bioRxiv - Bioinformatics最新文献

{"title":"Brownian motion data augmentation: a method to push neural network performance on nanopore sensors","authors":"Javier Kipen, Joakim Jaldén","doi":"10.1101/2024.09.10.612270","DOIUrl":"https://doi.org/10.1101/2024.09.10.612270","url":null,"abstract":"Nanopores are highly sensitive sensors that have achieved commercial success in DNA/RNA sequencing, with potential applications in protein sequencing and biomarker identification. Solid-state nanopores, in particular, face challenges such as instability and low signal-to-noise ratios (SNRs), which lead scientists to adopt data-driven methods for nanopore signal analysis, although data acquisition remains restrictive. In this paper, we augment training samples by simulating virtual Brownian motion based on dynamic models in the literature. We apply this method to a publicly available dataset of a classification task containing nanopore reads of DNA with encoded barcodes. A neural network named QuipuNet was previously published for this dataset, and we demonstrate that our augmentation method produces a noticeable increase in QuipuNets accuracy. Furthermore, we introduce a novel neural network named YupanaNet, which achieves greater accuracy (95.8%) than QuipuNet (94.6%) on the same dataset. YupanaNet benefits from both the enhanced generalization provided by Brownian motion data augmentation and the incorporation of novel architectures, including skip connections and a self-attention mechanism.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico study of selected alkaloids as dual inhibitors of β and γ-Secretases for Alzheimer's disease","authors":"Isreal Onifade, Haruna Umar, Abdullahi Aborode, Aeshah Awaji, Ifeoluwapo Jegede, Bunmi Adeleye, Dorcas Fatoba","doi":"10.1101/2024.09.10.612359","DOIUrl":"https://doi.org/10.1101/2024.09.10.612359","url":null,"abstract":"Alzheimer's disease (AD) has become ubiquitous as the number of elderly individuals increases and has been conceived as a socioeconomic problem lately. To date, no success is recorded for disease-modifying therapies for AD but only drugs for symptomatic relief exist. Research has been centered on the role of β-amyloid on the pathogenesis of AD, which has led to the development of drugs that target Aβ (β and γ-Secretase inhibitors) to reduce the amount of Aβ formed. However, the existing β and γ-Secretase inhibitors were associated with harmful side effects, low efficacy, and inability to cross the blood-brain barrier. Thus, in this current study, 54 alkaloids from the PhytoHub server (phytohub.eu), and two approved drugs were docked against β- Secretases. Additionally, galantamine and 5 alkaloids with the utmost binding potential with β-secretase were subjected to pharmacokinetics evaluation and docked against γ-secretase. From our results, 5 compounds displayed for both docking periods, with demissidine, solasodine, tomatidine, and solanidine having better BE than the control drugs. Based on the pharmacokinetics evaluation, 4 compounds possessed good pharmacokinetic evaluation and biological activities than galantamine. This study suggests that demissidine, solasodine, tomatidine, and solanidine are promising dual inhibitors against β and γ-Secretase proteins in silico. However, there is an urgent need to carry out in vitro and in vivo experiments on these new leads to validate the findings of this study.\u0000Keywords: β- and γ-Secretase inhibitors, Alzheimer's disease, alkaloids, Virtual docking","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping and Modeling Age-Related Changes in Intrinsic Neural Timescales","authors":"Kaichao Wu, Leonardo Lyra Gollo","doi":"10.1101/2024.09.10.612380","DOIUrl":"https://doi.org/10.1101/2024.09.10.612380","url":null,"abstract":"Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Normal aging, often associated with cognitive deficits, involves progressive neuronal and synaptic loss, shaping brain structure and dynamics. The impact of these structural changes on temporal integration coding in the aged brain remains largely unknown. To address this gap, we mapped differences in intrinsic timescales and gray matter volume (GMV) within brain regions using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on a visual discrimination task was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related changes in GMV and intrinsic timescales, we propose an age-dependent model of spiking neuron networks. In younger subjects, brain regions were modeled in a near-critical branching regime, while in elderly subjects, regions had fewer neurons and synapses, pushing the dynamics toward a more subcritical regime. The empirical results were reproduced by the model: Neuronal networks representing brain regions in young subjects exhibited longer intrinsic timescales due to critical slowing down. These findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing future interventions for cognitive decline.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and Validation of Mitophagy-Related Genes in Diabetic Retinopathy","authors":"Wenxuan Peng, Yulin Zou","doi":"10.1101/2024.09.10.612286","DOIUrl":"https://doi.org/10.1101/2024.09.10.612286","url":null,"abstract":"Background: Diabetic retinopathy is one of the common chronic complications of diabetes, characterized by retinal microvascular and neurodegenerative impairment,and it is the primary cause of vision impairment and blindness in adults. Many studies have demonstrated that mitophagy plays a significant role in the pathological mechanism of DR. However, its mechanism is not yet fully clear and requires further research.