Biology subjects

Zheng, S.

Publications and source records attributed to Zheng, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Rational molecular design for improved ZHD101 thermal stability based on the introduction of disulfide bonds at the dimer interface and B-factor analysis

AbstractZearalenone hydrolase 101 (ZHD101), derived from Clonostachys rosea, is known to effectively degrade the main contaminant (zearalenone) in animal feed, but the thermal instability of ZHD101 limits its industrial application. In this study, we successfully enhanced the thermal stability of ZHD101 through two iterative rounds of rational molecular design. First, after the prediction of disulfide bond sites, ZHD101T229C was obtained, and a new disulfide bond was formed between two single ZHD101 subunits to construct a dimer ZHD101. Then, based on ZHD101T229C, two high-vibration hotspot amino acids N137 and D170 were selected by analyzing atomic position fluctuations and dynamic information A small and precise mutant library containing three mutants (ZHD101T229C/N137L, ZHD101T229C/D170L, and ZHD101T229C/D170C) was obtained by saturation mutagenesis and calculation of binding energy in silico. Compared with the wild type, the thermal half-inactivation temperature (T50) of ZHD101T229C/D170C increased by 7{degrees}C, its half-life (t1/2) increased by 200% at 50{degrees}C, and its melting temperature (Tm) increased by 18.1{degrees}C. Molecular docking suggested that new covalent bond formation and shorter bond distance may contribute to the improved thermal stability of ZHD101. The rational design strategy proposed in this work can provide a reference for the thermal stability modification and optimization of other proteins. ImportanceZearalenone (ZEN) is a nonsteroidal estrogenic mycotoxin that poses a significant threat to animal feed safety. ZEN hydrolase 101 (ZHD101) catalyzes the conversion of ZEN into a nontoxic product, offering a promising detoxification strategy. However, the thermal instability of ZHD101 severely limits its industrial application. In this study, the thermal stability of ZHD101 was significantly improved through two rounds of rational design, resulting in the ZHD101T229C/D170C mutant. ZHD101T229C/D170C exhibited the improved half-inactivation temperature (T50), half-life (t1/2) and the highest melting temperature (Tm) reported thus far. Overall, the iterative combinatiorial mutation strategy involved the introduction of a disulfide bond between two single subunits and the application of B-factor analysis to identify hotspot amino acids residues. This approach effectively enhanced the enzymes structural stability, paving the way for its industrial application. ZHD101T229C/D170C and the rational design strategy presented in this work provide a robust framework for the thermal stability optimization of similar biocatalysts, advancing the practical use of ZHD101 in the feed industry.

molecular biology↗

bioRxiv2024

BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions

Protein-protein interactions are crucial for drug discovery and understanding biological mechanisms. Despite significant advances in predicting the structures of protein complexes, led by AlphaFold3, determining the strength of these interactions accurately remains a challenge. Traditional low-throughput experimental methods do not generate sufficient data for comprehensive benchmarking or training deep learning models. Deep mutational scanning (DMS) experiments provide rich, high-throughput data; however, they are often used incompletely, neglecting to consider the binding partners, and on a per-study basis without assessing the generalization capabilities of fine-tuned models across different assays. To address these limitations, we collected over ten million raw DMS data points and refined them to half a million high-quality points from twenty-five assays, focusing on protein-protein interactions. We intentionally excluded non-PPI DMS data pertaining to intrinsic protein properties, such as fluorescence or catalytic activity. Our dataset meticulously pairs binding energies with the sequences and structures of all interacting partners using a comprehensive pipeline, recognizing that interactions inherently involve at least two proteins. This curated dataset serves as a foundation for benchmarking and training the next generation of deep learning models focused on protein-protein interactions, thereby opening the door to a plethora of high-impact applications including understanding cellular networks and advancing drug target discovery and development.

biophysics↗

bioRxiv2024

Scalable emulation of protein equilibrium ensembles with generative deep learning

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental techniques and molecular dynamics (MD) simulations can, in principle, determine conformational states, binding configurations and their probabilities, but suffer from low throughput. Here we develop a Biomolecular Emulator (BioEmu), a generative deep learning system that can generate thousands of statistically independent samples from the protein structure ensemble per hour on a single graphical processing unit. By leveraging novel training methods and vast data of protein structures, over 200 milliseconds of MD simulation, and experimental protein stabilities, BioEmus protein ensembles represent equilibrium in a range of challenging and practically relevant metrics. Qualitatively, BioEmu samples many functionally relevant conformational changes, ranging from formation of cryptic pockets, over unfolding of specific protein regions, to large-scale domain rearrangements. Quantitatively, BioEmu samples protein conformations with relative free energy errors around 1 kcal/mol, as validated against millisecond-timescale MD simulation and experimentally-measured protein stabilities. By simultaneously emulating structural ensembles and thermodynamic properties, BioEmu reveals mechanistic insights, such as the causes for fold destabilization of mutants, and can efficiently provide experimentally-testable hypotheses.

