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Several protocols have been established for the generation of lens organoids from embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and other cells with regenerative potential in humans or various animal models. It is important to examine how well the regenerated lens organoids reflect lens biology, in terms of its development, homeostasis, and aging. Toward this goal, the iSyTE database (integrated Systems Tool for Eye gene discovery; https://research.bioinformatics.udel.edu/iSyTE/ ), a bioinformatics resource tool that contains meta-analyzed gene expression data in wild-type lens across different embryonic, postnatal, and adult stages, can serve as a resource for comparative analysis. This article outlines the approaches toward effective use of iSyTE to gain insights into normal gene expression in the mouse lens, enriched expression in the lens, and differential gene expression in select mouse gene-perturbation cataract/lens defects models, which in turn can be used to evaluate expression of key lens-relevant genes in lens organoids by transcriptomics (e.g., RNA-sequencing (RNA-seq), microarrays, etc.) or other downstream methods (e.g., RT-qPCR, etc.).

Asunto(s)

Cristalino , Organoides , Regeneración , Cristalino/citología , Cristalino/metabolismo , Organoides/metabolismo , Organoides/citología , Animales , Ratones , Regeneración/genética , Perfilación de la Expresión Génica/métodos , Biología Computacional/métodos , Simulación por Computador , Humanos , Catarata/genética , Catarata/patología , Catarata/metabolismo , Transcriptoma , Bases de Datos Genéticas

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This document outlines the steps necessary to assemble and submit the standard data package required for contributing to the global genomic surveillance of enteric pathogens. Although targeted to GenomeTrakr laboratories and collaborators, these protocols are broadly applicable for enteric pathogens collected for different purposes. There are five protocols included in this chapter: (1) quality control (QC) assessment for the genome sequence data, (2) validation for the contextual data, (3) data submission for the standard pathogen package or Pathogen Data Object Model (DOM) to the public repository, (4) viewing and querying data at NCBI, and (5) data curation for maintaining relevance of public data. The data are available through one of the International Nucleotide Sequence Database Consortium (INSDC) members, with the National Center for Biotechnology Information (NCBI) being the primary focus of this document. NCBI Pathogen Detection is a custom dashboard at NCBI that provides easy access to pathogen data plus results for a standard suite of automated cluster and genotyping analyses important for informing public health and regulatory decision-making.

Asunto(s)

Genómica , Control de Calidad , Humanos , Genómica/métodos , Genómica/normas , Bases de Datos Genéticas , Programas Informáticos , Genoma Bacteriano , Curaduría de Datos/métodos

RESUMEN

Cohesin is a protein complex that plays a key role in regulating chromosome structure and gene expression. While next-generation sequencing technologies have provided extensive information on various aspects of cohesin, integrating and exploring the vast datasets associated with cohesin are not straightforward. CohesinDB ( https://cohesindb.iqb.u-tokyo.ac.jp ) offers a web-based interface for browsing, searching, analyzing, visualizing, and downloading comprehensive multiomics cohesin information in human cells. In this protocol, we introduce how to utilize CohesinDB to facilitate research on transcriptional regulation and chromatin organization.

Asunto(s)

Proteínas de Ciclo Celular , Proteínas Cromosómicas no Histona , Cohesinas , Navegador Web , Proteínas Cromosómicas no Histona/metabolismo , Proteínas Cromosómicas no Histona/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas de Ciclo Celular/genética , Humanos , Programas Informáticos , Biología Computacional/métodos , Genómica/métodos , Bases de Datos Genéticas , Cromatina/metabolismo , Cromatina/genética , Internet , Multiómica

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The red flour beetle Tribolium castaneum has emerged as a powerful model in insect functional genomics. However, a major limitation in the field is the lack of a detailed spatio-temporal view of the genetic signatures underpinning the function of distinct tissues and life stages. Here, we present an ontogenetic and tissue-specific web-based resource for Tribolium transcriptomics: BeetleAtlas (https://www.beetleatlas.org). This web application provides access to a database populated with quantitative expression data for nine adult and seven larval tissues, as well as for four embryonic stages of Tribolium. BeetleAtlas allows one to search for individual Tribolium genes to obtain values of both total gene expression and enrichment in different tissues, together with data for individual isoforms. To facilitate cross-species studies, one can also use Drosophila melanogaster gene identifiers to search for related Tribolium genes. For retrieved genes there are options to identify and display the tissue expression of related Tribolium genes or homologous Drosophila genes. Five additional search modes are available to find genes conforming to any of the following criteria: exhibiting high expression in a particular tissue; showing significant differences in expression between larva and adult; having a peak of expression at a specific stage of embryonic development; belonging to a particular functional category; and displaying a pattern of tissue expression similar to that of a query gene. We illustrate how the different feaures of BeetleAtlas can be used to illuminate our understanding of the genetic mechanisms underpinning the biology of what is the largest animal group on earth.