\u0000Methods: We obtained relevant datasets of diabetic retinopathy from the GEO database and used R language to screen for differentially expressed genes. We intersected these genes with mitophagy-related genes and identified differentially expressed mitophagy-related genes. We performed GO and KEGG analysis on the differentially expressed mitophagy-related genes, followed by PPI network analysis. Using Cytoscape software, we selected mitophagy hub genes. Finally, we further validated the expression of the mitophagy hub genes in an in vitro cell culture high-glucose model using quantitative real-time polymerase chain reaction (qRT-PCR).\u0000Results: We identified 27 differentially expressed genes related to mitophagy by using R language, with 10 genes upregulated and 17 genes downregulated. We performed GO and KEGG enrichment analysis using R software to further study the potential biological functions of differentially expressed genes. Through PPI network analysis and Cytoscape software, we selected 10 hub genes associated with mitophagy. Finally, through qRT-PCR validation of these 10 hub genes, we found that the mRNA expression differences of MFN1, BNIP3L, GABARAPL1, and PINK1 genes were consistent with our bioinformatics analysis results.\u0000Conclusion: We consider that MFN1, BNIP3L, GABARAPL1, and PINK1 may serve as potential biomarkers for diabetic retinopathy.The upregulation and downregulation of these genes provide new insights for further exploration of the role of mitophagy in the pathological mechanism of diabetic retinopathy.These genes can serve as new potential therapeutic targets for the treatment of diabetic retinopathy.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Molecular Pretraining via Localized Geometric Generation","authors":"Yuancheng Sun, Kai Chen, Kang Liu, Qiwei Ye","doi":"10.1101/2024.09.10.612249","DOIUrl":"https://doi.org/10.1101/2024.09.10.612249","url":null,"abstract":"Self-supervised learning on 3D molecular structures is gaining importance in data-driven scientific research and applications due to the high costs of annotating biochemical data. However, the strategic selection of semantic units for modeling 3D molecular structures remains underexplored, despite its crucial role in effective pretraining - a concept well-established in language processing and computer vision. We introduce Localized Geometric Generation (LEGO), a novel approach that treats tetrahedrons within 3D molecular structures as fundamental building blocks, leveraging their geometric simplicity and widespread presence across chemical functional patterns. Inspired by masked modeling, LEGO perturbs tetrahedral local structures and learns to reconstruct them in a self-supervised manner. Experimental results demonstrate LEGO consistently enhances molecular representations across biochemistry and quantum property prediction benchmarks. Additionally, the tetrahedral modeling and pretraining generalize from small molecules to larger molecular systems, validating by protein-ligand affinity prediction. Our results highlight the potential of selecting semantic units to build more expressive and interpretable neural networks for scientific AI applications.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Protein Half-lives from Amino Acid Sequences by Protein Language Models","authors":"Tatsuya Sagawa, Eisuke Kanao, Kosuke Ogata, Koshi Imami, Yasushi Ishihama","doi":"10.1101/2024.09.10.612367","DOIUrl":"https://doi.org/10.1101/2024.09.10.612367","url":null,"abstract":"We developed a protein half-life prediction model, PLTNUM, based on a protein language model using an extensive dataset of protein sequences and protein half-lives from the NIH3T3 mouse embryo fibroblast cell line as a training set. PLTNUM achieved an accuracy of 71% on validation data and showed robust performance with an ROC of 0.73 when applied to a human cell line dataset. By incorporating Shapley Additive Explanations (SHAP) into PLTNUM, we identified key factors contributing to shorter protein half-lives, such as cysteine-containing domains and intrinsically disordered regions. Using SHAP values, PLTNUM can also predict potential degron sequences that shorten protein half-lives. This model provides a platform for elucidating the sequence dependency of protein half-lives, while the uncertainty in predictions underscores the importance of biological context in influencing protein half-lives.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DRED: A Comprehensive Database of Genes Related to Repeat Expansion Diseases","authors":"Qingqing Shi, Min Dai, Yingke Ma, Jun Liu, Xiuying Liu, Xiu-Jie Wang","doi":"10.1101/2024.09.07.611652","DOIUrl":"https://doi.org/10.1101/2024.09.07.611652","url":null,"abstract":"Expansion of tandem repeats in genes often causes severe neuromuscular diseases, such as fragile X syndrome, huntington's disease, and spinocerebellar ataxia. However, information on genes associated with repeat expansion diseases is scattered throughout the literature, systematic prediction of potential genes that may cause diseases via repeat expansion is also lacking. Here, we develop DRED, a Database of genes related to Repeat Expansion Diseases, as a manually-curated database that covers all known 61 genes related to repeat expansion diseases