Briefings in Functional Genomics最新文献_第4页

A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data. 针对单细胞和空间转录组学数据的降维和聚类方法综合调查。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-06-11 DOI: 10.1093/bfgp/elae023

Yidi Sun, Lingling Kong, Jiayi Huang, Hongyan Deng, Xinling Bian, Xingfeng Li, Feifei Cui, Lijun Dou, Chen Cao, Quan Zou, Zilong Zhang

{"title":"A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data.","authors":"Yidi Sun, Lingling Kong, Jiayi Huang, Hongyan Deng, Xinling Bian, Xingfeng Li, Feifei Cui, Lijun Dou, Chen Cao, Quan Zou, Zilong Zhang","doi":"10.1093/bfgp/elae023","DOIUrl":"https://doi.org/10.1093/bfgp/elae023","url":null,"abstract":"In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302188","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}

引用次数: 0

Editorial for BFG special issue: Computational genomics for precision medicine and personalized healthcare. BFG 特刊编辑：精准医学和个性化医疗的计算基因组学。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-06-11 DOI: 10.1093/bfgp/elae021

Tapabrata Chakraborti, Subhadip Basu

引用次数: 0

Exploring the impact of N4-acetylcytidine modification in RNA on non-neoplastic disease: unveiling its role in pathogenesis and therapeutic opportunities. 探索 RNA 中 N4-乙酰胞嘧啶修饰对非肿瘤性疾病的影响：揭示其在发病机制中的作用和治疗机会。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-06-06 DOI: 10.1093/bfgp/elae020

Keyu Wan, Tiantian Nie, Wenhao Ouyang, Yunjing Xiong, Jing Bian, Ying Huang, Li Ling, Zhenjun Huang, Xianhua Zhu

引用次数: 0

Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (Sesamum indicum L.). 芝麻基因组网络资源（SesameGWR）：芝麻（Sesamum indicum L.）非生物和生物胁迫反应转录组特征的完善注释数据资源。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-06-04 DOI: 10.1093/bfgp/elae022

Himanshu Avashthi, Ulavappa Basavanneppa Angadi, Divya Chauhan, Anuj Kumar, Dwijesh Chandra Mishra, Parimalan Rangan, Rashmi Yadav, Dinesh Kumar

{"title":"Sesame Genomic Web Resource (SesameGWR): a well-annotated data resource for transcriptomic signatures of abiotic and biotic stress responses in sesame (Sesamum indicum L.).","authors":"Himanshu Avashthi, Ulavappa Basavanneppa Angadi, Divya Chauhan, Anuj Kumar, Dwijesh Chandra Mishra, Parimalan Rangan, Rashmi Yadav, Dinesh Kumar","doi":"10.1093/bfgp/elae022","DOIUrl":"https://doi.org/10.1093/bfgp/elae022","url":null,"abstract":"Sesame (Sesamum indicum L.) is a globally cultivated oilseed crop renowned for its historical significance and widespread growth in tropical and subtropical regions. With notable nutritional and medicinal attributes, sesame has shown promising effects in combating malnutrition cancer, diabetes, and other diseases like cardiovascular problems. However, sesame production faces significant challenges from environmental threats such as charcoal rot, drought, salinity, and waterlogging stress, resulting in economic losses for farmers. The scarcity of information on stress-resistance genes and pathways exacerbates these challenges. Despite its immense importance, there is currently no platform available to provide comprehensive information on sesame, which significantly hinders the mining of various stress-associated genes and the molecular breeding of sesame. To address this gap, here a free, web-accessible, and user-friendly genomic web resource (SesameGWR, http://backlin.cabgrid.res.in/sesameGWR/) has been developed This platform provides key insights into differentially expressed genes, transcription factors, miRNAs, and molecular markers like simple sequence repeats, single nucleotide polymorphisms, and insertions and deletions associated with both biotic and abiotic stresses.. The functional genomics information and annotations embedded in this web resource were predicted through RNA-seq data analysis. Considering the impact of climate change and the nutritional and medicinal importance of sesame, this study is of utmost importance in understanding stress responses. SesameGWR will serve as a valuable tool for developing climate-resilient sesame varieties, thereby enhancing the productivity of this ancient oilseed crop.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238358","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}

