Methods最新文献

{"title":"SITP: A single cell bioinformatics analysis flow captures proteasome markers in the development of breast cancer","authors":"Xue-Jie Zhou ,&nbsp;Xiao-Feng Liu ,&nbsp;Xin Wang ,&nbsp;Xu-Chen Cao","doi":"10.1016/j.ymeth.2024.11.011","DOIUrl":"10.1016/j.ymeth.2024.11.011","url":null,"abstract":"<div><div>Single cell sequencing and related databases have been widely used in the exploration of cancer occurrence and development, but there is still no in-depth explanation of specific and complicated cellular protein modification processes. Ubiquitin-Proteasome System (UPS), as a specific and precise protein modification and degradation process, plays an important role in the biological functions of cancer cell proliferation and apoptosis. Proteasomes, vital multi-catalytic proteinases in eukaryotic cells, play a crucial role in protein degradation and contribute to tumor regulation. The 26S proteasome, part of the ubiquitin–proteasome system. In this study, we have enrolled a common SITP process including analysis of single cell sequencing to elucidate a flow that can capture typical proteasome markers in the oncogenesis and progression of breast cancer. PSMD11, a key component of the 26S proteasome regulatory particle, has been identified as a critical survival factor in cancer cells. Results suggest that PSMD11’s rapid degradation is linked to acute apoptosis in cancer cells, making it a potential target for cancer treatment. Our study explored the potential mechanisms of PSMD11 in breast cancer development. The findings revealed the feasibility of disclosing ubiquitinating biomarkers from public database, as well as presented new evidence supporting PSMD11 as a potential therapeutic biomarker for breast cancer.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"233 ","pages":"Pages 1-10"},"PeriodicalIF":4.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643675","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":"Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer.","authors":"Chengxin He, Zhenjiang Zhao, Xinye Wang, Huiru Zheng, Lei Duan, Jie Zuo","doi":"10.1016/j.ymeth.2024.11.010","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.11.010","url":null,"abstract":"<p><p>Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643674","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":"Ab-amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model.","authors":"Yuwei Zhou, Wenwen Liu, Chunmei Luo, Ziru Huang, Gunarathne Samarappuli Mudiyanselage Savini, Lening Zhao, Rong Wang, Jian Huang","doi":"10.1016/j.ymeth.2024.11.005","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.11.005","url":null,"abstract":"<p><p>Therapeutic antibodies have emerged as a promising treatment option for a wide range of diseases. However, the light chain of antibodies can potentially induce amyloidosis, a condition characterized by protein misfolding and aggregation, posing a significant safety concern. Therefore, it is crucial to assess the amyloidogenic risk of therapeutic antibodies during the early stages of drug development. In this study, we introduce AB-Amy 2.0, a new computational model with enhanced performance for assessing the light chain amyloidogenic risk of therapeutic antibodies. By employing pretrained protein language models (PLMs) embeddings, AB-Amy 2.0 achieves higher accuracy in amyloidogenic risk prediction compared with traditional features offering a crucial tool for early-stage identification of antibodies with low aggregation propensity. The AB-Amy 2.0 was trained on antiBERTy embeddings and utilizes the SVM algorithm, resulting in superior performance metrics. On an independent test dataset, the model achieved high sensitivity, specificity, ACC, MCC and AUC of 93.47%, 89.23%, 91.92%, 0.8261 and 0.9739, respectively. These results highlight the effectiveness and robustness of AB-Amy 2.0 in predicting light chain amyloidogenic risk accurately. To facilitate user-friendly access, we have developed an online web server (http://i.uestc.edu.cn/AB-Amy2) and a command line tool (https://github.com/zzyywww/ABAmy2). These resources enable the broader application of this advanced model and promise to enhance the development of safer therapeutic antibodies.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643649","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":"Data Preprocessing Methods for Selective Sweep Detection using Convolutional Neural Networks.","authors":"Hanqing Zhao, Nikolaos Alachiotis","doi":"10.1016/j.ymeth.2024.11.003","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.11.003","url":null,"abstract":"<p><p>The identification of positive selection has been framed as a classification task, with Convolutional Neural Networks (CNNs) already outperforming summary statistics and likelihood-based approaches in accuracy. Despite the prevalence of CNN-based methods that manipulate the pixels of images representing raw genomic data as a preprocessing step to improve classification accuracy, the efficacy of these pixel-rearrangement techniques remains inadequately examined, particularly in the presence of confounding factors like population bottlenecks, migration and recombination hotspots. We introduce a set of pixel rearrangement algorithms aimed at enhancing CNN classification accuracy in detecting selective sweeps. These algorithms are employed to assess the performance of four CNN models for selective sweep detection. Our findings illustrate that the judicious application of rearrangement algorithms notably enhances the overall classification accuracy of a CNN across various datasets simulating confounding factors. We observed that sorting the columns of the genomic matrices has higher on CNN performance than rearranging the sequences. To some extent, these rearrangement algorithms are more robust to misspecified demographic models compared with the utilization of the default preprocessing algorithm as suggested by the respective authors of each CNN architecture. We provide the data rearrangement algorithms as a distinct package available for download at: https://github.com/Zhaohq96/Genetic-data-rearrangement.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643673","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":"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":"SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks.","authors":"Fangqin Zhang, Zhan Shen, Siyi Huang, Yuan Zhu, Ming Yi","doi":"10.1016/j.ymeth.2024.11.006","DOIUrl":"https://doi.org/10.1016/j.ymeth.2024.11.006","url":null,"abstract":"<p><p>Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, the identification of spatial domains at the single-cell level remains a significant challenge in elucidating biological processes. To address this, SpaInGNN was developed, a sophisticated graph neural network (GNN) framework that accurately delineates spatial domains by integrating spatial location data, histological information, and gene expression profiles into low-dimensional latent embeddings. Additionally, to fully leverage spatial coordinate data, spatial integration using graph neural network (SpaInGNN) refines the graph constructed for spatial locations by incorporating both tissue image distance and Euclidean distance, following a pre-clustering of gene expression profiles. This refined graph is then embedded using a self-supervised GNN, which minimizes self-reconfiguration loss. By applying SpaInGNN to refined graphs across multiple consecutive tissue slices, this study mitigates the impact of batch effects in data analysis. The proposed method demonstrates substantial improvements in the accuracy of spatial domain recognition, providing a more faithful representation of the tissue organization in both mouse olfactory bulb and human lateral prefrontal cortex samples.</p>","PeriodicalId":390,"journal":{"name":"Methods","volume":" ","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611449","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}