Briefings in bioinformatics最新文献_第6页

Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework. 利用基于 gnomAD 的东亚参考框架对 PrediXcan 预测进行跨人群增强。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae549

Han-Ching Chan, Amrita Chattopadhyay, Tzu-Pin Lu

{"title":"Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework.","authors":"Han-Ching Chan, Amrita Chattopadhyay, Tzu-Pin Lu","doi":"10.1093/bib/bbae549","DOIUrl":"10.1093/bib/bbae549","url":null,"abstract":"Over the past decade, genome-wide association studies have identified thousands of variants significantly associated with complex traits. For each locus, gene expression levels are needed to further explore its biological functions. To address this, the PrediXcan algorithm leverages large-scale reference data to impute the gene expression level from single nucleotide polymorphisms, and thus the gene-trait associations can be tested to identify the candidate causal genes. However, a challenge arises due to the fact that most reference data are from subjects of European ancestry, and the accuracy and robustness of predicted gene expression in subjects of East Asian (EAS) ancestry remains unclear. Here, we first simulated a variety of scenarios to explore the impact of the level of population diversity on gene expression. Population differentiated variants were estimated by using the allele frequency information from The Genome Aggregation Database. We found that the weights of a variants was the main factor that affected the gene expression predictions, and that ~70% of variants were significantly population differentiated based on proportion tests. To provide insights into this population effect on gene expression levels, we utilized the allele frequency information to develop a gene expression reference panel, Predict Asian-Population (PredictAP), for EAS ancestry. PredictAP can be viewed as an auxiliary tool for PrediXcan when using genotype data from EAS subjects.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142516298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features. GGN-GO：通过多尺度结构特征预测蛋白质功能的几何图网络。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae559

Jia Mi, Han Wang, Jing Li, Jinghong Sun, Chang Li, Jing Wan, Yuan Zeng, Jingyang Gao

{"title":"GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.","authors":"Jia Mi, Han Wang, Jing Li, Jinghong Sun, Chang Li, Jing Wan, Yuan Zeng, Jingyang Gao","doi":"10.1093/bib/bbae559","DOIUrl":"10.1093/bib/bbae559","url":null,"abstract":"Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model's ability to identify key nodes (residues). To address these challenges, we propose a geometric graph network (GGN-GO) for predicting protein function that enriches feature extraction by capturing multi-scale geometric structural features at the atomic and residue levels. We use a geometric vector perceptron to convert these features into vector representations and aggregate them with node features for better understanding and propagation in the network. Moreover, we introduce a graph attention pooling layer captures key node information by adaptively aggregating local functional motifs, while contrastive learning enhances graph representation discriminability through random noise and different views. The experimental results show that GGN-GO outperforms six comparative methods in tasks with the most labels for both experimentally validated and predicted protein structures. Furthermore, GGN-GO identifies functional residues corresponding to those experimentally confirmed, showcasing its interpretability and the ability to pinpoint key protein regions. The code and data are available at: https://github.com/MiJia-ID/GGN-GO.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction. IMGT/RobustpMHC：用于 I 类 MHC 肽结合预测的稳健训练。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae552

Anjana Kushwaha, Patrice Duroux, Véronique Giudicelli, Konstantin Todorov, Sofia Kossida

{"title":"IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction.","authors":"Anjana Kushwaha, Patrice Duroux, Véronique Giudicelli, Konstantin Todorov, Sofia Kossida","doi":"10.1093/bib/bbae552","DOIUrl":"10.1093/bib/bbae552","url":null,"abstract":"The accurate prediction of peptide-major histocompatibility complex (MHC) class I binding probabilities is a critical endeavor in immunoinformatics, with broad implications for vaccine development and immunotherapies. While recent deep neural network based approaches have showcased promise in peptide-MHC (pMHC) prediction, they have two shortcomings: (i) they rely on hand-crafted pseudo-sequence extraction, (ii) they do not generalize well to different datasets, which limits the practicality of these approaches. While existing methods rely on a 34 amino acid pseudo-sequence, our findings uncover the involvement of 147 positions in direct interactions between MHC and peptide. We further show that neural architectures can learn the intricacies of pMHC binding using even full sequences. To this end, we present PerceiverpMHC that is able to learn accurate representations on full-sequences by leveraging efficient transformer based architectures. Additionally, we propose IMGT/RobustpMHC that harnesses the potential of unlabeled data in improving the robustness of pMHC binding predictions through a self-supervised learning strategy. We extensively evaluate RobustpMHC on eight different datasets and showcase an overall improvement of over 6% in binding prediction accuracy compared to state-of-the-art approaches. We compile CrystalIMGT, a crystallography-verified dataset presenting a challenge to existing approaches due to significantly different pMHC distributions. Finally, to mitigate this distribution gap, we further develop a transfer learning pipeline.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142589993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CDHu40: a novel marker gene set of neuroendocrine prostate cancer. CDHu40：神经内分泌性前列腺癌的新型标记基因集。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae471

Sheng Liu, Hye Seung Nam, Ziyu Zeng, Xuehong Deng, Elnaz Pashaei, Yong Zang, Lei Yang, Chenglong Li, Jiaoti Huang, Michael K Wendt, Xin Lu, Rong Huang, Jun Wan