Asunto(s)

Transcriptoma , Tribolium , Animales , Tribolium/genética , Tribolium/embriología , Regulación del Desarrollo de la Expresión Génica , Perfilación de la Expresión Génica , Larva/genética , Larva/crecimiento & desarrollo , Larva/metabolismo , Bases de Datos Genéticas , Especificidad de Órganos , Drosophila melanogaster/genética , Drosophila melanogaster/metabolismo

RESUMEN

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein's function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein's function. Ideally, accurate annotations would allow us to predict a bacterium's capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest. Database URL: http://papers.genomics.lbl.gov.

Asunto(s)

Genoma Bacteriano , Anotación de Secuencia Molecular , Anotación de Secuencia Molecular/métodos , Programas Informáticos , Bases de Datos Genéticas , Proteínas Bacterianas/genética , Interfaz Usuario-Computador , Bases de Datos de Proteínas

RESUMEN

BACKGROUND: Gene expression and alternative splicing are strictly regulated processes that shape brain development and determine the cellular identity of differentiated neural cell populations. Despite the availability of multiple valuable datasets, many functional implications, especially those related to alternative splicing, remain poorly understood. Moreover, neuroscientists working primarily experimentally often lack the bioinformatics expertise required to process alternative splicing data and produce meaningful and interpretable results. Notably, re-analyzing publicly available datasets and integrating them with in-house data can provide substantial novel insights. However, such analyses necessitate developing harmonized data handling and processing pipelines which in turn require considerable computational resources and in-depth bioinformatics expertise. RESULTS: Here, we present Cortexa-a comprehensive web portal that incorporates RNA-sequencing datasets from the mouse cerebral cortex (longitudinal or cell-specific) and the hippocampus. Cortexa facilitates understandable visualization of the expression and alternative splicing patterns of individual genes. Our platform provides SplicePCA-a tool that allows users to integrate their alternative splicing dataset and compare it to cell-specific or developmental neocortical splicing patterns. All standardized gene expression and alternative splicing datasets can be downloaded for further in-depth downstream analysis without the need for extensive preprocessing. CONCLUSIONS: Cortexa provides a robust and readily available resource for unraveling the complexity of gene expression and alternative splicing regulatory processes in the mouse brain. The data portal is available at https://cortexa-rna.com/.

Asunto(s)

Empalme Alternativo , Encéfalo , Animales , Empalme Alternativo/genética , Ratones , Encéfalo/metabolismo , Biología Computacional/métodos , Programas Informáticos , Bases de Datos Genéticas , Análisis de Secuencia de ARN/métodos , Corteza Cerebral/metabolismo , Hipocampo/metabolismo , Perfilación de la Expresión Génica/métodos

RESUMEN

OBJECTIVES: Chronic rejection remains the leading cause of progressive decline in graft function. Accumulating evidence indicates that macrophages participate in chronic rejection dependent on CD40-CD40L. The FOS family members are critical in inflammatory and immune responses. However, the mechanisms underlying the role of FOS family members in chronic rejection remain unclear. In this study, we aimed to elucidate the role and underlying mechanisms of FOS-positive macrophages regulated by CD40 that mediate chronic allograft rejection. MATERIALS AND METHODS: We downloaded publicly accessible chronic rejection kidney transplant single-cell sequencing datasets from the gene expression omnibus database. Differentially expressed genes between the CD40hi and CD40low macrophage chronic rejection groups were analyzed. We established a chronic rejection mouse model by using CTLA-4-Ig. We treated bone marrow-derived macrophages with an anti-CD40 antibody. We assessed expression of the FOS family by flow cytometry, real-time quantitative polymerase chain reaction, Western blotting, and immunofluorescence. We identified altered signaling pathways by using RNA sequencing analysis. We detected DNA specifically bound to transcription factors by using ChIP-sequencing, with detection of the degree of graft fibrosis and survival. RESULTS: FOS was highly expressed on CD40hi macrophages in patients with chronic transplantrejection. Mechanistically, we showed that CD40 activated NF-κB2 translocation into the nucleus to upregulate c-Fos and FosB expression, thus promoting chronic rejection of cardiac transplant.We showed thatNF-κB2 regulated c-Fos and FosB expression by binding to the c-fos and fosb promoter regions. Inhibition of c-Fos/activator protein-1 decreased graft fibrosis and prolonged graft survival. CONCLUSIONS: CD40 may activate transcription factor NF-κB2 translocation into the nucleus of macrophages to upregulate c-Fos and FosB expression, thus promoting chronic rejection of cardiac transplant. Inhibition of c-Fos/activator protein-1 decreased grafts fibrosis and prolonged graft survival.

