplicons in a single qPCR reaction. As one clinical demonstration of this algorithm, we designed an assay to detect 2,302 distinct KMT2A gene fusion subtypes using 204 primers in a single tube. In contrast to FISH and convention NGS approaches with 2% variant allele frequency (VAF) limit of detection, our DIMPLE qPCR assay was able to analytically detect gene fusions down to 0.05% VAF. We also constructed proof-of-concept multiplex qPCR panels for additional oncology gene fusions, multiplex pathogen detection, and DNA methylation markers. The scalability and low computational cost DIMPLE are complementary to new instrument platforms for massively multiplex qPCR readout for enabling rapid, point-of-care nucleic acid testing.

bioengineering↗

bioRxiv2026

An enzymatic metabolic sensing axis in taste cells detects glucose-yielding carbohydrates

Glucose is a potent reinforcer of intake, yet most foods contain complex saccharides that do not yield free glucose until after digestion. How the oral sensory system rapidly evaluates the potential metabolic value of food remains unclear. Here, we identify an oral enzymatic-metabolic sensing mechanism that enables detection of glucose-yielding carbohydrates independent of canonical sweet taste receptors. Using genetic, virogenetic, molecular, and behavioral approaches in mice, we show that glucokinase (GCK) in taste cells is necessary for the attraction to glucose-containing sugars. We further demonstrate that maltase glucoamylase (MGAM), a glycosidic enzyme expressed on and near taste cells, facilitates rapid oral sugar sensing. Disruption of either GCK or MGAM in the major taste fields selectively attenuates the attraction to maltose and a carbohydrate-rich mixed diet, establishing both as intermediaries in the initial transduction pathway for complex saccharides that ultimately give rise to nutrient reward. Molecular profiling of taste papillae further revealed that deficient sweet sensing was accompanied by a compensatory increase in lingual MGAM, highlighting an adaptive mechanism for maintaining oral carbohydrate sensitivity. Together, these findings reveal that the oral epithelium actively preprocesses and metabolically evaluates dietary carbohydrates, providing a mechanism for rapid estimation of energetic value prior to ingestion. Significance StatementCarbohydrates strongly motivate eating, yet most are consumed in complex forms that do not immediately release glucose. We show that the oral epithelium contains an adaptive enzymatic-metabolic sensing system that enables rapid evaluation of glucose-yielding carbohydrates before digestion. Two enzymes, maltase glucoamylase and glucokinase, act locally in taste fields to preprocess and metabolically assess complex sugars, biasing ingestive behavior toward energetically favorable foods. This mechanism operates independently of canonical sweet taste receptors and is dynamically regulated by dietary experience and receptor sensitivity. These findings reveal that the mouth actively estimates the energetic value of food prior to ingestion, reshaping our understanding of how nutrient sensing guides dietary choice.

neuroscience↗

bioRxiv2025

Transforming a Fragile Protein Helix into an Ultrastable Scaffold via a Hierarchical AI and Chemistry Framework

The rational design of proteins that maintain structural integrity under concurrent thermal, mechanical, and chemical stress remains a challenge in molecular engineering. We present a hierarchical framework that transforms an -helical domain into an ultrastable scaffold by integrating AI-guided design with foundational chemical principles. This approach progresses from global architectural reinforcement, using multiple AI tools to create a stabilized four-helix bundle, to local chemical tuning, where AlphaFold3 guides the installation of salt bridges and metal-coordination motifs. A computational pipeline using physics-based screening such as molecular dynamics (MD) simulations efficiently distilled millions of designs into a minimal candidate set. The resulting -helical proteins exhibit unprecedented multi-axis stability, with mechanical unfolding forces exceeding 200 pN, thermal resilience >100 {degrees}C, and high resistance to chemical denaturants. By systematically dissecting the contributions of hydrophobic packing, electrostatics, and metal coordination, we establish a general blueprint for imparting extreme robustness. This work bridges AI-driven structural generation with chemical precision, advancing the creation of durable proteins for mechanistic studies and synthetic biology.

biophysics↗

bioRxiv2025

Macrophages display spatiotemporal specificity in intrahepatic cholangiocarcinoma and drive tumour progression via OSM and THBS1

