Briefings in Functional Genomics最新文献_第6页

Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies. 使用硅学方法合成和分析泛非病毒 GP-1,2 前蛋白特异性抗体。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-11 DOI: 10.1093/bfgp/elae012

Maciej Wiśniewski, Peace Babirye, Carol Musubika, Eleni Papakonstantinou, Samuel Kirimunda, Michał Łaźniewski, Teresa Szczepińska, Moses L. Joloba, Elias Eliopoulos, Erik Bongcam-Rudloff, D. Vlachakis, Anup Kumar Halder, Dariusz Plewczyński, M. Wayengera

{"title":"Use of in silico approaches, synthesis and profiling of Pan-filovirus GP-1,2 preprotein specific antibodies.","authors":"Maciej Wiśniewski, Peace Babirye, Carol Musubika, Eleni Papakonstantinou, Samuel Kirimunda, Michał Łaźniewski, Teresa Szczepińska, Moses L. Joloba, Elias Eliopoulos, Erik Bongcam-Rudloff, D. Vlachakis, Anup Kumar Halder, Dariusz Plewczyński, M. Wayengera","doi":"10.1093/bfgp/elae012","DOIUrl":"https://doi.org/10.1093/bfgp/elae012","url":null,"abstract":"Intermolecular interactions of protein-protein complexes play a principal role in the process of discovering new substances used in the diagnosis and treatment of many diseases. Among such complexes of proteins, we have to mention antibodies; they interact with specific antigens of two genera of single-stranded RNA viruses belonging to the family Filoviridae-Ebolavirus and Marburgvirus; both cause rare but fatal viral hemorrhagic fever in Africa, with pandemic potential. In this research, we conduct studies aimed at the design and evaluation of antibodies targeting the filovirus glycoprotein precursor GP-1,2 to develop potential targets for the pan-filovirus easy-to-use rapid diagnostic tests. The in silico research using the available 3D structure of the natural antibody-antigen complex was carried out to determine the stability of individual protein segments in the process of its formation and maintenance. The computed free binding energy of the complex and its decomposition for all amino acids allowed us to define the residues that play an essential role in the structure and indicated the spots where potential antibodies can be improved. Following that, the study involved targeting six epitopes of the filovirus GP1,2 with two polyclonal antibodies (pABs) and 14 monoclonal antibodies (mABs). The evaluation conducted using Enzyme Immunoassays tested 62 different sandwich combinations of monoclonal antibodies (mAbs), identifying 10 combinations that successfully captured the recombinant GP1,2 (rGP). Among these combinations, the sandwich option (3G2G12* - (rGP) - 2D8F11) exhibited the highest propensity for capturing the rGP antigen.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140716078","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

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. 多组学数据整合机器学习技术综述：精准肿瘤学的挑战与应用。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-10 DOI: 10.1093/bfgp/elae013

Debabrata Acharya, Anirban Mukhopadhyay

{"title":"A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.","authors":"Debabrata Acharya, Anirban Mukhopadhyay","doi":"10.1093/bfgp/elae013","DOIUrl":"https://doi.org/10.1093/bfgp/elae013","url":null,"abstract":"Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140719603","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

DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation DockingGA：利用变压器神经网络和遗传算法进行对接模拟，提高靶向分子生成能力

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-07 DOI: 10.1093/bfgp/elae011

Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang

{"title":"DockingGA: enhancing targeted molecule generation using transformer neural network and genetic algorithm with docking simulation","authors":"Changnan Gao, Wenjie Bao, Shuang Wang, Jianyang Zheng, Lulu Wang, Yongqi Ren, Linfang Jiao, Jianmin Wang, Xun Wang","doi":"10.1093/bfgp/elae011","DOIUrl":"https://doi.org/10.1093/bfgp/elae011","url":null,"abstract":"Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592804","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

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs 基于深度学习的长非编码 RNA 相互作用机制识别与预测综述

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-04-05 DOI: 10.1093/bfgp/elae010

Biyu Diao, Jin Luo, Yu Guo

{"title":"A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs","authors":"Biyu Diao, Jin Luo, Yu Guo","doi":"10.1093/bfgp/elae010","DOIUrl":"https://doi.org/10.1093/bfgp/elae010","url":null,"abstract":"Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body’s normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592722","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

Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study. 利用纳米孔测序鉴定关节置换术中的细菌感染：一项初步研究。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-30 DOI: 10.1093/bfgp/elae008

Hollie Wilkinson, Jamie McDonald, Helen S McCarthy, Jade Perry, Karina Wright, Charlotte Hulme, Paul Cool

