Briefings in Functional Genomics最新文献

Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.). 利用综合多组学方法对水稻（Oryza sativa L.）产量性状主要 QTL 候选基因进行优先排序。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-09-03 DOI: 10.1093/bfgp/elae035

Issa Keerthi, Vishnu Shukla, Sudhamani Kalluru, Lal Ahamed Mohammad, P Lavanya Kumari, Eswarayya Ramireddy, Lakshminarayana R Vemireddy

{"title":"Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.).","authors":"Issa Keerthi, Vishnu Shukla, Sudhamani Kalluru, Lal Ahamed Mohammad, P Lavanya Kumari, Eswarayya Ramireddy, Lakshminarayana R Vemireddy","doi":"10.1093/bfgp/elae035","DOIUrl":"https://doi.org/10.1093/bfgp/elae035","url":null,"abstract":"Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127426","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

Environmental community transcriptomics: strategies and struggles. 环境群落转录组学：战略与斗争。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-08-24 DOI: 10.1093/bfgp/elae033

Jeanet Mante, Kyra E Groover, Randi M Pullen

引用次数: 0

A review: simulation tools for genome-wide interaction studies. 综述：全基因组相互作用研究的模拟工具。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-08-23 DOI: 10.1093/bfgp/elae034

Junliang Shang, Anqi Xu, Mingyuan Bi, Yuanyuan Zhang, Feng Li, Jin-Xing Liu

{"title":"A review: simulation tools for genome-wide interaction studies.","authors":"Junliang Shang, Anqi Xu, Mingyuan Bi, Yuanyuan Zhang, Feng Li, Jin-Xing Liu","doi":"10.1093/bfgp/elae034","DOIUrl":"https://doi.org/10.1093/bfgp/elae034","url":null,"abstract":"Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037783","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

Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches. 加强新型同工酶的发现：利用纳米孔长读数测序和机器学习方法。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-08-19 DOI: 10.1093/bfgp/elae031

Kristina Santucci, Yuning Cheng, Si-Mei Xu, Michael Janitz

{"title":"Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches.","authors":"Kristina Santucci, Yuning Cheng, Si-Mei Xu, Michael Janitz","doi":"10.1093/bfgp/elae031","DOIUrl":"https://doi.org/10.1093/bfgp/elae031","url":null,"abstract":"Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001414","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

Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping. 超越炒作：利用人工智能、大数据、可穿戴设备和物联网进行高通量家畜表型分析。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-08-19 DOI: 10.1093/bfgp/elae032

Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane

{"title":"Beyond the hype: using AI, big data, wearable devices, and the internet of things for high-throughput livestock phenotyping.","authors":"Tomas Klingström, Emelie Zonabend König, Avhashoni Agnes Zwane","doi":"10.1093/bfgp/elae032","DOIUrl":"https://doi.org/10.1093/bfgp/elae032","url":null,"abstract":"Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001413","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

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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