Tumor-associated macrophages (TAMs) are abundant in intrahepatic cholangiocarcinoma (ICC) and correlate with poor prognosis, but their non-immunosuppressive roles remain unclear. This study aimed to define TAM transcriptional heterogeneity and map their interactions with ICC cells that promote malignancy beyond immune suppression. Single-cell and spatial transcriptome analysis with mouse and human ICC tissues revealed a TAM subpopulation, designated M{varphi}_VEGFA, that emerged as the major macrophage population from the intermediate stage of ICC development onward. Data from Gene Expression Omnibus (GEO) and spatial transcriptome indicate that M{varphi}_VEGFA was associated with poor prognosis and spatially localized to the ICC cells. Cell co-culture and cell-cell communication analyses revealed that M{varphi}_VEGFA promoted ICC cell proliferation via secreted OSM and downstream OSMR-STAT3-CCND1 axis, and enhanced ICC cell invasion through THBS1 and downstream CD47-mTOR/DBN1 signaling. Critically, multiplex immunofluorescence with human ICC tissue microarray indicated that OSM and THBS1 were co-elevated in human ICCs, and dual blockade of both targets inhibited ICC progression in mouse models. This study uncovers the direct and critical roles of M{varphi}_VEGFA in driving ICC progression with paracrine signaling and further highlights the potential of targeting macrophages in ICC treatment.

cancer biology↗

bioRxiv2025

Targeting HMGA1-driven leukemic transformation in myeloproliferative neoplasms with pacritinib

Progression of myeloproliferative neoplasms (MPNs) to secondary acute myeloid leukemia (sAML) is a lethal transition lacking reliable early biomarkers and effective therapies. Integrating multi-omics analyses, a cohort of 240 patient biopsies, and functional studies, we identify High Mobility Group A1 (HMGA1) as a critical driver and predictor of this transformation. HMGA1 expression, readily detectable by immunohistochemistry, progressively increases from chronic MPN phases to sAML, outperforming established markers (CD34/CD117) in predicting impending leukemic transformation (AUC = 0.96). Mechanistically, HMGA1 cooperates with JAK2V617F and TP53 mutations to promote leukemogenesis. Clinically, high HMGA1 levels correlate with resistance to first-generation JAK inhibitors and portend poor overall survival. Importantly, the next generation JAK2/FLT3 inhibitor pacritinib abrogates HMGA1-driven proliferation and significantly extends survival in preclinical models, offering an immediate therapeutic strategy for this high-risk population. Our findings establish HMGA1 as an actionable biomarker for risk stratification and early intervention, and a therapeutic target to overcome therapeutic resistance in MPN-sAML. HighlightsO_LIHMGA1 IHC staining serves as a readily implementable biomarker, outperforming existing marker for early prediction of MPN progression to sAML and identifying patients at high risk. C_LIO_LIElevated HMGA1 expression correlates with resistance to first-generation JAK inhibitors and predicts poor overall survival in MPN-sAML patients, highlighting a critical unmet therapeutic need. C_LIO_LIThe next-generation JAK2 inhibitor pacritinib effectively overcomes HMGA1-driven aggressive disease and therapy resistance, offering a rational, clinically available treatment strategy for HMGA1-high MPN-sAML. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/657170v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@18b835org.highwire.dtl.DTLVardef@e5b31dorg.highwire.dtl.DTLVardef@5245caorg.highwire.dtl.DTLVardef@163f5de_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

EEG entropy reflects both intrinsic and stimulation-induced corticospinal excitability

BackgroundCortical excitability fluctuates throughout the day on multiple timescales, ranging from milliseconds to hours. This is reflected in the large variability of the brains response to transcranial magnetic stimulation (TMS). However, robust and interpretable biomarkers of the brains current excitability state are lacking. ObjectiveWe investigated whether entropy derived from singular value decomposition of short electroencephalography (EEG) segments could serve as a biomarker of cortical excitability as probed by TMS. MethodsEntropy was computed from 1-second EEG segments preceding single-pulse TMS applied over the motor cortex. We assessed whether spontaneous fluctuations in pre-pulse entropy predicted trial-by-trial variability in TMS-induced motor-evoked potentials (MEPs). Additionally, we evaluated whether entropy tracked stimulation-induced changes in cortical excitability. ResultsHigher pre-pulse entropy, particularly over frontal regions, was associated with larger MEP amplitudes. TMS locally increased entropy over the motor cortex, while entropy decreased in other regions during the intervention. Participants who showed greater local increases in entropy from pre-to post-intervention also demonstrated larger increases in corticospinal excitability. ConclusionEntropy derived from short EEG segments reflects both intrinsic and stimulation-induced changes in cortical excitability. This marker may help optimize TMS interventions by informing brain state-dependent stimulation strategies and providing an index of intervention efficacy.