reported in PubMed and OMIM, along with detailed repeat information for each gene. DRED also includes 516 genes with the potential to cause diseases via repeat expansion, which were predicted basing on their repeat composition, genetic variations, genomic features, and disease associations. Various types of information on repeat expansion diseases and their corresponding genes/repeats are presented in DRED, together with links to external resources, such as NCBI and ClinVar. DRED provides user-friendly interfaces with comprehensive functions, and can serve as a central data resource for basic research and repeat expansion disease-related medical diagnosis. DRED is freely accessible at http://omicslab.genetics.ac.cn/dred, and is frequently updated to include newly reported genes related to repeat expansion diseases.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring cell differentiation maps from lineage tracing data","authors":"Palash Sashittal, Richard Zhang, Benjamin Law, Alexander Strzalkowski, Henri Schmidt, Adriano Bolondi, Michelle Chan, Benjamin Raphael","doi":"10.1101/2024.09.09.611835","DOIUrl":"https://doi.org/10.1101/2024.09.09.611835","url":null,"abstract":"During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta, that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"bayesReact: Expression-coupled regulatory motif analysis detects microRNA activity in cancer and at the single cell level","authors":"Asta Mannstaedt Rasmussen, Alexandre Bouchard-Cote, Jakob Skou Pedersen","doi":"10.1101/2024.09.10.612047","DOIUrl":"https://doi.org/10.1101/2024.09.10.612047","url":null,"abstract":"Motivation:\u0000Regulatory constraints are crucial in maintaining tissue and cell integrity, and play important roles during developmental processes and environmental responses. Yet many regulatory mechanisms remain unobserved at the single-cell level and statistical inference may, in some cases, help elucidate their condition-specific activity and perturbation during disease progression. Results:\u0000We introduce bayesReact (BAYESian modeling of Regular Expression ACTivity), a generative model of motif occurrence across experimentally ranked sequences to infer motif-based regulatory activities. The method is evaluated for microRNAs (miRNAs), which perform post-transcriptional regulation through target mRNA destabilization and translational repression. Inferred miRNA activities positively correlate with the observed miRNA expressions in primary tumors from The Cancer Genome Atlas (TCGA) and mouse stem cells. The top miRNA activity profiles are as informative for TCGA cancer-type cluster identification as the top miRNA or mRNA expression profiles. The activity captures tissue-specific miRNA patterns observed in the matched expression, e.g., the expression of miR-122-5p in the liver and miR-124-3p in low-grade gliomas (LGG). We observe a negative association between the activity of the two miRNAs and their target gene expressions, including between the miR-124-3p activity and the anti-neuronal REST expression in LGG. bayesReact outperforms the existing method, miReact, on sparse count data, and shows a higher correlation with the miRNA expression in single-cell data. The method recovers temporal activities of prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the large Sfmbt2 cluster (miR-297-669). The bayesReact model is probabilistic and quantifies the uncertainty of all provided estimates. It is unsupervised and permits screens of bulk or single-cell data to identify condition-specific regulatory motif candidates. It further improves miRNA activity inference in single-cell data. Availability and implementation:\u0000bayesReact is implemented as an R-package, uses a Hamiltonian Monte-Carlo sampler for posterior approximation, and is available at https://github.com/astamr/bayesReact.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"niiv: Fast Self-supervised Neural Implicit Isotropic Volume Reconstruction","authors":"Jakob Troidl, Yiqing Liang, Johanna Beyer, Mojtaba Tavakoli, Johann Georg Danzl, Markus Hadwiger, Hanspeter Pfister, James Tompkin","doi":"10.1101/2024.09.07.611785","DOIUrl":"https://doi.org/10.1101/2024.09.07.611785","url":null,"abstract":"Three-dimensional (3D) microscopy data often is anisotropic with significantly lower resolution (up to 8x) along the z axis than along the xy axes. Computationally generating plausible isotropic resolution from anisotropic imaging data would benefit the visual analysis of large-scale volumes. This paper proposes niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data that can quickly produce images at arbitrary (continuous) output resolutions. Within a neural field, the representation embeds a learned latent code that describes the implicit higher-resolution isotropic image region. Under isotropic volume assumptions, we self-supervise this representation on low-/high-resolution lateral image pairs to reconstruct an isotropic volume from low-resolution axial images. We evaluate our method on simulated and real anisotropic electron (EM) and light microscopy (LM) data. Compared to a state-of-the-art diffusion-based method, niiv shows improved reconstruction quality (+1dB PSNR) and is over three orders of magnitude faster (2,000x) to infer. Specifically, niiv reconstructs a 128^3 voxel volume in 1/10th of a second, renderable at varying (continuous) high resolutions for display.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}