Methods最新文献

{"title":"MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning","authors":"Wei Peng ,&nbsp;Zhihao Zhang ,&nbsp;Wei Dai ,&nbsp;Zhihao Ping ,&nbsp;Xiaodong Fu ,&nbsp;Li Liu ,&nbsp;Lijun Liu ,&nbsp;Ning Yu","doi":"10.1016/j.ymeth.2024.11.001","DOIUrl":"10.1016/j.ymeth.2024.11.001","url":null,"abstract":"<div><div>Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integrating<!--> <!-->the<!--> <!-->gene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methods<!--> <!-->in cell classification. In this work, we propose MVCLST, a multi-view comparative learning<!--> <!-->method to analyze spatial transcriptomics<!--> <!-->data for accurate cell type classification. MVCLST<!--> <!-->constructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to cluster<!--> <!-->the learned features<!--> <!-->for cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomics<!--> <!-->data analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. It<!--> <!-->also outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulb<!--> <!-->data.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 115-128"},"PeriodicalIF":4.2,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring causal relationships among histone modifications in exon skipping event","authors":"Pengmian Feng ,&nbsp;Yuanfang Tian ,&nbsp;Wei Chen","doi":"10.1016/j.ymeth.2024.11.008","DOIUrl":"10.1016/j.ymeth.2024.11.008","url":null,"abstract":"<div><div>Alternative splicing is a crucial process of gene expression. Over 90% multi-exonic genes in human genome undergo alternative splicing. Although the splicing code has been proposed, it still couldn’t satisfactorily explain the tissue-specific alternative splicing. Results of co-transcriptional RNA processing analysis demonstrated that, except for trans- and cis-acting elements, histone modifications also play a role in alternative splicing. In the present work, we analyzed the associations among 27 kinds of histone modifications in H1 human embryonic stem cell. In order to illustrate the casual relationships between histone modification and alternative splicing, we built the Bayesian network and validated its robustness by using cross validation test. In addition to the combinatorial patterns, distinct histone modification patterns were also observed in the alternative spliced exons and surrounding intron regions, indicating that histone modifications could substantially mark alternative splicing.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 89-95"},"PeriodicalIF":4.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"dsRNAPredictor-II: An improved predictor of identifying dsRNA and its silencing efficiency for Tribolium castaneum based on sequence length distribution","authors":"Liping Xu,&nbsp;Jia Zheng,&nbsp;Yetong Zhou,&nbsp;Cangzhi Jia","doi":"10.1016/j.ymeth.2024.11.007","DOIUrl":"10.1016/j.ymeth.2024.11.007","url":null,"abstract":"<div><div>RNA interference (RNAi) has been widely utilized to investigate gene functions and has significant potential for control of pest insects. However, recent studies have revealed that the target insect species, dsRNA molecule length, target genes, and other experimental factors can affect the efficiency of RNAi mediated control, restricting the further development and application of this technology. Therefore, the aim of this study was to establish a deep learning model using bioinformatics to help researchers identify dsRNA fragments with the highest RNAi efficiency. In this study, we optimized an existing model, namely, dsRNAPredictor, by designing sub-models based on different sequence lengths. Accordingly, the data were divided into two groups: 130–399 bp and 400–616 bp long sequences. Then, one-hot encoding was employed to extract sequence information. The convolutional neural network framework comprising three convolutional layers, three average pooling layers, a flattened layer, and three dense layers was employed as the classifier. By adjusting the parameters, we established two sub-models for different sequence distributions. Using multiple independent test datasets and conducting hypothesis testing, we demonstrated that our model exhibits superior performance and strong robustness to dsRNAPredictor, respectively. Therefore, our model may help design dsRNAs with pre-screening potential and facilitate further research and applications.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 129-138"},"PeriodicalIF":4.