neuroscience↗

bioRxiv2025

fCUT&Tag-seq: An Optimized CUT&Tag-based Method for High-Resolution Histone Modification and Chromatin-Binding Protein Profiling in both Model and Plant Pathogenic Fungi

Histone modifications and chromatin-binding proteins play crucial roles in regulating gene expression in eukaryotes, with significant implications for fungal pathogenicity and development. However, profiling these modifications or proteins across the genome in fungi remains challenging due to the technical limitations of the traditional, widely used ChIP-Seq method. Here, we present an optimized CUT&Tag-Seq protocol (fCUT&Tag-Seq) specifically designed for filamentous fungi and dimorphic fungi. Our approach involves the preparation of protoplasts and nuclear extraction to enhance antibody accessibility, along with formaldehyde crosslinking to improve protein-DNA binding efficiency. We then successfully applied fCUT&Tag-Seq to accurately profile multiple histone modifications like H3K9me3, H3K27me3, H3K4me3, and H3K18ac, across different plant pathogenic or model fungal species, including Verticillium dahliae, Neurospora crassa, Fusarium graminearum, and Sporisorium scitamineum, showing good signal-to-noise ratios, reproducibility, and detection sensitivity. Furthermore, we extended this method to profile chromatin-binding proteins, such as the histone acetyltransferase Gcn5. This study establishes fCUT&Tag-Seq as a robust and useful tool for fungal epigenetic research, enabling detailed exploration of chromatin dynamics and advancing our understanding of fungal gene regulation, development, and pathogenicity. Impact StatementWe developed a faster, lowLJinput method to study how genes are turned on and off in fungi, even in tough species that are difficult to analyze with standard methods. Our new approach, named fCUT&Tag-Seq, requires only 10,000 cells and can be completed in just two days. It delivers clearer, more reliable results and has already been successfully applied to multiple fungi, including crop pathogens. By revealing the molecular "control switches" that govern fungal development and virulence, we expect it will accelerate basic research and help identify new targets for controlling destructive plant diseases.

microbiology↗

bioRxiv2025

Metagenomic estimation of absolute bacterial biomass in the mammalian gut through host-derived read normalization.

Absolute bacterial biomass estimation in the human gut is crucial for understanding microbiome dynamics and host-microbe interactions. Current methods for quantifying bacterial biomass in stool, such as flow cytometry, qPCR, or spike-ins (i.e., adding cells or DNA from an organism not normally found in a sample), can be labor-intensive, costly, and confounded by factors like water content, DNA extraction efficiency, PCR inhibitors, and other technical challenges that add bias and noise. We propose a simple, cost-effective approach that circumvents some of these technical challenges: directly estimating bacterial biomass from metagenomes using bacterial-to-host (B:H) read ratios. We compare B:H ratios to the standard methods outlined above, demonstrating that B:H ratios are useful proxies for bacterial biomass in stool and possibly in other host-associated substrates. We show how B:H ratios can be used to track antibiotic treatment response and recovery in both mice and humans, which showed 403-fold and 45-fold reductions in bacterial biomass during antibiotic treatment, respectively. Our results indicate that host and bacterial metagenomic DNA fractions in human stool fluctuate longitudinally around a stable mean in healthy individuals, and the average host read fraction varies across healthy individuals by < 8-9 fold. B:H ratios offer a convenient alternative to other absolute biomass quantification methods, without the need for additional measurements, experimental design considerations, or machine learning algorithms, enabling retrospective absolute biomass estimates from existing stool metagenomic data.

microbiology↗

bioRxiv2024

A CRISPR/SpCas9M-reporting system for efficient and rapid genome editing in Caulobacter crescentus

As members of the -proteobacteria group, Caulobacter crescentus and its relatives are known for their asymmetric life cycle and comprehensive applications in gene delivery, agricultural biotechnology, and the production of high-value compounds. However, genetic manipulations of these bacteria are often time-consuming and labor-intensive due to the lack of efficient genome editing tools. Here, we report a practical CRISPR/SpCas9M-reporting system that overcomes the limitations of SpCas9 expression, enabling efficient, markerless, and rapid genome editing in C. crescentus. As a demonstration, we successfully knocked out two genes encoding the scaffold proteins, achieving apparent editing efficiencies up to 80%. Key components, including the Cas protein, Cas inducer, sgRNA, homologous arms, and reporter, were systematically analyzed and optimized to enhance the editing efficiency or decrease the cell lethality. A nearly zero off-target ratio was observed after the curing of the editor plasmid in editing strains. Furthermore, we applied the CRISPR/SpCas9M-reporting system to two C. crescentus relatives, Agrobacterium fabrum and Sinorhizobium meliloti, establishing it as an efficient and reliable editing strategy. We anticipate that this system could be applied to other hard-to-edit organisms, accelerating both basic and applied research in -proteobacteria.

