Genomics & informatics最新文献

{"title":"Statistical learning methods for improving predictive performance in time-dependent survival models.","authors":"Hyungwoo Seo, Wonil Chung","doi":"10.1186/s44342-025-00050-7","DOIUrl":"10.1186/s44342-025-00050-7","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has highlighted the need for survival models to assess risk factors and time-dependent effects in infectious diseases. However, the Cox proportional hazards (PH) model, which assumes constant covariate effects, struggles to capture disease dynamics. This underscores the need for advanced models that incorporate time-dependent coefficients and covariates for improved accuracy.</p><p><strong>Methods: </strong>To address the need for modeling time-dependent effects and covariates, we applied a stratified Cox PH model with multiple time intervals to better satisfy the PH assumption. We conducted simulations to evaluate the performance of machine learning and deep learning survival models, including random survival forest (RSF), DeepSurv, and DeepHit. To improve time-dependent effect estimation, we introduced a refined time-interval division and a weighted sum approach for integrated hazard ratios of COVID-19 variants. The event of interest was death, and the specific risk compared was the risk of death from the start of the study to either death or the last follow-up among infected versus uninfected individuals.</p><p><strong>Results: </strong>Our results showed that increasing the number of time intervals improved predictive accuracy. When the PH assumption held, the Cox PH model outperformed machine learning and deep learning models. Applying our approach to UK Biobank data, expanding time intervals from five to fifteen enhanced performance. The previously reported hazard ratio of 7.333 for the pre-Delta period was refined to 29.359 for the Early variant, 20.734 for EU1, and 4.079 for Alpha, revealing a decline in risk across variants.</p><p><strong>Conclusions: </strong>These findings suggest that refining time intervals improves the understanding of time-dependent effects in infectious diseases. Incorporating stratified intervals and advanced models enhances risk assessment and predictive accuracy for COVID-19 and other evolving diseases.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling the molecular landscape of polycystic ovary syndrome (PCOS) and role of inflammation through transcriptomics analysis of human ovarian granulosa cells.","authors":"Kanika Mahra, Vineet Singh, Jae-Ho Shin","doi":"10.1186/s44342-025-00051-6","DOIUrl":"10.1186/s44342-025-00051-6","url":null,"abstract":"<p><strong>Background: </strong>Polycystic ovary syndrome (PCOS) is a common metabolic problem in women of reproductive age that can lead to infertility and other metabolic disorders. Recent evidence indicates that inflammation might be one of the contributing factors in PCOS progression. However, there is a lack of information on the regulation of inflammatory genes in PCOS. Therefore, the aim of the study is to investigate the role of inflammation-associated genes and pathways in relation to PCOS.</p><p><strong>Method: </strong>The bulk RNA-seq data of granulosa cells of human ovaries of PCOS-affected and healthy women were analyzed to evaluate the inflammatory regulation in PCOS. After quality trimming, the raw RNA-seq data were aligned to the human genome, and gene expression was quantified using featureCounts with Ensembl annotation. Further, downstream analyses of the resulting count matrix were performed in R Studio, where differentially expressed genes (DEG) were identified and CO-DEG analysis was performed.</p><p><strong>Results: </strong>The study identifies the various differentially expressed inflammatory genes in the case of PCOS such as SPI1, HSPB1, MNDA, and ITGA. These DEG are closely associated with the activation of inflammatory responses, i.e., activation of lymphocytes and leukocytes, leukocyte migration and mononuclear cell proliferation, stimulating binding of various cytokines, immunoglobulins, and chemokines. PCOS group also exhibited an increased expression of androgen-mediated genes (SPI1 and ETS transcription factors) and genes associated with hyperlipidemia and insulin resistance (TNFRSF1B). Further, KEGG pathway enrichment analysis revealed significant upregulation of various pathways (autophagy, endocytosis) in the PCOS group. In addition, network analysis (cnetplot) of the top 10 KEGG GSEA pathways also highlights the key pathways in the PCOS group such as SNARE complex assembly pathway, SNAP-25, nucleophagy, and regulation of mast cell activation.</p><p><strong>Conclusion: </strong>Therefore, the study highlights that inflammation is a major effector in PCOS, which also fuels obesity, an independent effector that further worsens the PCOS condition. In addition, the genes related to hyperandrogenism, hyperlipidemia, and insulin resistance were also overexpressed in PCOS, exacerbating the condition.