Asunto(s)

Antígenos CD40 , Modelos Animales de Enfermedad , Rechazo de Injerto , Trasplante de Corazón , Macrófagos , Proteínas Proto-Oncogénicas c-fos , Transducción de Señal , Animales , Humanos , Masculino , Ratones , Antígenos CD40/metabolismo , Antígenos CD40/genética , Células Cultivadas , Enfermedad Crónica , Bases de Datos Genéticas , Fibrosis , Rechazo de Injerto/inmunología , Rechazo de Injerto/metabolismo , Rechazo de Injerto/genética , Supervivencia de Injerto , Trasplante de Corazón/efectos adversos , Macrófagos/inmunología , Macrófagos/metabolismo , Ratones Endogámicos BALB C , Ratones Endogámicos C57BL , FN-kappa B/metabolismo , Proteínas Proto-Oncogénicas c-fos/metabolismo , Proteínas Proto-Oncogénicas c-fos/genética , Factor de Transcripción AP-1/metabolismo

RESUMEN

Single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal tool for exploring cellular landscapes across diverse species and tissues. Precise annotation of cell types is essential for understanding these landscapes, relying heavily on empirical knowledge and curated cell marker databases. In this study, we introduce MarkerGeneBERT, a natural language processing (NLP) system designed to extract critical information from the literature regarding species, tissues, cell types, and cell marker genes in the context of single-cell sequencing studies. Leveraging MarkerGeneBERT, we systematically parsed full-text articles from 3702 single-cell sequencing-related studies, yielding a comprehensive collection of 7901 cell markers representing 1606 cell types across 425 human tissues/subtissues, and 8223 cell markers representing 1674 cell types across 482 mouse tissues/subtissues. Comparative analysis against manually curated databases demonstrated that our approach achieved 76% completeness and 75% accuracy, while also unveiling 89 cell types and 183 marker genes absent from existing databases. Furthermore, we successfully applied the compiled brain tissue marker gene list from MarkerGeneBERT to annotate scRNA-seq data, yielding results consistent with original studies. Conclusions: Our findings underscore the efficacy of NLP-based methods in expediting and augmenting the annotation and interpretation of scRNA-seq data, providing a systematic demonstration of the transformative potential of this approach. The 27323 manual reviewed sentences for training MarkerGeneBERT and the source code are hosted at https://github.com/chengpeng1116/MarkerGeneBERT .

Asunto(s)

Biomarcadores , Procesamiento de Lenguaje Natural , Análisis de la Célula Individual , Humanos , Animales , Análisis de la Célula Individual/métodos , Ratones , Análisis de Secuencia de ARN/métodos , Bases de Datos Genéticas , Biología Computacional/métodos

RESUMEN

HNRNPA2B1 is a member of the HNRNP family, which is associated with telomere function, mRNA translation, and splicing, and plays an important role in tumor development. To date, there have been no pan-cancer studies of HNRNPA2B1, particularly within the TME. Therefore, we conducted a pan-cancer analysis of HNRNPA2B1 using TCGA data. Based on datasets from TCGA, TARGET, Genotype-Tissue Expression, and Human Protein Atlas, we employed a range of bioinformatics approaches to explore the potential oncogenic role of HNRNPA2B1. This included analyzing the association of HNRNPA2B1 expression with prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), immune response, and immune cell infiltration of individual tumors. We further validated the bioinformatic findings using immunohistochemistry techniques. HNRNPA2B1 was found to be differentially expressed across most tumor types in TCGA's pan-cancer database and was predictive of poorer clinical staging and survival status. HNRNPA2B1 expression was also closely linked to TMB, MSI, tumor stemness, and chemotherapy response. HNRNPA2B1 plays a significant role in the TME and is involved in the regulation of novel immunotherapies. Its expression is significantly associated with the infiltration of macrophages, dendritic cells, NK cells, and T cells. Furthermore, HNRNPA2B1 is closely associated with immune checkpoints, immune-stimulatory genes, immune-inhibitory genes, MHC genes, chemokines, and chemokine receptors. We performed a comprehensive evaluation of HNRNPA2B1, revealing its potential role as a prognostic indicator for patients and its immunomodulatory functions.

