Briefings in Functional Genomics最新文献

{"title":"Supervised clustering of bacterial promoter identifies two groups with different relevant positions at -10.","authors":"Paulo Cambranis-Boldo, Gustavo Sganzerla Martinez, André Borges Farias, Ernesto Perez-Rueda","doi":"10.1093/bfgp/elag008","DOIUrl":"10.1093/bfgp/elag008","url":null,"abstract":"<p><p>Promoters are DNA sequences responsible for the specific recognition of the transcriptional machinery in all the biological systems. In bacteria, two main groups of promoters have been described, the σ70-like family, which includes the housekeeping σ70 and the alternative σ38, σ32, σ28, and σ24; and the σ54 family. However, promoter sequences may differ even inside the same family, and this classification may not fully capture the functional diversity of groups. In this work, we explored and classified a collection of bacterial promoters associated with six σ factor families in the bacterium Escherichia coli K-12 using a Supervised clustering workflow that uses Shapley values. From this analysis, we identified two subgroups of sequences differentiated by the conservation of the -9 to -7 positions upstream the Transcription Start Site, suggesting that this region may be employed in classification frameworks. Finally, a detailed analysis of σ70 promoter sequences identify five clusters with a conserved signal in the -10, but with different sequence composition. In summary, these signatures could provide more insights into the sigma factor-promoter interaction and the separation of the DNA double-strand.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13617569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.","authors":"Yugui Xu, Zhigan Zhou, Hao Yuan, Siqi Liu, Quan Zou, Yongqing Zhang, Wenqian Zhang","doi":"10.1093/bfgp/elag006","DOIUrl":"10.1093/bfgp/elag006","url":null,"abstract":"<p><p>Polypharmacy has become essential in managing complex and chronic conditions, yet it introduces significant clinical risks due to potential drug-drug interactions (DDIs). Existing computational models struggle to provide accurate and interpretable predictions, largely due to fragmented integration of molecular structures and biomedical knowledge. These limitations arise from static graph designs, inadequate substructure modeling, and insufficient incorporation of semantic context, highlighting the need for a unified, explainable solution. In this study, we propose MKGFlow-DDI, a multi-view knowledge-guided framework that jointly leverages drug-drug interaction networks and biomedical knowledge graphs to dynamically construct drug-flow subgraphs. The model incorporates a dual-channel encoder designed to capture atom-level information and substructure-level features, integrating them with the global semantic embeddings of the composite network to derive novel feature representations. These representations are fused to initialize node features within each subgraph, which are iteratively optimized through similarity-based edge refinement to reduce noise and enhance biological relevance. To further improve generalization and stability, a contrastive learning module is introduced to align representations of perturbed subgraphs by maximizing consistency across positive and negative sample pairs. Experimental results on DrugBank and TWOSIDES demonstrate that MKGFlow-DDI outperforms state-of-the-art baselines, especially in scenarios involving previously unseen drugs. Additionally, the model produces interpretable semantic pathways that align with known pharmacological mechanisms, enabling clinically meaningful insights. Overall, MKGFlow-DDI establishes a robust and biologically grounded approach to DDI prediction, offering a promising direction for computational pharmacovigilance and personalized therapy optimization.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13320273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148364267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.","authors":"Jie Ni, Xinting Zhang, Mingyang Li, Yun Liu, Adam Jatowt","doi":"10.1093/bfgp/elag009","DOIUrl":"10.1093/bfgp/elag009","url":null,"abstract":"<p><p>Accurate cancer subtype classification from single-transcriptome data remains challenging because of biological heterogeneity, measurement noise, and limited sample sizes. This study examined whether disease-gene priors add predictive information under strict separation of association-edge, patient-sample, and external-transfer boundaries. It also audited a historically selected 12-gene subset without claiming de novo discovery. Important mRNA Identification through Hybrid Fusion (ImRIHF) uses a layer attention graph convolutional network to learn cancer-specific association scores. A graph convolutional network for disease classification (GCNDC) then classifies subtypes after score-guided expression weighting. Association folds were