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12337442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144823646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of cell-type-specific, transcriptionally active transposable elements using long-read RNA-sequencing data-based comprehensive annotation.","authors":"Chaemin Lim, Hyunsu An, Jihwan Park","doi":"10.1186/s44342-025-00048-1","DOIUrl":"10.1186/s44342-025-00048-1","url":null,"abstract":"<p><strong>Background: </strong>The biological functions of transposable element (TE)-derived transcripts during physiological development, disease development, and progression have been previously reported. However, research on locus-specific TE-derived transcript expression in various human cell types remains limited.</p><p><strong>Methods: </strong>We processed 2596 publicly available human long-read RNA-sequencing (LR RNA-seq) datasets covering 21 organs and 71 cell lines in both healthy individuals and diseased patients with various conditions to compile this TE-derived transcript annotation. We established a pipeline for assembling transcripts containing TE sequences to measure transcriptionally active TE-derived transcripts in diverse tissues and cell types. Next, we applied our TE annotation to the Genotype-Tissue Expression (GTEx) single-cell RNA-sequencing (scRNA-seq) data from eight tissues.</p><p><strong>Results: </strong>We constructed the first transcriptom6e-based TE annotation using massive amounts of human LR RNA-seq data for use as a comprehensive reference to detect locus-specific TE-derived transcripts. Our annotation showed better detection accuracy for TE-derived transcripts than the RepeatMasker and GENCODE nonTE gene annotations. This annotation enabled the identification of novel TE-derived transcripts and their isoforms. We also identified alternative transcription end sites for long noncoding genes and confirmed previously annotated TE-nonTE gene fusion transcripts. Next, we applied our TE-derived transcript annotation to public scRNA-seq data from various human tissues and identified several cell-type-specific TE-derived transcripts in a locus-specific manner.</p><p><strong>Conclusions: </strong>We generated a comprehensive, TE-derived transcript annotation using large-scale, LR RNA-seq data. Researchers can use our TE reference annotation to analyze active TE transcripts and their splicing isoforms in specific transcriptome datasets and to detect de novo TE transcripts. The discovery of cell-type-specific TE-derived transcripts may help explain mechanisms underlying the maintenance of cellular identity and provide new insights into the pathological mechanisms of various diseases.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12326599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144796603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico design and evaluation of a multiepitope vaccine against Bordetella pertussis: structural, immunological, and biological properties.","authors":"Negar Souod, Hamid Madanchi, Fariborz Bahrami, Saeed Reza Pakzad, Fereshteh Shahcheraghi, Soheila Ajdary","doi":"10.1186/s44342-025-00049-0","DOIUrl":"10.1186/s44342-025-00049-0","url":null,"abstract":"<p><strong>Introduction/objectives: </strong>Despite widespread vaccination, the increasing incidence of pertussis underscores the urgent need for innovative vaccine strategies. This study aims to design and analyze, using in silico methods, a multiepitope protein that incorporates epitopes from the S1 subunit of pertussis toxin and the type 1 immunodominant domain of filamentous hemagglutinin (F1). The goal is to enhance both systemic and mucosal immunity through the incorporation of the C-terminal fragment of Clostridium perfringens enterotoxin (C-CPE).</p><p><strong>Methods: </strong>Using reverse vaccinology, we predicted immunogenic epitopes for lymphocytes derived from the S1 and F1 proteins. The epitopes were assembled into a multiepitope construct named mF1S1-C-CPE, which was then evaluated for its physicochemical, immunological, and biological properties. Immunoinformatics tools were employed to analyze antigenicity, allergenicity, and population coverage. Additionally, molecular docking simulations of peptide‒MHC and mF1S1-C-CPE_TLR2/TLR4 binding were conducted.</p><p><strong>Results: </strong>Structural analysis indicated that the final multiepitope construct maintained stability and solubility in aqueous environments. Immunoinformatic analysis revealed strong immunogenic properties, effectively eliciting both systemic and mucosal immune responses. Molecular docking demonstrated high-affinity binding patterns between the peptides (both individual or within the mF1S1-C-CPE) and corresponding HLA molecules. Additionally, molecular docking simulations of mF1S1-C-CPE and TLR2/TLR4 indicated strong binding affinity to receptors of innate immunity. The construct was predicted to be stable, soluble, and suitable for expression in Escherichia coli (CAI 0.93; GC content 54.9%).</p><p><strong>Conclusion: </strong>This innovative approach holds promise for enhancing pertussis vaccination strategies by improving mucosal immune responses. Further in vivo studies are essential to validate the efficacy of this multiepitope vaccine candidate.