{"title":"Using nanopore sequencing to identify bacterial infection in joint replacements: a preliminary study.","authors":"Hollie Wilkinson, Jamie McDonald, Helen S McCarthy, Jade Perry, Karina Wright, Charlotte Hulme, Paul Cool","doi":"10.1093/bfgp/elae008","DOIUrl":"https://doi.org/10.1093/bfgp/elae008","url":null,"abstract":"This project investigates if third-generation genomic sequencing can be used to identify the species of bacteria causing prosthetic joint infections (PJIs) at the time of revision surgery. Samples of prosthetic fluid were taken during revision surgery from patients with known PJIs. Samples from revision surgeries from non-infected patients acted as negative controls. Genomic sequencing was performed using the MinION device and the rapid sequencing kit from Oxford Nanopore Technologies. Bioinformatic analysis pipelines to identify bacteria included Basic Local Alignment Search Tool, Kraken2 and MinION Detection Software, and the results were compared with standard of care microbiological cultures. Furthermore, there was an attempt to predict antibiotic resistance using computational tools including ResFinder, AMRFinderPlus and Comprehensive Antibiotic Resistance Database. Bacteria identified using microbiological cultures were successfully identified using bioinformatic analysis pipelines. Nanopore sequencing and genomic classification could be completed in the time it takes to perform joint revision surgery (2-3 h). Genomic sequencing in this study was not able to predict antibiotic resistance in this time frame, this is thought to be due to a short-read length and low read depth. It can be concluded that genomic sequencing can be useful to identify bacterial species in infected joint replacements. However, further work is required to investigate if it can be used to predict antibiotic resistance within clinically relevant timeframes.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330337","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

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction. 系统分析基因组数据的不同生物信息学管道及其对染色质环路预测深度学习模型的影响。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-30 DOI: 10.1093/bfgp/elae009

Anup Kumar Halder, Abhishek Agarwal, Karolina Jodkowska, Dariusz Plewczynski

{"title":"A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction.","authors":"Anup Kumar Halder, Abhishek Agarwal, Karolina Jodkowska, Dariusz Plewczynski","doi":"10.1093/bfgp/elae009","DOIUrl":"https://doi.org/10.1093/bfgp/elae009","url":null,"abstract":"Genomic data analysis has witnessed a surge in complexity and volume, primarily driven by the advent of high-throughput technologies. In particular, studying chromatin loops and structures has become pivotal in understanding gene regulation and genome organization. This systematic investigation explores the realm of specialized bioinformatics pipelines designed specifically for the analysis of chromatin loops and structures. Our investigation incorporates two protein (CTCF and Cohesin) factor-specific loop interaction datasets from six distinct pipelines, amassing a comprehensive collection of 36 diverse datasets. Through a meticulous review of existing literature, we offer a holistic perspective on the methodologies, tools and algorithms underpinning the analysis of this multifaceted genomic feature. We illuminate the vast array of approaches deployed, encompassing pivotal aspects such as data preparation pipeline, preprocessing, statistical features and modelling techniques. Beyond this, we rigorously assess the strengths and limitations inherent in these bioinformatics pipelines, shedding light on the interplay between data quality and the performance of deep learning models, ultimately advancing our comprehension of genomic intricacies.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140330336","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

Understanding large scale sequencing datasets through changes to protein folding. 通过蛋白质折叠的变化理解大规模测序数据集。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-23 DOI: 10.1093/bfgp/elae007

David Shorthouse, Harris Lister, Gemma S Freeman, Benjamin A Hall

引用次数: 0

An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data. 利用单细胞 RNA-seq 数据估算细胞间通讯的改进型分层变异自动编码器。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elac056

Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang

{"title":"An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data.","authors":"Shuhui Liu, Yupei Zhang, Jiajie Peng, Xuequn Shang","doi":"10.1093/bfgp/elac056","DOIUrl":"10.1093/bfgp/elac056","url":null,"abstract":"Analysis of cell-cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell-cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand-receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9222533","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

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. 用于单细胞 RNA-seq 和空间解析转录组研究的差异表达分析的最新进展。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad011

Xiya Guo, Jin Ning, Yuanze Chen, Guoliang Liu, Liyan Zhao, Yue Fan, Shiquan Sun

引用次数: 0

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease. 整合功能评分和调控数据，预测非编码 SNPs 对一种复杂神经疾病的影响。

IF 4 3区生物学

Briefings in Functional Genomics Pub Date : 2024-03-20 DOI: 10.1093/bfgp/elad020

Daniela Felício, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M Lopes, Carolina Lemos, Nádia Pinto, Sandra Martins

{"title":"Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease.","authors":"Daniela Felício, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M Lopes, Carolina Lemos, Nádia Pinto, Sandra Martins","doi":"10.1093/bfgp/elad020","DOIUrl":"10.1093/bfgp/elad020","url":null,"abstract":"Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9918600","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