molecular biology↗

bioRxiv2024

C2AM-Unet: Coordinate and Channel Attention Mixing Flexible Architecture for Retinal Vessel Segmentation

Accurate retinal Microvascular segmentation demands a balanced combination of anatomical fidelity and hemodynamic relevance. However, existing methods fall short in preserving critical structures such as capillary junctions and bifurcations, thus limiting clinical applications and causing fragmentation. To address these limitations, we propose DFMS-Net, a novel dualfield segmentation framework that synergistically integrates geometric-field modeling--achieved through the Spatial Pathway Extractor (SPE) and Transformer-based Topology Interaction (TTI) for preserving structural continuity--and functional-field optimization--realized by the Semantic Attention Amplification (SAA) module for enhancing semantic visibility--via a unified Dual-Field Hemo-dynamic Attention (DFHA) mechanism. This core module enables joint enhancement of vessel continuity, accurate resolution of complex branching patterns, and recovery of low-contrast capillaries, all under physiological guidance. By co-optimizing geometric and functional cues within a unified attention learning paradigm, DFMS-Net produces segmentations that are both morphologically accurate and hemodynamically plausible. Furthermore, we propose two specialized variants using a streamlined Double SPE Attention for vessel continuity refinement. To address the directionality-dependent nature of structural damage in retinal ischemia and glaucoma, we propose Variant-1--a streamlined architecture that emphasizes dual-stage directional refinement to enhance trajectory coherence and improve the detection of topological disruptions, while Variant-2 supports high-resolution analysis of capillary dropout in early diabetic retinopathy through detailed microvascular recovery. Extensive experiments on retinal (DRIVE, STARE) and coronary angiography (DCA1, CHUAC) datasets demonstrate that DFMS-Net achieves state-of-theart performance. Meanwhile, its strong generalization capability offers a promising foundation for diagnosing both retinal and cardiovascular diseases. The code will be available at https://github.com/699zjl/DFMS-Net-new.

bioinformatics↗

bioRxiv2024

SARM1 Activation Promotes Axonal Degeneration Via a Two-Step Liquid-to-Solid Phase Transition

SARM1 protein plays a central role in axonal degeneration, a key process in many neurodegenerative diseases and nerve injuries. It mediates this by depleting axonal NAD+ through its NADase activity, catalyzed by the TIR domain. Normally, this activity is kept in an inactive state and becomes activated in response to various neuronal damage signals. However, the molecular mechanism behind SARM1 activation, particularly how activation is restricted to damaged axons, remains to be fully elucidated. In this study, using a class of pyridine-containing compounds that induce SARM1-dependent cell death and axonal degeneration, we reveal a two-step process of SARM1 activation. The first step involves sub-lethal "priming" activation of SARM1 mediated by the NAD+ precursor NMN, leading to the formation of covalent conjugates between the hydrolyzed product of NAD+, adenosine diphosphate ribose (ADPR), and the compounds catalyzed by the intrinsic base exchange activity of SARM1. In the second step, these ADPR-conjugates act as molecular glues, promoting the formation of self-proliferating superhelical filaments. On these filaments, SARM1s TIR domains adopt an active NADase configuration. As the superhelical filaments rapidly reach their limit of solubility, they precipitate out of the liquid phase as condensates with stable, fully activated NAD hydrolysis activity. Interestingly, we found that a series of reported SARM1 inhibitors currently under clinical development, which target the TIR enzymatic domain, can paradoxically activate SARM1s NADase activity via this mechanism. These findings provide new insights into how SARM1 activation is spatially restricted to damaged axons and offer important implications for the development of therapeutics targeting SARM1.

molecular biology↗

bioRxiv2024

YY1 Enhances the Stability of HIF-1α Protein by Interacting with NUSAP1 in Macrophages within the Prostate Cancer Microenvironment