Asunto(s)

Biomarcadores de Tumor , Biología Computacional , Regulación Neoplásica de la Expresión Génica , Ribonucleoproteína Heterogénea-Nuclear Grupo A-B , Neoplasias , Microambiente Tumoral , Humanos , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Ribonucleoproteína Heterogénea-Nuclear Grupo A-B/genética , Ribonucleoproteína Heterogénea-Nuclear Grupo A-B/metabolismo , Pronóstico , Neoplasias/inmunología , Neoplasias/genética , Neoplasias/mortalidad , Neoplasias/diagnóstico , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Biología Computacional/métodos , Inestabilidad de Microsatélites , Bases de Datos Genéticas , Mutación , Linfocitos Infiltrantes de Tumor/inmunología , Linfocitos Infiltrantes de Tumor/metabolismo

RESUMEN

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, which lead to various findings in transcriptomic research. This study addresses these challenges by integrating 34 datasets, including 26 control groups, 6 prognostic datasets and 2 single-cell RNA sequencing (scRNA-seq) datasets to identify 10,000 AML-related genes (ARGs). We focused on genes with low variability and high consistency and successfully discovered 191 AML signatures (ASs). Leveraging machine learning techniques, specifically the XGBoost model and our custom framework, we classified AML subtypes with both scRNA-seq and bulk RNA-seq data, complementing the ELN2022 classification approach. Our research also identified promising treatments for AML through drug repurposing, with solasonine showing potential efficacy for high-risk AML patients, supported by molecular docking and transcriptomic analyses. To enhance reproducibility and customizability, we developed CSAMLdb, a user-friendly database platform. It facilitates the reuse and personalized analysis of nearly all results obtained in this research, including single-gene prognostics, multi-gene scoring, enrichment analysis, machine learning risk assessment, drug repositioning analysis and literature abstract named entity recognition. CSAMLdb is available at http://www.csamldb.com.

Asunto(s)

Reposicionamiento de Medicamentos , Perfilación de la Expresión Génica , Leucemia Mieloide Aguda , Transcriptoma , Humanos , Leucemia Mieloide Aguda/genética , Leucemia Mieloide Aguda/tratamiento farmacológico , Reposicionamiento de Medicamentos/métodos , Transcriptoma/genética , Perfilación de la Expresión Génica/métodos , Aprendizaje Automático , Reproducibilidad de los Resultados , Pronóstico , Regulación Leucémica de la Expresión Génica/efectos de los fármacos , Biología Computacional/métodos , Simulación del Acoplamiento Molecular , Bases de Datos Genéticas

RESUMEN

IGFBP6, a member of the IGF binding protein (IGFBP) family, is a specific inhibitor of insulin-like growth factor II (IGF-II) and can inhibit the growth of malignant tumors overexpressing IGF-II. Type 2 diabetes (T2D) is a basic disorder of glucose metabolism that can be regulated by IGF-related pathways. We performed bioinformatics analysis of the TCGA database to explore the possible mechanism of IGFBP6 in breast cancer (BC) metabolism and prognosis and collected clinical samples from BC patients with and without T2D to compare and verify the prognostic effect of IGFBP6. In our study, the levels of IGFBP1-6 were positively correlated with overall survival (OS) in patients with breast cancer. IGFBP6 was upregulated in estrogen receptor (ER)-positive BC, and ER-positive and progesterone receptor (PR) positive patients had a higher expression level of IGFBP6 than ER-negative and PR-negative patients. IGFBP6 could be used as an independent prognostic factor in BC. The expression of IGFBP6 was decreased in BC tissue, and BC tissue from patients with T2D had lower IGFBP6 expression levels than BC tissue from patients without T2D. IGFBP6 is mainly involved in the PI3K-Akt and TGF-ß signaling pathways and tumor microenvironment regulation. In terms of metabolism, the expression of IGFBP6 was negatively correlated with that of most glucose metabolism-related genes. IGFBP6 expression was mainly correlated with mutations in TP53, PIK3CA, CDH1, and MAP3K1. In addition, the upregulation of IGFBP6 in BC increased the drug sensitivity to docetaxel, paclitaxel and gemcitabine. Overall, these results indicated that high expression of IGFBP6 is associated with a good prognosis in BC patients, especially in those without T2D. It is not only involved in the maintenance of the tumor microenvironment in BC but also inhibits the energy metabolism of cancer cells through glucose metabolism-related pathways. These findings may provide a new perspective on IGFBP6 as a potential prognostic marker for BC.