masked before both Gaussian kernels and the heterogeneous graph were rebuilt. Patient preprocessing, feature selection, graph construction, tuning, and fitting remained training-contained. Twenty repeated five-fold refits described stability, while separate locked audits supplied paired effects. Patient-disjoint external transfer used frozen development predictors and no target-cohort fitting. Matched GCNDC and full-transcriptome controls bounded interpretation within one fixed 29-coordinate multiplicity family. UCEC remained feasibility-only because its locked audit contained 10 MSEAC cases. Leakage-controlled association AUPR and AUROC were 0.932 and 0.941, respectively. Non-UCEC locked effects versus matched GCNDC ranged from $+0.027$ to $+0.035$; the UCEC effect was $+0.032$ and remained descriptive. Strict-common external effects were $+0.034$ in METABRIC, $+0.026$ in CGGA, and $+0.036$ in GSE14333. Of 12 historical challenge genes, 11 mapped, 4 were BH-significant, and 2 were both direction-concordant and BH-significant. The frozen disease-guided rule showed modest positive differences from matched controls in the named retrospective cohorts. Full-transcriptome controls, UCEC feasibility limits, and source-specific transfer boundaries restrict this result. The analyses do not establish generic superiority, prospective validity, clinical utility, population generalization, causality, or a validated biomarker panel.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149118926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics insights into plant genomic imprinting: approaches, challenges, and future perspectives.","authors":"Xiaotong Jing, Xi Su, Quan Zou, Mengting Niu","doi":"10.1093/bfgp/elaf025","DOIUrl":"10.1093/bfgp/elaf025","url":null,"abstract":"<p><p>Genomic imprinting is an epigenetic occurrence that results in the expression of alleles specific to the parent of origin, plays pivotal roles in plant development, stress adaptation, and agronomic trait regulation. While imprinting has been intensively investigated in model plants (e.g. Arabidopsis, maize, and rice), its dynamic regulatory mechanisms and evolutionary implications remain enigmatic. Recent advances in bioinformatics-including single-cell omics, machine learning, and deep learning-have revolutionized the identification, functional annotation, and network modeling of imprinted genes. This review not only provides a detailed summary of the identification, functions and regulatory mechanisms of plant imprinted genes, but also systematically summarizes methodologies for studying plant genomic imprinting, highlights challenges in multi-omics data integration, and envisions artificial intelligence-driven strategies for epigenetic breeding.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12785899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145946984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of EGR1/LIPT1 regulatory axis on cuproptosis in chromophobe renal cell carcinoma.","authors":"Jingxian Luo, Mingqiang Su, Xianyong Li, Dayong Ye, Xiaofu Zeng, Yujie Wang, Guangqing Fu","doi":"10.1093/bfgp/elaf023","DOIUrl":"10.1093/bfgp/elaf023","url":null,"abstract":"<p><p>Renal cell carcinoma (RCC) is one of the most prevalent solid tumors, and chromophobe renal cell carcinoma (chRCC) is its third most common subtype. The cuproptosis has become a hot topic in the field of cancer treatment. This study aimed to investigate the potential targets of cuproptosis in chRCC cells. We first downloaded the chRCC mRNA transcriptome data from The Cancer Genome Atlas. Based on the previous reports, we speculated that the expression of LIPT1 was considerably down-regulated in chRCC tissues. The upstream transcription factor (TF) EGR1 was predicted by the hTFtarget web tool, and the interaction between EGR1 and LIPT1 was further verified by dual-luciferase and chromatin immunoprecipitation experiments. The mRNA expression levels of EGR1 and LIPT1 were detected by quantitative polymerase chain reaction. The expression levels of target protein LIPT1 and cuproptosis-associated protein were detected by western blot and immunofluorescence. Cell Counting Kit-8 assay was employed to detect the viability of RCC98 cells. The Transwell assay was utilized to assess the migration and invasion abilities of RCC98 cells. LIPT1 and its upstream TF, EGR1, were significantly down-regulated in chRCC tissues and cells. EGR1 could transcriptionally activate LIPT1. Additionally, overexpression of LIPT1 significantly reduced the cancer-associated malignant phenotype of chRCC and elevated the sensitivity of RCC98 cells to cuproptosis. However, on this basis, knocking down EGR1 restored the anti-cancer effect conferred by overexpression of LIPT1. This work aimed to investigate the transcriptional activation of LIPT1 by EGR1 in RCC98 cells to repress the malignant progression of cancer cells while enhancing the sensitivity of RCC98 cells to cuproptosis.