Immune checkpoint therapy for prostate cancer (PCa) has failed in clinical trials; however, the precise underlying mechanisms involved remain elusive. PCa, a classic "immune-cold" tumor, is characterized by an immunosuppressive tumor microenvironment. Within this milieu, macrophages, the predominant immune cell population, have a propensity to infiltrate the hypoxic zones of tumors. In a previous study, we showed that Yin Yang 1 (YY1) is highly expressed in macrophages in PCa tissues. Here, through multiplexed imaging mass cytometry (IMC) of a PCa tissue microarray, we further demonstrate that YY1+ macrophages aggregate in hypoxic areas of tumors and that hypoxia promotes the phase separation of YY1 in the nucleus by increasing YY1 tyrosine phosphorylation in macrophages. Furthermore, YY1 binds to NUSAP1 and promotes the SUMOylation of HIF-1, which promotes phase separation and stabilization of the HIF-1 protein. We also demonstrated that either treatment with a small molecule inhibitor (tenapanor) to decrease the YY1-NUSAP1-HIF-1 interaction or myeloid-specific YY1 gene knockout impairs subcutaneous PCa tumor formation. Furthermore, we present a first-generation tetrahedral DNA nanostructure (TDN) based on the proteolysis targeting chimera (PROTAC) technique, named YY1-DcTAC, which targets and degrades YY1 in tumor-associated macrophages. In a PCa mouse model, YY1-DcTAC exhibited prolonged drug efficacy, robust macrophage-specific responsiveness, potent antitumor effects, and increased CD8+ T cell tumor infiltration. In summary, our findings underscore the pivotal role of YY1 within the hypoxia/HIF-1 pathway in tumor-associated macrophages and affirm the therapeutic potential of targeting YY1 for treating PCa.

cancer biology↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗

bioRxiv2024

Navigating the Unstructured by Evaluating AlphaFold's Efficacy in Predicting Missing Residues and Structural Disorder in Proteins

This study explored the difference between predicted structure confidence and disorder detection in protein, focusing on regions with undefined structures detected as missing segments in X-ray crystallography and Cryo-EM data. Recognizing the importance of these unstructured regions for protein functionality, we examined the alignment of numerous protein sequences with their resolved or not structures. The research utilized a comprehensive PDB dataset, classifying residues into modeled, hard missing and soft missing based on their visibility in structural data. By analysis, key features were firstly determined, including confidence score pLDDT from Al-phaFold2, an advanced AI-based tool, and IUPred, a conventional disorder prediction method. Our analysis reveals that "hard missing" residues often reside in low-confidence regions, but are not exclusively associated with disorder predictions. It was assessed how effectively individual key features can distinguish between structured and unstructured data, as well as the potential benefits of combining these features for advanced machine learning applications. This approach aims to uncover varying correlations across different experimental methodologies in the latest structural data. By analyzing the relationships between predictions and experimental structures, we can more effectively identify structural targets within proteins, guiding experimental designs toward areas of potential functional significance, whether they exhibit high stability or crucial unstructured regions.

bioinformatics↗

bioRxiv2024

Directing cellular transitions on gene graph-enhanced cell state manifold

A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. To address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical, and cytokine perturbations across diverse cell types, conditions, and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression, and drug response. CellNavi represents a substantial advancement in driver gene prediction and cell state manipulation, opening new avenues in disease biology and therapeutic discovery.

cell biology↗

bioRxiv2024

Chromosomal instability promotes cell migration and invasion via EFEMP1 in extracellular vesicles

Triple-negative breast cancer (TNBC) is characterised by high rates of chromosomal instability (CIN) and a tumour microenvironment (TME) modulated by extracellular vesicles (EVs). To assess how CIN might affect the TME in TNBC, we studied the EV landscape of TNBC cell lines with induced CIN. We find that CIN leads to increased secretion of EVs and that these EVs promote cell migration of recipient cells. EVs are enriched for extracellular matrix (ECM) proteins, including EFEMP1. Indeed, modulation of EFEMP1 levels in EVs significantly alters migration behaviour of EV-treated cells. We show that EFEMP1 expression is regulated by STAT1, and that EVs from STAT1-deficient cells no longer promote migration, which can be rescued by overexpression of EFEMP1 in STAT1-null cells. Xenografting TNBC cells with EFEMP1 enriched cells promotes migration in zebrafish embryos, suggesting that EFEMP1 expression is a factor that promotes metastasis. Together our results uncover a novel role for CIN in shaping the TME of TNBC and identify EFEMP1 as a potential therapeutic target to prevent cell migration within the TME.

cancer biology↗

bioRxiv2024

Uncovering pre-sensitizing agents to FLT3 inhibitors in acute myeloid leukemia with ReSisTrace lineage tracing