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene expression network analysis identified CDK1 and KIF11 as possible key molecules in the development of colorectal cancer from normal tissues.","authors":"Soo Bin Lee, Young Seon Noh, Ji-Wook Moon, Soohyun Sim, Sung Won Han, Eun Sun Kim, Ji-Yun Lee","doi":"10.1186/s44342-025-00046-3","DOIUrl":"10.1186/s44342-025-00046-3","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is one of the most common malignancies and the second most common cause of cancer-related mortality worldwide. Despite extensive research, the mechanism underlying CRC development remains unclear. This study aimed to understand the development and progression of CRC.</p><p><strong>Methods: </strong>Gene network analysis of tumors with their paired normal tissues was performed using the differentially expressed genes dataset for CRC from the Cancer Genome Atlas. Further investigation of the regulatory relationship between hub genes and tumor development was conducted by protein-protein interaction network, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the selected hub genes.</p><p><strong>Results: </strong>The network was more centered, and a common hub as well as a hub of hub genes were more connected to each other in the tumor than in the normal tissue, indicating changes in the network from normal to tumor. Eight downregulated and two upregulated hub genes (CDK1 and KIF11) in the tumor were identified. Further, the regulatory pathway was altered, especially in cell cycle and cell division. All R implementation codes are available on the journal website as supplementary materials.</p><p><strong>Conclusions: </strong>Our findings may help understand the biological processes underlying tumor development and progression and suggest CDK1 and KIF11 as possible key molecules in the development of CRC.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-variable RNA deletion using the CRISPR-Cas9 technique demonstrated improved outcomes in human intestine single-cell RNA sequencing data, even at half sequencing depths.","authors":"Dong Jun Kim, Christine Suh Yun Joh, So Young Jeong, Yong Jun Kim, Seong Joon Koh, Hyun Je Kim","doi":"10.1186/s44342-025-00043-6","DOIUrl":"10.1186/s44342-025-00043-6","url":null,"abstract":"<p><p>In single-cell RNA sequencing (scRNA-seq) data, issues related to the high expression of non-variable RNAs often arise due to organ traits or sample quality. Computational methods, such as SoupX (Young (Gigascience 9:giaa151, 2020)), have been used to solve this problem but it may remove biologically relevant data. This study presents a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-based method that selectively removes non-variable RNAs. We applied this approach to scRNA-seq data from human intestinal tissues of 17 patients. By targeting non-variable genes, including ribosomal and mitochondrial RNAs, CRISPR-Cas9 treatment effectively reduced their expression, outperforming computational methods in both the number and extent of gene removal. The CRISPR-Cas9 treated samples, sequenced at half the depth compared to untreated samples, maintained comparable sequencing quality, and saturation, demonstrating that this approach can reduce sequencing costs while preserving data quality. Cell type composition and gene expression patterns remained consistent between treated and original datasets, with no unintended gene deletions. Overall, our findings suggest that the CRISPR-Cas9-based method offers a cost-effective solution for improving scRNA-seq data quality, particularly for tissues with high levels of non-variable RNAs, without compromising biological integrity.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.","authors":"Ankish Arya, Prabhat Tripathi, Nidhi Dubey, Imlimaong Aier, Pritish Kumar Varadwaj","doi":"10.1186/s44342-025-00044-5","DOIUrl":"10.1186/s44342-025-00044-5","url":null,"abstract":"<p><p>Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"13"},"PeriodicalIF":0.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide association study-driven identification of thrombomodulin and factor V as the best biomarker combination for deep vein thrombosis.","authors":"Usi Sukorini, Gisca Ajeng Widya Ninggar, Mohammad Hendra Setia Lesmana, Lalu Irham, Wirawan Adikusuma, Hegaria Rahmawati, Nur Imma Fatimah Harahap, Chiou-Feng Lin, Rahmat Dani Satria","doi":"10.1186/s44342-025-00047-2","DOIUrl":"10.1186/s44342-025-00047-2","url":null,"abstract":"<p><p>Deep vein thrombosis (DVT) is a clinically significant condition characterized by the formation of thrombi in deep venous structures, leading to high morbidity and potential mortality. Identifying reliable biomarkers for DVT risk prediction remains challenging due to the intricate genetic and molecular mechanisms underlying the disease. This study aims to investigate the best biomarker for DVT. Our study utilized genome-wide association studies (GWAS) findings coupled with a functional annotation scoring system to identify and prioritize genetic markers with strong associations to DVT. Furthermore, gene expression levels were analyzed to determine the most promising genetic markers. Several databases were utilized, including the GWAS Catalog, HaploReg 4.2, WebGestalt, Enrichr, and the GTEx Portal. Through the comprehensive analysis, we found 5 potential biomarkers and highlighted thrombomodulin (THBD) and Factor V (F5) as the best blood-based biomarkers. THBD and F5 genes were selected based on their elevated expression levels in blood and the presence of eQTLs. Functionally, THBD modulates coagulation via protein C activation, while F5 is pivotal in thrombin formation and clot stabilization, underscoring their mechanistic relevance to DVT pathogenesis, and rendering them suitable for non-invasive clinical assessment. Our findings emphasize the potential of genetic biomarkers to transform DVT risk assessment and support advancements in precision medicine for thrombotic disorders.