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12785890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145946962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review on RNA subcellular localization prediction.","authors":"Cece Zhang, Xuehuan Zhu, Nick Peterson, Jieqiong Wang, Shibiao Wan","doi":"10.1093/bfgp/elag011","DOIUrl":"https://doi.org/10.1093/bfgp/elag011","url":null,"abstract":"<p><p>The subcellular localization of RNAs, including long non-coding RNAs, messenger RNAs, microRNAs and other smaller RNAs, plays a critical role in determining their biological functions. Understanding RNA localization sheds light on processes like gene expression regulation with spatial and temporal precision. However, traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly. To overcome these challenges, computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives, enabling large-scale prediction of RNA subcellular localization. This paper provides a comprehensive review of the latest advancements in AI-based approaches for RNA subcellular localization prediction, covering various RNA types and focusing on sequence-based, image-based, and hybrid methodologies that combine both data types. We highlight the potential of these methods to accelerate RNA research, uncover molecular pathways, and guide targeted disease treatments. Furthermore, we critically discuss the challenges in AI/ML approaches for RNA subcellular localization, such as data scarcity and lack of benchmarks, and opportunities to address them. This review aims to serve as a valuable resource for researchers seeking to develop innovative solutions in the field of RNA subcellular localization and beyond.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653565/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetically supported mediators linking peripheral metabolism to cerebral ischemia: a multi-omics characterization of HMGCR, TLR4, and MMP9 in angina pectoris and stroke.","authors":"Ying Li, Haozhou Tan, Li Zhao, Chenggang Zhu, Qian Feng","doi":"10.1093/bfgp/elag004","DOIUrl":"10.1093/bfgp/elag004","url":null,"abstract":"<p><p>Emerging evidence implicates peripheral metabolic disturbances in the pathogenesis of cerebrovascular diseases. However, the specific molecular conduits linking angina pectoris (AP) to ischemic stroke (IS) remain elusive. To address this, we evaluated the causal effect of AP on IS subtypes utilizing Bayesian-weighted Mendelian randomization (BWMR) based on large-scale GWAS meta-data (n = 628 000). Cross-tissue transcriptomic analyses, coupled with the Boruta feature selection algorithm, were employed to screen for candidate mediators, with subsequent validation of genetic causality and therapeutic viability via standard Mendelian randomization (MR), colocalization analysis, and molecular docking. Furthermore, their involvement in cellular immunity was characterized using CIBERSORT and single-cell ribonucleic acid-seq (scRNA-seq). BWMR established a robust causal relationship from stable AP to IS (OR = 1.13, P = .004). Multi-omics integration identified 18 hub macromolecules, prioritizing HMGCR, TLR4, and MMP9 as the primary candidate mediators enriched in lipid metabolism and inflammatory pathways. MR corroborated the genetic relevance of these loci to IS risk, while molecular docking confirmed their structural druggability. A multilayer perceptron classifier (MLPC) utilizing these targets demonstrated high predictive accuracy for IS (AUC = 0.82-0.90). Crucially, scRNA-seq revealed that TLR4 and MMP9 expression in peripheral immune cells (CD8+ T cells and neutrophils) may drive a metabolic-inflammatory cascade, functioning as vital conduits for peripheral-to-brain signal transduction. In conclusion, this multi-omics framework identifies HMGCR, TLR4, and MMP9 as genetically supported mediators bridging AP and IS, elucidating peripheral-cerebral crosstalk and highlighting actionable targets for clinical risk stratification and pharmacological repurposing.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"25 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13291907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148260694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in investigation of circRNA/lncRNA-miRNA-mRNA networks through RNA sequencing data analysis.","authors":"Yulan Gao, Konii Takenaka, Si-Mei Xu, Yuning Cheng, Michael Janitz","doi":"10.1093/bfgp/elaf005","DOIUrl":"10.1093/bfgp/elaf005","url":null,"abstract":"<p><p>Non-coding RNAs (ncRNAs) are RNA molecules that are transcribed from DNA but are not translated into proteins. Studies over the past decades have revealed that ncRNAs can be classified into small RNAs, long non-coding RNAs and circular RNAs by genomic size and structure. Accumulated evidences