While FLT3 inhibitors have significantly improved the treatment of aggressive FLT3-mutated acute myeloid leukemia (AML), the emergence of resistance remains as a major challenge. Here, we applied our recently developed single-cell lineage-tracing method ReSisTrace to identify cells that are pre-resistant or pre-sensitive to FLT3 inhibitors midostaurin and quizartinib in FLT3-ITD-positive AML. By comparing the gene expression profiles of these cells, we unraveled the transcriptional pre-resistance signatures, including G1 to S phase transition 1 (GSPT1) gene. Targeting GSPT1 with the small molecule CC-90009 exhibited strong synergistic effect when combined with FLT3 inhibitors in the FLT3-ITD-mutated MOLM-13 and MV4-11 cell lines and primary AML patient samples. Further, we identified novel compounds that induced transcriptomic changes opposite to the pre-resistance signatures, thereby driving cells to FLT3 inhibitor-sensitive states. Vistusertib (mTOR inhibitor), linsitinib (IGF1R and insulin receptor inhibitor), and meisoindigo (IGF1R and Src family kinase inhibitor), all inhibiting pathways parallel to or downstream of oncogenic FLT3 signaling, were predicted and validated to pre-sensitize the FLT3-ITD-mutated cell lines and primary cells to FLT3 inhibitors. Collectively, these findings demonstrate the validity of our lineage-tracing method in unveiling pre-existing transcriptional features of treatment vulnerability in hematological cancers, and elucidate novel strategies for enhancing FLT3 inhibitor treatment efficacy in FLT3-ITD-positive AML by preventing the emergence of treatment resistance.

cancer biology↗

bioRxiv2024

PDS5s control the Arabidopsis 3D genome by suppressing the formation of TAD-like domains

One key structural element in the three-dimensional (3D) chromatin organization is the Topologically Associating Domain (TAD), which facilitates the formation of distinct chromatin compartments and fosters specific chromatin interactions. Similar chromatin compartments, known as TAD-like domains, have been identified in many plant species. However, the model plant Arabidopsis thaliana has been an exception. In this study, we address this long-standing issue by presenting evidence that Arabidopsis PDS5 proteins play a crucial role in preventing the formation of TAD-like domains.

molecular biology↗

bioRxiv2024

"Magnetic Sand" : Illusions of Interactivity

We found a series of new illusions, in which actions performed near a random white-noise display lead to the perception that the display is altered interactively with the observers actions. The perceptions resemble interactions with a box of magnetic sand, where the hand can leave traces, or attract and repulse grains in its vicinity. 1) the observer puts a finger very close to a dynamic noise display, slowly moving as though drawing a letter or a shape. A trace appears left in the fingers path, decaying within 500 ms or so. 2) When the observer moves their palm toward and away from the display, opening and closing their fingers as if grabbing and releasing grains of sand, the random dots appear as though they were magnetically attracted to or repelled by the fingers. 3) When an open hand close to the display is slowly moved back and forth laterally, the nearby dots appear to get attracted to or captured by the fingers and thus appear to move with them. 4) The same kind of action capture occurs even when the hand is not visible, moving behind the display. These illusions are robust across a wide range of parameters, including frame rate, luminance contrast, dot size (spatial frequency), and finger movement factors. Inter-subject variability is not correlated across illusion types, and the illusions also diverge in behavior across dynamic and static noise conditions. This indicates that multiple mechanisms are involved to different extents across illusions. Several known visual motion detectors and other low-level mechanisms may be involved in seeding the perceptual phenomena. However, a complete explanation would require mechanisms of action capture, whereby the internal model of the persons actions and their predicted consequences organizes visual attention and processing of the random stimulus components.

neuroscience↗

bioRxiv2024

Ultradian rhythms of CRHPVN neuron activity, behaviour and stress hormone secretion

The stress axis is always active, even in the absence of any threat. This manifests as hourly pulses of corticosteroid stress hormone secretion over the day. Corticotropin-releasing hormone neurons in the paraventricular nucleus of the hypothalamus (CRHPVN) control both the neuroendocrine stress axis as well as stress-associated behaviours. However, it is currently unclear how the resting activity of these neurons is coordinated with both spontaneous behaviour and ultradian pulses of corticosteroid secretion. To investigate this, we performed fiber photometry recordings of CRHPVN neuron activity in Crh-Ires-Cre mice and a newly generated line of Crh-Ires-Cre rats. In both mice and rats, CRHPVN neurons displayed an ultradian rhythm of activity with reoccurring upstates of activity approximately once per hour over the 24-hour day. Upstates in activity were coordinated with increases in animal activity/arousal. Chemogenetic activation of CRHPVN neurons was also sufficient to induce behavioural arousal. In rats, increases in CRH neural activity preceded some pulses of corticosteroid secretion but not others. Thus, while CRHPVN neurons display an ultradian rhythm of activity over the 24-hour day that is coordinated with behavioural arousal, the relationship between CRHPVN activity and pulses of corticosteroid secretion is not one-to-one. Significance StatementUltradian rhythms are present in many biological systems, however, the underlying neural mechanisms responsible for generating these rhythms is unclear. Corticotropin-releasing hormone (CRH) neurons control corticosteroid levels in the body as well as arousal and behaviour. Here we show that CRH neurons exhibit a pronounced ultradian rhythm in neural activity over the 24-hour day. CRH neural activity was highly correlated with, and predictive of, behavioral arousal. However, CRH neural activity was not as well correlated with pulses of corticosteroid secretion. Together, these data revel the patterns of CRH neuron activity over the 24-hour day and their relationship with behaviour and stress hormone secretion.