{"title":"CDHu40: a novel marker gene set of neuroendocrine prostate cancer.","authors":"Sheng Liu, Hye Seung Nam, Ziyu Zeng, Xuehong Deng, Elnaz Pashaei, Yong Zang, Lei Yang, Chenglong Li, Jiaoti Huang, Michael K Wendt, Xin Lu, Rong Huang, Jun Wan","doi":"10.1093/bib/bbae471","DOIUrl":"10.1093/bib/bbae471","url":null,"abstract":"Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named CDHu40, demonstrated superior performance in distinguishing NE PCa (NEPC) and non-NEPC samples based on gene expression profiles. CDHu40 outperformed most of the other published marker sets, excelling particularly at the prognostic level. Notably, some marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11422505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Thinking points for effective batch correction on biomedical data. 对生物医学数据进行有效批量校正的思考要点。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae515

Harvard Wai Hann Hui, Weijia Kong, Wilson Wen Bin Goh

引用次数: 0

Mediation analysis in longitudinal study with high-dimensional methylation mediators. 利用高维甲基化中介因子对纵向研究进行中介分析。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae496

Yidan Cui, Qingmin Lin, Xin Yuan, Fan Jiang, Shiyang Ma, Zhangsheng Yu

{"title":"Mediation analysis in longitudinal study with high-dimensional methylation mediators.","authors":"Yidan Cui, Qingmin Lin, Xin Yuan, Fan Jiang, Shiyang Ma, Zhangsheng Yu","doi":"10.1093/bib/bbae496","DOIUrl":"https://doi.org/10.1093/bib/bbae496","url":null,"abstract":"Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142458376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs. ACLNDA：在异构图中预测非编码 RNA 与疾病关联的非对称图对比学习框架。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae533

Laiyi Fu, ZhiYuan Yao, Yangyi Zhou, Qinke Peng, Hongqiang Lyu

{"title":"ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.","authors":"Laiyi Fu, ZhiYuan Yao, Yangyi Zhou, Qinke Peng, Hongqiang Lyu","doi":"10.1093/bib/bbae533","DOIUrl":"https://doi.org/10.1093/bib/bbae533","url":null,"abstract":"Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics. We propose ACLNDA, an asymmetric graph contrastive learning framework for analyzing heterophilic ncRNA-disease associations. It constructs inter-layer adjacency matrices from the original lncRNA, miRNA, and disease associations, and uses a Top-K intra-layer similarity edges construction approach to form a triple-layer heterogeneous graph. Unlike traditional works, to account for both node attribute features (ncRNA/disease) and node preference features (association), ACLNDA employs an asymmetric yet simple graph contrastive learning framework to maximize one-hop neighborhood context and two-hop similarity, extracting ncRNA-disease features without relying on graph augmentations or homophily assumptions, reducing computational cost while preserving data integrity. Our framework is capable of being applied to a universal range of potential LDA, MDA, and LMI association predictions. Further experimental results demonstrate superior performance to other existing state-of-the-art baseline methods, which shows its potential for providing insights into disease diagnosis and therapeutic target identification. The source code and data of ACLNDA is publicly available at https://github.com/AI4Bread/ACLNDA.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: Structure prediction of linear and cyclic peptides using CABS-flex. 更正：使用 CABS-flex 预测线性和环状肽的结构。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae605

引用次数: 0

Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty. 基于加权距离惩罚的套索约束正则化高斯图形模型识别细胞类型。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae572

Wei Zhang, Yaxin Xu, Xiaoying Zheng, Juan Shen, Yuanyuan Li

{"title":"Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.","authors":"Wei Zhang, Yaxin Xu, Xiaoying Zheng, Juan Shen, Yuanyuan Li","doi":"10.1093/bib/bbae572","DOIUrl":"10.1093/bib/bbae572","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distance penalty term utilizing the Gaussian kernel function, which maps data from a low-dimensional nonlinear space to a high-dimensional linear space. We subsequently impose a Lasso constraint on the regularized Gaussian graphical model to enhance its ability to capture linear data characteristics. Additionally, we utilize the Eigengap strategy to predict the number of cell types and obtain predicted labels via spectral clustering. The experimental results on 14 test datasets demonstrate the superior clustering accuracy of the WLGG algorithm over 16 alternative methods. Furthermore, downstream analysis, including marker gene identification, pseudotime inference, and functional enrichment analysis based on the similarity matrix and predicted labels from the WLGG algorithm, substantiates the reliability of WLGG and offers valuable insights into biological dynamic biological processes and regulatory mechanisms.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection. 推进微生物诊断：以通用系统发育为指导的计算算法，为精确检测微生物寻找独特的序列。

IF 6.8 2区生物学

Briefings in bioinformatics Pub Date : 2024-09-23 DOI: 10.1093/bib/bbae545

Gulshan Kumar Sharma, Rakesh Sharma, Kavita Joshi, Sameer Qureshi, Shubhita Mathur, Sharad Sinha, Samit Chatterjee, Vandana Nunia

{"title":"Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.","authors":"Gulshan Kumar Sharma, Rakesh Sharma, Kavita Joshi, Sameer Qureshi, Shubhita Mathur, Sharad Sinha, Samit Chatterjee, Vandana Nunia","doi":"10.1093/bib/bbae545","DOIUrl":"10.1093/bib/bbae545","url":null,"abstract":"Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11497845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142495335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0