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of YY1 loop anchor based on multi-omics features","authors":"Jun Ren ,&nbsp;Zhiling Guo ,&nbsp;Yixuan Qi ,&nbsp;Zheng Zhang ,&nbsp;Li Liu","doi":"10.1016/j.ymeth.2024.11.004","DOIUrl":"10.1016/j.ymeth.2024.11.004","url":null,"abstract":"<div><div>The three-dimensional structure of chromatin is crucial for the regulation of gene expression. YY1 promotes enhancer-promoter interactions in a manner analogous to CTCF-mediated chromatin interactions. However, little is known about which YY1 binding sites can form loop anchors. In this study, the LightGBM model was used to predict YY1-loop anchors by integrating multi-omics data. Due to the large imbalance in the number of positive and negative samples, we use AUPRC to reflect the quality of the classifier. The results show that the LightGBM model exhibits strong predictive performance (<span><math><mrow><mi>A</mi><mi>U</mi><mi>P</mi><mi>R</mi><mi>C</mi><mo>≥</mo><mn>0.93</mn></mrow></math></span>). To verify the robustness of the model, the dataset was divided into training and test sets at a 4:1 ratio. The results show that the model performs well for YY1-loop anchor prediction on both the training and independent test sets. Additionally, we ranked the importance of the features and found that the formation of YY1-loop anchors is primarily influenced by the co-binding of transcription factors CTCF, SMC3, and RAD21, as well as histone modifications and sequence context.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 96-106"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HistoSPACE: Histology-inspired spatial transcriptome prediction and characterization engine","authors":"Shivam Kumar,&nbsp;Samrat Chatterjee","doi":"10.1016/j.ymeth.2024.11.002","DOIUrl":"10.1016/j.ymeth.2024.11.002","url":null,"abstract":"<div><div>Spatial transcriptomics (ST) enables the visualization of gene expression within the context of tissue morphology. This emerging discipline has the potential to serve as a foundation for developing tools to design precision medicines. However, due to the higher costs and expertise required for such experiments, its translation into a regular clinical practice might be challenging. Despite implementing modern deep learning to enhance information obtained from histological images using AI, efforts have been constrained by limitations in the diversity of information. In this paper, we developed a model, HistoSPACE, that explores the diversity of histological images available with ST data to extract molecular insights from tissue images. Further, our approach allows us to link the predicted expression with disease pathology. Our proposed study built an image encoder derived from a universal image autoencoder. This image encoder was connected to convolution blocks to build the final model. It was further fine-tuned with the help of ST-Data. The number of model parameters is small and requires lesser system memory and relatively lesser training time. Making it lightweight in comparison to traditional histological models. Our developed model demonstrates significant efficiency compared to contemporary algorithms, revealing a correlation of 0.56 in leave-one-out cross-validation. Finally, its robustness was validated through an independent dataset, showing similar prediction with predefined disease pathology. Our code is available at <span><span>https://github.com/samrat-lab/HistoSPACE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 107-114"},"PeriodicalIF":4.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications","authors":"Quang-Hien Kha ,&nbsp;Ngan Thi Kim Nguyen ,&nbsp;Nguyen Quoc Khanh Le ,&nbsp;Jiunn-Horng Kang","doi":"10.1016/j.ymeth.2024.10.012","DOIUrl":"10.1016/j.ymeth.2024.10.012","url":null,"abstract":"<div><div>Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) models have made strides in DDI prediction, existing approaches lack specificity for oral diabetes medications and face challenges in interpretability. To address these limitations, we propose a novel ML-based framework utilizing the Simplified Molecular Input Line Entry System (SMILES) to encode structural information of oral diabetes drugs. Using this representation, we developed an XGBoost model, selecting molecular features through LASSO. Our dataset, sourced from DrugBank, included 42 oral diabetes drugs and 1,884 interacting drugs, divided into training, validation, and testing sets. The model identified 606 optimal features, achieving an F1-score of 0.8182. SHAP analysis was employed for feature interpretation, enhancing model transparency and clinical relevance. By predicting adverse DDIs, our model offers a valuable tool for clinical decision-making, aiding safer prescription practices. The 606 critical features provide insights into atomic-level interactions, linking computational predictions with biological experiments. We present a classification model specifically designed for predicting DDIs associated with oral diabetes medications, with an openly accessible web application to support diabetes management in multi-drug regimens and comorbidity settings.