Briefings in Functional Genomics最新文献_第7页

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 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae007

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

引用次数: 0

Computational drug repurposing for viral infectious diseases: a case study on monkeypox. 病毒性传染病的计算药物再利用：猴痘案例研究。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad058

Sovan Saha, Piyali Chatterjee, Mita Nasipuri, Subhadip Basu, Tapabrata Chakraborti

引用次数: 0

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

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elad046

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 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-27 DOI: 10.1093/bfgp/elae021

Tapabrata Chakraborti, Subhadip Basu

引用次数: 0

From bench to bedside: potential of translational research in COVID-19 and beyond. 从实验室到床边:2019冠状病毒病及其他领域转化研究的潜力

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad051

Nityendra Shukla, Uzma Shamim, Preeti Agarwal, Rajesh Pandey, Jitendra Narayan

{"title":"From bench to bedside: potential of translational research in COVID-19 and beyond.","authors":"Nityendra Shukla, Uzma Shamim, Preeti Agarwal, Rajesh Pandey, Jitendra Narayan","doi":"10.1093/bfgp/elad051","DOIUrl":"10.1093/bfgp/elad051","url":null,"abstract":"The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) have been around for more than 3 years now. However, due to constant viral evolution, novel variants are emerging, leaving old treatment protocols redundant. As treatment options dwindle, infection rates continue to rise and seasonal infection surges become progressively common across the world, rapid solutions are required. With genomic and proteomic methods generating enormous amounts of data to expand our understanding of SARS-CoV-2 biology, there is an urgent requirement for the development of novel therapeutic methods that can allow translational research to flourish. In this review, we highlight the current state of COVID-19 in the world and the effects of post-infection sequelae. We present the contribution of translational research in COVID-19, with various current and novel therapeutic approaches, including antivirals, monoclonal antibodies and vaccines, as well as alternate treatment methods such as immunomodulators, currently being studied and reiterate the importance of translational research in the development of various strategies to contain COVID-19.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"349-362"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138178078","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

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms. DBPMod：用于计算识别模式生物中 DNA 结合蛋白的监督学习模型。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad039

Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Nitesh K Sharma, Aarushi Agarwal, Ajit Gupta, Rajender Parsad

{"title":"DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms.","authors":"Upendra K Pradhan, Prabina K Meher, Sanchita Naha, Nitesh K Sharma, Aarushi Agarwal, Ajit Gupta, Rajender Parsad","doi":"10.1093/bfgp/elad039","DOIUrl":"10.1093/bfgp/elad039","url":null,"abstract":"DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"363-372"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10483304","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

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning. 利用高斯噪声增强单细胞 RNA-seq 对比学习改进细胞类型识别。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad059

Ibrahim Alsaggaf, Daniel Buchan, Cen Wan

引用次数: 0

Genomics in Clinical trials for Breast Cancer. 乳腺癌临床试验中的基因组学。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad054

David Enoma

{"title":"Genomics in Clinical trials for Breast Cancer.","authors":"David Enoma","doi":"10.1093/bfgp/elad054","DOIUrl":"10.1093/bfgp/elad054","url":null,"abstract":"Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"325-334"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139038236","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

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites. DeepPRMS：预测蛋白质精氨酸甲基化位点的高级深度学习模型。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elae001

Monika Khandelwal, Ranjeet Kumar Rout

{"title":"DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.","authors":"Monika Khandelwal, Ranjeet Kumar Rout","doi":"10.1093/bfgp/elae001","DOIUrl":"10.1093/bfgp/elae001","url":null,"abstract":"Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research and drug discovery. Some experimental techniques, such as methyl-specific antibodies, chromatin immune precipitation and mass spectrometry, exist for predicting protein methylation sites, but these techniques are time-consuming and costly. The ability to predict methylation sites using in silico techniques may help researchers identify potential candidate sites for future examination and make it easier to carry out site-specific investigations and downstream characterizations. In this research, we proposed a novel deep learning-based predictor, named DeepPRMS, to identify protein methylation sites in primary sequences. The DeepPRMS utilizes the gated recurrent unit (GRU) and convolutional neural network (CNN) algorithms to extract the sequential and spatial information from the primary sequences. GRU is used to extract sequential information, while CNN is used for spatial information. We combined the latent representation of GRU and CNN models to have a better interaction among them. Based on the independent test data set, DeepPRMS obtained an accuracy of 85.32%, a specificity of 84.94%, Matthew's correlation coefficient of 0.71 and a sensitivity of 85.80%. The results indicate that DeepPRMS can predict protein methylation sites with high accuracy and outperform the state-of-the-art models. The DeepPRMS is expected to effectively guide future research experiments for identifying potential methylated protein sites. The web server is available at http://deepprms.nitsri.ac.in/.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"452-463"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139547623","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

Integrating multi-omics data to analyze the potential pathogenic mechanism of CTSH gene involved in type 1 diabetes in the exocrine pancreas. 整合多组学数据，分析外分泌胰腺CTSH基因参与1型糖尿病的潜在致病机制。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad052

Zerun Song, Shuai Li, Zhenwei Shang, Wenhua Lv, Xiangshu Cheng, Xin Meng, Rui Chen, Shuhao Zhang, Ruijie Zhang

{"title":"Integrating multi-omics data to analyze the potential pathogenic mechanism of CTSH gene involved in type 1 diabetes in the exocrine pancreas.","authors":"Zerun Song, Shuai Li, Zhenwei Shang, Wenhua Lv, Xiangshu Cheng, Xin Meng, Rui Chen, Shuhao Zhang, Ruijie Zhang","doi":"10.1093/bfgp/elad052","DOIUrl":"10.1093/bfgp/elad052","url":null,"abstract":"Type 1 diabetes (T1D) is an autoimmune disease caused by the destruction of insulin-producing pancreatic islet beta cells. Despite significant advancements, the precise pathogenesis of the disease remains unknown. This work integrated data from expression quantitative trait locus (eQTL) studies with Genome wide association study (GWAS) summary data of T1D and single-cell transcriptome data to investigate the potential pathogenic mechanisms of the CTSH gene involved in T1D in exocrine pancreas. Using the summary data-based Mendelian randomization (SMR) approach, we obtained four potential causative genes associated with T1D: BTN3A2, PGAP3, SMARCE1 and CTSH. To further investigate these genes'roles in T1D development, we validated them using a scRNA-seq dataset from pancreatic tissues of both T1D patients and healthy controls. The analysis showed a significantly high expression of the CTSH gene in T1D acinar cells, whereas the other three genes showed no significant changes in the scRNA-seq data. Moreover, single-cell WGCNA analysis revealed the strongest positive correlation between the module containing CTSH and T1D. In addition, we found cellular ligand-receptor interactions between the acinar cells and different cell types, especially ductal cells. Finally, based on functional enrichment analysis, we hypothesized that the CTSH gene in the exocrine pancreas enhances the antiviral response, leading to the overexpression of pro-inflammatory cytokines and the development of an inflammatory microenvironment. This process promotes β cells injury and ultimately the development of T1D. Our findings offer insights into the underlying pathogenic mechanisms of T1D.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"406-417"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138483553","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