bioengineering↗

bioRxiv2024

G3BP1 Maintains Vascular Endothelial Barrier Integrity by Negatively Regulating the MYD88-ARNO-ARF6 Signaling Pathway

Endothelial permeability is essential for vascular function. This process is regulated by intercellular junctions, including adherens junctions (AJs) and tight junctions (TJs), which form the endothelial barrier and dynamically connect adjacent cells. However, the mechanisms that control endothelial permeability are not fully understood. We investigated the role of the RNA-binding protein G3BP1 in regulating endothelial permeability. We examined the effects of Loss of g3bp1 in conditional knockout mice and primary human HUVEC cells. We analyzed endothelial barrier integrity and permeability, focusing on AJ and TJ expression, under both normal and LPS-induced inflammatory conditions. Loss of g3bp1 in vascular endothelial cells decreased AJ and TJ expression, compromised barrier integrity, and increased permeability. These effects were exacerbated under LPS-induced inflammatory conditions. Loss of g3bp1 also increased the expression of MYD88, ARNO, and ARF6, and enhanced ARF6 activity. Mechanistically, G3BP1 bound to and stabilized MYD88 mRNA, negatively regulating the MYD88-ARNO-ARF6 signaling pathway. Inhibiting this pathway by reducing MYD88 or ARF6 expression, or inhibiting ARNO activity, restored AJ/TJ expression and barrier function in G3BP1-deficient models. Our findings identify G3BP1 as a novel regulator of vascular endothelial permeability. G3BP1 acts by negatively regulating the MYD88-ARNO-ARF6 signaling pathway. Targeting G3BP1 may be a potential therapeutic strategy for diseases associated with endothelial barrier dysfunction.

physiology↗

bioRxiv2024

Effect of fumarate and live yeast on methane emissions, rumen fermentation, blood metabolites, and lactation performance in dairy goats

The objective of this study was to evaluate the effects and combined effects of fumarate (32 g/d) and active dry yeast (ADY) (1.5 g/d) on methane emissions, rumen fermentation, microbial community and function, apparent total tract digestibility, blood metabolites, and lactation performance in 28 lactating goats using a randomized complete block design with a 2 x 2 factorial arrangement. Fumarate supplementation inhibited methane emission rates for 3 h post-feeding, while ADY increased the methane emission rates for 2 h pre-feeding, without combined effects between them. Fumarate increased the rumen pH and reduced total VFA concentration without changing the VFA profiles before feeding. Based on two custom dataset of 4,674 metagenome-assembled genomes and 7,758,615 nonredundant genes, neither fumarate nor live yeast influenced the rumen microbial community and function. The addition of fumarate reduced the concentrations of glucose, serum malondialdehyde (MDA), and ammonia in the serum, as well as the activity of insulin, while increasing the activity of serum malate dehydrogenase (MDH) and the levels of urea in both serum and milk. The addition of live yeast reduced serum glucose levels and increased serum MDA concentrations and MUN levels. There was a negative interaction between fumaric acid and live yeast on the levels of urea nitrogen in serum and milk. ConclusionsThe methane inhibition effect of fumaric acid persists for 3 h post-feeding, reflecting its retention or metabolism time in the rumen. Fumaric acid enhances the TCA cycle and the urea cycle in dairy goats, thereby improving energy utilization efficiency and reducing the risk of ammonia toxicity.

microbiology↗

bioRxiv2024

Discovery of a Heparan-Sulfate Binding Domain in Monkeypox Virus H3 Protein as a New Anti-poxviral Drug Target

Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged -helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.

biophysics↗

bioRxiv2024

StereoMM: A Graph Fusion Model for Integrating Spatial Transcriptomic Data and Pathological Images