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 81-88"},"PeriodicalIF":4.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of novel digital PCR assays for the rapid quantification of Gram-negative bacteria biomarkers using RUCS algorithm","authors":"Alexandra Bogožalec Košir ,&nbsp;Špela Alič ,&nbsp;Viktorija Tomič ,&nbsp;Dane Lužnik ,&nbsp;Tanja Dreo ,&nbsp;Mojca Milavec","doi":"10.1016/j.ymeth.2024.10.011","DOIUrl":"10.1016/j.ymeth.2024.10.011","url":null,"abstract":"<div><div>Rapid and accurate identification of bacterial pathogens is crucial for effective treatment and infection control, particularly in hospital settings. Conventional methods like culture techniques and MALDI-TOF mass spectrometry are often time-consuming and less sensitive. This study addresses the need for faster and more precise diagnostic methods by developing novel digital PCR (dPCR) assays for the rapid quantification of biomarkers from three Gram-negative bacteria: <em>Acinetobacter baumannii</em>, <em>Klebsiella pneumoniae</em>, and <em>Pseudomonas aeruginosa</em>.</div><div>Utilizing publicly available genomes and the <em>rapid identification of PCR primers for unique core sequences</em> or RUCS algorithm, we designed highly specific dPCR assays. These assays were validated using synthetic DNA, bacterial genomic DNA, and DNA extracted from clinical samples. The developed dPCR methods demonstrated wide linearity, a low limit of detection (∼30 copies per reaction), and robust analytical performance with measurement uncertainty below 25 %. The assays showed high repeatability and intermediate precision, with no cross-reactivity observed. Comparison with MALDI-TOF mass spectrometry revealed substantial concordance, highlighting the methods’ suitability for clinical diagnostics.</div><div>This study underscores the potential of dPCR for rapid and precise quantification of Gram-negative bacterial biomarkers. The developed methods offer significant improvements over existing techniques, providing faster, more accurate, and SI-traceable measurements. These advancements could enhance clinical diagnostics and infection control practices.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 72-80"},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging flow cytometry reveals LPS-induced changes to intracellular intensity and distribution of α-synuclein in a TLR4-dependent manner in STC-1 cells.","authors":"Anastazja M Gorecki, Chidozie C Anyaegbu, Melinda Fitzgerald, Kathryn A Fuller, Ryan S Anderton","doi":"10.1016/j.ymeth.2024.10.009","DOIUrl":"10.1016/j.ymeth.2024.10.009","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease is a chronic neurodegenerative disorder, where pathological protein aggregates largely composed of phosphorylated α-synuclein are implicated in disease pathogenesis and progression. Emerging evidence suggests that the interaction between pro-inflammatory microbial factors and the gut epithelium contributes to α-synuclein aggregation in the enteric nervous system. However, the cellular sources and mechanisms for α-synuclein pathology in the gut are still unclear.</p><p><strong>Methods: </strong>The STC-1 cell line, which models an enteroendocrine population capable of communicating with the gut microbiota, immune and nervous systems, was treated with a TLR4 inhibitor (TAK-242) prior to microbial lipopolysaccharide (LPS) exposure to investigate the role of TLR4 signalling in α-synuclein alterations. Antibodies targeting the full-length protein (α-synuclein) and the Serine-129 phosphorylated form (pS129) were used. Complex, multi-parametric image analysis was conducted through confocal microscopy (with Zen 3.8 analysis) and imaging flow cytometry (with IDEAS® analysis).</p><p><strong>Results: </strong>Confocal microscopy revealed heterogenous distribution of α-synuclein and pS129 in STC-1 cells, with prominent pS129 staining along cytoplasmic processes. Imaging flow cytometry further quantified the relationship between various α-synuclein morphometric features. Thereafter, imaging flow cytometry demonstrated a dose-specific effect of LPS, where the low (8 μg/mL), but not high dose (32 μg/mL), significantly altered measures related to α-synuclein intensity, distribution, and localisation. Pre-treatment with a TLR4 inhibitor TAK-242 alleviated some of these significant alterations.