have eludicated the critical roles of these non-coding transcripts in regulating gene expression through transcription and translation, thereby shaping cellular function and disease pathogenesis. Notably, recent studies have investigated the function of ncRNAs as competitive endogenous RNAs (ceRNAs) that sequester miRNAs and modulate mRNAs expression. The ceRNAs network emerges as a pivotal regulatory function, with significant implications in various diseases such as cancer and neurodegenerative disease. Therefore, we highlighted multiple bioinformatics tools and databases that aim to predict ceRNAs interaction. Furthermore, we discussed limitations of using current technologies and potential improvement for ceRNAs network detection. Understanding of the dynamic interplay within ceRNAs may advance the biological comprehension, as well as providing potential targets for therapeutic intervention.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"24 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational inference of Rhizobium phaseoli transcriptional regulatory network predicts Transcription Factors involved in nodulation.","authors":"Ericka M Hernandez-Benitez, Esperanza Martínez-Romero, José Luis Aguirre-Noyola, Jose Arcadio Farias-Rico, Daniela Ledezma-Tejeida","doi":"10.1093/bfgp/elaf020","DOIUrl":"10.1093/bfgp/elaf020","url":null,"abstract":"<p><p>Growth of the common bean plant Phaseolus vulgaris is tightly linked to its symbiotic relationship with diverse rhizobial species, particularly Rhizobium phaseoli, an alphaproteobacterium that forms root nodules and provides high levels of nitrogen to the plant. Molecular cross-talk is known to happen through plant-derived metabolites, but only flavonoids have been identified as nodulation signals, which act through the activation of the NodD Transcription Factor (TF). The identification of signals that mediate nodulation via TFs can aid in the rational design of biofertilizers that promote plant-bacteria symbiosis. Here, we identified 57 TFs in the R. phaseoli genome through sequence conservation from Escherichia coli, and predicted a transcriptional regulatory network comprising 16 TFs, and 1,371 target genes. We identified the regulatory interactions relevant to nodulation via transcriptome analysis, and hypothesize that PuuR is a TF involved in nodulation, potentially acting via its known binding metabolite putrescine. Sequence and structural evidence predict a model where putrescine acts as a signaling metabolite in nodulation via the TF PuuR, and the regulation of the nodI gene.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":"24 ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12645834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145607605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"pyRforest: a comprehensive R package for genomic data analysis featuring scikit-learn Random Forests in R.","authors":"Tyler Kolisnik, Faeze Keshavarz-Rahaghi, Rachel V Purcell, Adam N H Smith, Olin K Silander","doi":"10.1093/bfgp/elae038","DOIUrl":"10.1093/bfgp/elae038","url":null,"abstract":"<p><p>Random Forest models are widely used in genomic data analysis and can offer insights into complex biological mechanisms, particularly when features influence the target in interactive, nonlinear, or nonadditive ways. Currently, some of the most efficient Random Forest methods in terms of computational speed are implemented in Python. However, many biologists use R for genomic data analysis, as R offers a unified platform for performing additional statistical analysis and visualization. Here, we present an R package, pyRforest, which integrates Python scikit-learn \"RandomForestClassifier\" algorithms into the R environment. pyRforest inherits the efficient memory management and parallelization of Python, and is optimized for classification tasks on large genomic datasets, such as those from RNA-seq. pyRforest offers several additional capabilities, including a novel rank-based permutation method for biomarker identification. This method can be used to estimate and visualize P-values for individual features, allowing the researcher to identify a subset of features for which there is robust statistical evidence of an effect. In addition, pyRforest includes methods for the calculation and visualization of SHapley Additive exPlanations values. Finally, pyRforest includes support for comprehensive downstream analysis for gene ontology and pathway enrichment. pyRforest thus improves the implementation and interpretability of Random Forest models for genomic data analysis by merging the strengths of Python with R. pyRforest can be downloaded at: https://www.github.com/tkolisnik/pyRforest with an associated vignette at https://github.com/tkolisnik/pyRforest/blob/main/vignettes/pyRforest-vignette.pdf.</p>","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}