Spatially resolved omics technologies generating multimodal and high-throughput data lead to the urgent need for advanced analysis to allow the biological discoveries by comprehensively utilizing information from multi-omics data. The H&E image and spatial transcriptomic data indicate abundant features which are different and complementary to each other. AI algorithms can perform nonlinear analysis on these aligned or unaligned complex datasets to decode tumoral heterogeneity for detecting functional domain. However,the interpretability of AI-generated outcomes for human experts is a problem hindering application of multi-modal analysis in clinic. We presented a machine learning based toolchain called StereoMM, which is a graph fusion model that can integrate gene expression, histological images, and spatial location. StereoMM firstly performs information interaction on transcriptomic and imaging features through the attention module, guaranteeing explanations for its decision-making processes. The interactive features are input into the graph autoencoder together with the graph of spatial position, so that multimodal features are fused in a self-supervised manner. Here, StereoMM was subjected to mouse brain tissue, demonstrating its capability to discern fine tissue architecture, while highlighting its advantage in computational speed. Utilizing data from Stereo-seq of human lung adenosquamous carcinoma and 10X Visium of human breast cancer, we showed its superior performance in spatial domain recognition over competing software and its ability to reveal tumor heterogeneity. The fusion approach for imaging and gene expression data within StereoMM aids in the more accurate identification of domains, unveils critical molecular features, and elucidates the connections between different domains, thereby laying the groundwork for downstream analysis.

bioinformatics↗

bioRxiv2024

PhaSeDis: A Manually Curated Database of Phase Separation-Disease Associations and Corresponding Small Molecules

Biomacromolecules form membraneless organelles through liquid-liquid phase separation in order to regulate the efficiency of particular biochemical reactions. Dysregulation of phase separation might result in pathological condensation or sequestration of biomolecules, leading to diseases. Thus, phase separation and phase separating factors may serve as drug targets for disease treatment. Nevertheless, such associations have not yet been integrated into phase separation related databases. Therefore, based on MloDisDB, a database for membraneless organelle factor-disease association previously developed by our lab, we constructed PhaSeDis, the phase separation-disease association database. We increased the number of phase separation entries from 52 to 185, and supplemented the evidence provided by the original article verifying the phase separation nature of the factors. Moreover, we included the information of interacting small molecules with low or high-throughput evidence that might serve as potential drugs for phase separation entries. PhaSeDis strives to offer comprehensive descriptions of each entry, elucidating how phase separating factors induce pathological conditions via phase separation and the mechanisms by which small molecules intervene. We believe that PhaSeDis would be very important in the application of phase separation regulation in treating related diseases. PhaSeDis is available at http://mlodis.phasep.pro.

bioinformatics↗

bioRxiv2024

Anti-drift pose tracker (ADPT): A transformer-based network for robust animal pose estimation cross-species

Deep learning-based methods have advanced animal pose estimation, enhancing accuracy and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compromise reliability. Here, we present the Anti-Drift Pose Tracker (ADPT), a transformer-based tool that mitigates tracking drift in behavioral analysis. Extensive experiments across cross-species datasets--including proprietary mouse and monkey recordings and public Drosophila and macaque datasets--demonstrate that ADPT significantly reduces drift and surpasses existing models like DeepLabCut and SLEAP in accuracy. Moreover, ADPT achieved 93.16% identification accuracy for 10 unmarked mice and 90.36% accuracy for freely interacting unmarked mice, which can be further refined to 99.72%, enhancing both anti-drift performance and pose estimation accuracy in social interactions. With its end-to-end design, ADPT is computationally efficient and suitable for real-time analysis, offering a robust solution for reproducible animal behavior studies. The ADPT code is available at https://github.com/tangguoling/ADPT.

neuroscience↗

bioRxiv2024

Optimizing Accuracy and Efficiency in Analyzing Non-UMI Liquid Biopsy Datasets Using the Sentieon ctDNA Pipeline

Sequencing clinical liquid biopsy, especially circulating tumor DNA (ctDNA), provides a valuable method for identifying low allele frequency tumor variants, opening novel clinical applications, particularly in treatment selection for late-stage cancer patients. Despite advancements, challenges in assay development persist, primarily due to limited sample volumes and insufficiency of reads supporting low allele frequency variants. The allele frequencies of clinically significant variants often hover close to the threshold of errors introduced by PCR and sequencing processes. Therefore, more sophisticated analysis methods are crucial to further reduce base error rates, enabling accurate discrimination between background errors and genuine somatic variants. While several ctDNA analysis pipelines have been published and adopted, there is room for improvement in terms of accuracy and run efficiency. In this study, we introduce Sentieons innovative consensus-based ctDNA pipeline - a rapid and precise solution for calling small somatic variants from non-UMI ctDNA sequencing data. The pipeline comprises four core modules: alignment, consensus generation, variant calling, and variant filtering. Through benchmarking with in-vitro and real clinical datasets, we observed that the Sentieon ctDNA pipeline exhibits higher accuracy compared to alternative methods.

bioinformatics↗