</p><p><strong>Conclusion: </strong>This study demonstrates that LPS-TLR4 signalling alters the intracellular localisation of α-synuclein in enteroendocrine cells in vitro and showcases the utility of combining imaging flow cytometry to investigate subtle protein changes that may not be apparent through confocal microscopy alone. Further investigation is required to understand the apparent dose-dependent effects of LPS on α-synuclein in the gut epithelium in healthy states as well as conditions such as Parkinson's disease.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":"93-111"},"PeriodicalIF":4.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLFA-UNet: A multi-level feature assembly UNet for medical image segmentation","authors":"Anass Garbaz ,&nbsp;Yassine Oukdach ,&nbsp;Said Charfi ,&nbsp;Mohamed El Ansari ,&nbsp;Lahcen Koutti ,&nbsp;Mouna Salihoun","doi":"10.1016/j.ymeth.2024.10.010","DOIUrl":"10.1016/j.ymeth.2024.10.010","url":null,"abstract":"<div><div>Medical image segmentation is crucial for accurate diagnosis and treatment in medical image analysis. Among the various methods employed, fully convolutional networks (FCNs) have emerged as a prominent approach for segmenting medical images. Notably, the U-Net architecture and its variants have gained widespread adoption in this domain. This paper introduces MLFA-UNet, an innovative architectural framework aimed at advancing medical image segmentation. MLFA-UNet adopts a U-shaped architecture and integrates two pivotal modules: multi-level feature assembly (MLFA) and multi-scale information attention (MSIA), complemented by a pixel-vanishing (PV) attention mechanism. These modules synergistically contribute to the segmentation process enhancement, fostering both robustness and segmentation precision. MLFA operates within both the network encoder and decoder, facilitating the extraction of local information crucial for accurately segmenting lesions. Furthermore, the bottleneck MSIA module serves to replace stacking modules, thereby expanding the receptive field and augmenting feature diversity, fortified by the PV attention mechanism. These integrated mechanisms work together to boost segmentation performance by effectively capturing both detailed local features and a broader range of contextual information, enhancing both accuracy and resilience in identifying lesions. To assess the versatility of the network, we conducted evaluations of MFLA-UNet across a range of medical image segmentation datasets, encompassing diverse imaging modalities such as wireless capsule endoscopy (WCE), colonoscopy, and dermoscopic images. Our results consistently demonstrate that MFLA-UNet outperforms state-of-the-art algorithms, achieving dice coefficients of 91.42%, 82.43%, 90.8%, and 88.68% for the MICCAI 2017 (Red Lesion), ISIC 2017, PH2, and CVC-ClinicalDB datasets, respectively.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 52-64"},"PeriodicalIF":4.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing","authors":"Van-Nui Nguyen ,&nbsp;Thi-Xuan Tran ,&nbsp;Thi-Tuyen Nguyen ,&nbsp;Nguyen Quoc Khanh Le","doi":"10.1016/j.ymeth.2024.10.006","DOIUrl":"10.1016/j.ymeth.2024.10.006","url":null,"abstract":"<div><div>Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination site prediction tools across species, these tools mainly rely on predefined sequence features and machine learning algorithms, with species-specific variations in ubiquitination patterns remaining poorly understood. This study introduces a novel approach for predicting <em>Arabidopsis thaliana</em> ubiquitination sites using a neural network model based on knowledge distillation and natural language processing (NLP) of protein sequences. Our framework employs a multi-species “Teacher model” to guide a more compact, species-specific “Student model”, with the “Teacher” generating pseudo-labels that enhance the “Student” learning and prediction robustness. Cross-validation results demonstrate that our model achieves superior performance, with an accuracy of 86.3 % and an area under the curve (AUC) of 0.926, while independent testing confirmed these results with an accuracy of 86.3 % and an AUC of 0.923. Comparative analysis with established predictors further highlights the model’s superiority, emphasizing the effectiveness of integrating knowledge distillation and NLP in ubiquitination prediction tasks. This study presents a promising and efficient approach for ubiquitination site prediction, offering valuable insights for researchers in related fields. The code and resources are available on GitHub: <span><span>https://github.com/nuinvtnu/KD_ArapUbi</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"232 ","pages":"Pages 65-71"},"PeriodicalIF":4.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142492289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}