引用次数: 0

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning. 利用多模态深度学习从组织病理学图像预测胃癌肿瘤突变负荷

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad032

Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang

{"title":"Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.","authors":"Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang","doi":"10.1093/bfgp/elad032","DOIUrl":"10.1093/bfgp/elad032","url":null,"abstract":"Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9965530","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}

引用次数: 0

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. 基于 Omics 的深度学习方法用于肺癌决策和疗法开发。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad031

Thi-Oanh Tran, Thanh Hoa Vo, Nguyen Quoc Khanh Le

{"title":"Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.","authors":"Thi-Oanh Tran, Thanh Hoa Vo, Nguyen Quoc Khanh Le","doi":"10.1093/bfgp/elad031","DOIUrl":"10.1093/bfgp/elad031","url":null,"abstract":"Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10281428","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}

引用次数: 0

Widespread transcriptomic alterations of transient receptor potential channel genes in cancer. 癌症中瞬时受体电位通道基因的广泛转录组变化。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad023

Tao Pan, Yueying Gao, Gang Xu, Lei Yu, Qi Xu, Jinyang Yu, Meng Liu, Can Zhang, Yanlin Ma, Yongsheng Li

引用次数: 0

Mapping of long stretches of highly conserved sequences in over 6 million SARS-CoV-2 genomes. 在 600 多万个 SARS-CoV-2 基因组中绘制高度保守的长序列图。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad027

Akhil Kumar, Rishika Kaushal, Himanshi Sharma, Khushboo Sharma, Manoj B Menon, Vivekanandan P

{"title":"Mapping of long stretches of highly conserved sequences in over 6 million SARS-CoV-2 genomes.","authors":"Akhil Kumar, Rishika Kaushal, Himanshi Sharma, Khushboo Sharma, Manoj B Menon, Vivekanandan P","doi":"10.1093/bfgp/elad027","DOIUrl":"10.1093/bfgp/elad027","url":null,"abstract":"We identified 11 conserved stretches in over 6.3 million SARS-CoV-2 genomes including all the major variants of concerns. Each conserved stretch is ≥100 nucleotides in length with ≥99.9% conservation at each nucleotide position. Interestingly, six of the eight conserved stretches in ORF1ab overlapped significantly with well-folded experimentally verified RNA secondary structures. Furthermore, two of the conserved stretches were mapped to regions within the S2-subunit that undergo dynamic structural rearrangements during viral fusion. In addition, the conserved stretches were significantly depleted for zinc-finger antiviral protein (ZAP) binding sites, which facilitated the recognition and degradation of viral RNA. These highly conserved stretches in the SARS-CoV-2 genome were poorly conserved at the nucleotide level among closely related β-coronaviruses, thus representing ideal targets for highly specific and discriminatory diagnostic assays. Our findings highlight the role of structural constraints at both RNA and protein levels that contribute to the sequence conservation of specific genomic regions in SARS-CoV-2.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9824704","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}

引用次数: 0

Integration of hybrid and self-correction method improves the quality of long-read sequencing data. 混合和自校正方法的整合提高了长读数测序数据的质量。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad026

Tao Tang, Yiping Liu, Binshuang Zheng, Rong Li, Xiaocai Zhang, Yuansheng Liu

引用次数: 0

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. DeepCMI：基于图的模型，可准确预测具有多种信息的 circRNA-miRNA 相互作用。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad030

Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang

{"title":"DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.","authors":"Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang","doi":"10.1093/bfgp/elad030","DOIUrl":"10.1093/bfgp/elad030","url":null,"abstract":"Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291543","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}

引用次数: 0