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics analysis reveals shared molecular pathways for relationship between ulcerative colitis and primary sclerosing cholangitis.","authors":"Pooya Jalali, Malihe Rezaee, Alireza Yaghoobi, Moein Piroozkhah, Mohammad Reza Zabihi, Shahram Aliyari, Zahra Salehi","doi":"10.1186/s44342-025-00045-4","DOIUrl":"10.1186/s44342-025-00045-4","url":null,"abstract":"<p><strong>Background: </strong>Inflammatory bowel disease (IBD) is a group of chronic inflammatory disorders, including ulcerative colitis (UC) and Crohn's disease, affecting the gastrointestinal tract and is associated with high morbidity and mortality. Accumulating evidence indicates that IBD not only impacts the gastrointestinal tract but also affects multiple extraintestinal organs, which may manifest prior to the diagnosis of IBD. Among these extraintestinal manifestations associated with IBD, primary sclerosing cholangitis (PSC) stands out as a prominent example. PSC is recognized as a progressive cholestatic disorder, characterized by the narrowing of bile ducts, eventual development of liver cirrhosis, end-stage liver disease, and the potential emergence of cholangiocarcinoma. This study aimed to identify the molecular contributors in UC-induced PSC by detecting the essential regulatory genes that are differentially expressed in both diseases.</p><p><strong>Materials and methods: </strong>The common single-nucleotide polymorphisms (SNPs) and differentially expressed genes (DEGs) were detected using DisGeNET and GEO databases, respectively. Then, the top module and hub genes within the protein-protein interaction network were identified. Furthermore, the co-expression network of the top module was constructed using the HIPPIE database. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Finally, we searched the DGIdb database for possible interacting drugs with UC-PSC top module genes.</p><p><strong>Results: </strong>A total of 132 SNPs and their associated genes were found to be shared between UC and PSC. Gene expression analysis identified 56 common DEGs between the two diseases. Following functional enrichment analysis, 207 significant biological processes (BP), 48 molecular functions (MF), and 8 KEGG pathways, with notable enrichment in mRNA-related processes such as mRNA splicing and RNA binding, were defined. Particularly, the PTPN2 gene was the only gene common between UC and PSC at both the SNP level and the expression level. Additionally, the top cluster of PPI network analysis was consisted of PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 genes. Finally, ceRNA network involving 4 mRNAs, 94 miRNAs, and 200 selected circRNAs was constructed.</p><p><strong>Conclusion: </strong>The present study provides novel potential candidate genes that may be involved in the molecular association between ulcerative colitis and primary sclerosing cholangitis, resulting in the development of diagnostic tools and therapeutic targets to prevent the progression of PSC from UC.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell network biology enabling cell-type-resolved disease genetics.","authors":"Junha Cha, Insuk Lee","doi":"10.1186/s44342-025-00042-7","DOIUrl":"10.1186/s44342-025-00042-7","url":null,"abstract":"<p><p>Gene network models provide a foundation for graph theory approaches, aiding in the novel discovery of drug targets, disease genes, and genetic mechanisms for various biological functions. Disease genetics must be interpreted within the cellular context of disease-associated cell types, which cannot be achieved with datasets consisting solely of organism-level samples. Single-cell RNA sequencing (scRNA-seq) technology allows computational distinction of cell states which provides a unique opportunity to understand cellular biology that drives disease processes. Importantly, the abundance of cell samples with their transcriptome-wide profile allows the modeling of systemic cell-type-specific gene networks (CGNs), offering insights into gene-cell-disease relationships. In this review, we present reference-based and de novo inference of gene functional interaction networks that we have recently developed using scRNA-seq datasets. We also introduce a compendium of CGNs as a useful resource for cell-type-resolved disease genetics. By leveraging these advances, we envision single-cell network biology as the key approach for mapping the gene-cell-disease axis.</p>","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":"23 1","pages":"10"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}