neuroscience↗

bioRxiv2024

Senescent cell heterogeneity and responses to senolytic treatment are related to cell cycle status during cell growth arrest.

Cellular senescence has been strongly linked to aging and age-related diseases. It is well established that the phenotype of senescent cells is highly heterogeneous and influenced by their cell type and senescence-inducing stimulus. Recent single-cell RNA-sequencing studies identified heterogeneity within senescent cell populations. However, proof of functional differences between such subpopulations is lacking. To identify functionally distinct senescent cell subpopulations, we employed high-content image analysis to measure senescence marker expression in primary human endothelial cells and fibroblasts. We found that G2-arrested senescent cells feature higher senescence marker expression than G1-arrested senescent cells. To investigate functional differences, we compared IL-6 secretion and response to ABT263 senolytic treatment in G1 and G2 senescent cells. We determined that G2-arrested senescent cells secrete more IL-6 and are more sensitive to ABT263 than G1-arrested cells. We hypothesize that cell cycle dependent DNA content is a key contributor to the heterogeneity within senescent cell populations. This study demonstrates the existence of functionally distinct senescent subpopulations even in culture. This data provides the first evidence of selective cell response to senolytic treatment among senescent cell subpopulations. Overall, this study emphasizes the importance of considering the senescent cell heterogeneity in the development of future senolytic therapies.

cell biology↗

bioRxiv2024

Shiba: A unified computational method for robust identification of differential RNA splicing across platforms

Alternative pre-mRNA splicing (AS) is a fundamental regulatory process that generates transcript diversity and cell type variation. We developed Shiba, a comprehensive method that integrates transcript assembly, splicing event identification, read counting, and differential splicing analysis across RNA-seq platforms. Shiba excels in capturing annotated and unannotated AS events with superior accuracy, sensitivity, and reproducibility. It addresses the often-overlooked issue of junction read imbalance, significantly reducing false positives to aid target prioritization and downstream analyses. Unlike other tools that require large numbers of biological replicates or resulting in low sensitivity and high false positives, Shibas statistics framework is agnostic to sample size, as demonstrated by simulated data and its effective application to real n=1 RNA-seq datasets. To extend its utility to single-cell RNA-seq, we developed scShiba, which applies Shibas pseudobulk approach to analyze splicing at the cluster level. scShiba successfully revealed AS regulation in developmental dopaminergic neurons and differences between excitatory and inhibitory neurons. Both Shiba and scShiba are available in Docker/Singularity containers and Snakemake pipelines, ensuring reproducibility. With their comprehensive capabilities, Shiba and scShiba enable systematic quantification of alternative splicing events across various platforms, laying a solid foundation for mechanistic exploration of the functional complexity in RNA splicing.

bioinformatics↗

bioRxiv2024

AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions

AlphaFold3 has set the new state-of-the-art in predicting protein-protein complex structures. However, the complete picture of biomolecular interactions cannot be fully captured by static structures alone. In the field of protein engineering and antibody discovery, the connection from structure to function is often mediated by binding energy. This work benchmarks AlphaFold3 against SKEMPI, a commonly used binding energy dataset. We demonstrate that AlphaFold3 learns unique information and synergizes with force field, profile-based, and other deep learning methods in predicting the mutational effects on protein-protein interactions. We hypothesize that AlphaFold3 captures a more global effect of mutations by learning a smoother energy landscape, but it lacks the modeling of full atomic details that are better addressed by force field methods, which possess a more rugged energy landscape. Integrating both approaches could be an interesting future direction. All of our benchmark results are openly available at https://github.com/luwei0917/AlphaFold3_PPI.

biophysics↗

bioRxiv2024

IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearmans correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong.

bioinformatics↗