Asunto(s)

Biomarcadores de Tumor , Neoplasias de la Mama , Proteína 6 de Unión a Factor de Crecimiento Similar a la Insulina , Humanos , Femenino , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/patología , Neoplasias de la Mama/genética , Neoplasias de la Mama/mortalidad , Pronóstico , Biomarcadores de Tumor/metabolismo , Proteína 6 de Unión a Factor de Crecimiento Similar a la Insulina/metabolismo , Proteína 6 de Unión a Factor de Crecimiento Similar a la Insulina/genética , Regulación Neoplásica de la Expresión Génica , Biología Computacional/métodos , Transducción de Señal , Glucosa/metabolismo , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/patología , Persona de Mediana Edad , Estimación de Kaplan-Meier , Bases de Datos Genéticas

RESUMEN

Virulence factor genes (VFGs) play pivotal roles in bacterial infections and have been identified within the human gut microbiota. However, their involvement in chronic diseases remains poorly understood. Here, we establish an expanded VFG database (VFDB 2.0) consisting of 62,332 nonredundant orthologues and alleles of VFGs using species-specific average nucleotide identity ( https://github.com/Wanting-Dong/MetaVF_toolkit/tree/main/databases ). We further develop the MetaVF toolkit, facilitating the precise identification of pathobiont-carried VFGs at the species level. A thorough characterization of VFGs for 5452 commensal isolates from healthy individuals reveals that only 11 of 301 species harbour these factors. Further analyses of VFGs within the gut microbiomes of nine chronic diseases reveal both common and disease-specific VFG features. Notably, in type 2 diabetes patients, long HiFi sequencing confirms that shared VF features are carried by pathobiont strains of Escherichia coli and Klebsiella pneumoniae. These findings underscore the critical importance of identifying and understanding VFGs in microbiome-associated diseases.

Asunto(s)

Microbioma Gastrointestinal , Factores de Virulencia , Humanos , Factores de Virulencia/genética , Enfermedad Crónica , Microbioma Gastrointestinal/genética , Klebsiella pneumoniae/genética , Klebsiella pneumoniae/patogenicidad , Klebsiella pneumoniae/aislamiento & purificación , Diabetes Mellitus Tipo 2/microbiología , Diabetes Mellitus Tipo 2/genética , Escherichia coli/genética , Escherichia coli/patogenicidad , Escherichia coli/aislamiento & purificación , Bacterias/genética , Bacterias/clasificación , Bacterias/aislamiento & purificación , Bacterias/patogenicidad , Bases de Datos Genéticas , Infecciones Bacterianas/microbiología

RESUMEN

BACKGROUND: Clear cell renal cell carcinoma (ccRCC), the predominate histological type of renal cell carcinoma (RCC), has been extensively studied, with poor prognosis as the stage increases. Research findings consistently indicated that the PI3K-Akt pathway is commonly dysregulated across various cancer types, including ccRCC. Targeting the PI3K-Akt pathway held promise as a potential therapeutic approach for treating ccRCC. Development and validation of PI3K-Akt pathway-related genes related biomarkers can enhance healthcare management of patients with ccRCC. PURPOSE: This study aimed to identify the key genes in the PI3K-Akt pathway associated with the diagnosis and prognosis of CCRCC using data mining from the Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO) datasets. METHODS: The purpose of this study is to use bioinformatics methods to screen data sets and clinicopathological characteristics associated with ccRCC patients. The exhibited significantly differential expressed genes (DEGs) associated with the PI3K-Akt pathway were examined by KEGG. In addition, Kaplan-Meier (KM) analysis used to estimate the survival function of the differential genes by using the UALCAN database and graphPad Prism 9.0. And exploring the association between the expression levels of the selected genes and the survival status and time of patients with ccRCC based on SPSS22.0. Finally, a multigene prognostic model was constructed to assess the prognostic risk of ccRCC patients. RESULTS: A total of 911 genes with common highly expressed were selected based on the GEO and TCGA databases. According to the KEGG pathway analysis, there were 42 genes enriched in PI3K-Akt signalling pathway. And seven of highly expressed genes were linked to a poor prognosis in ccRCC. And a multigene prognostic model was established based on IL2RG, EFNA3, and MTCP1 synergistic expression might be utilized to predict the survival of ccRCC patients. CONCLUSIONS: Three PI3K-Akt pathway-related genes may be helpful to identify the prognosis and molecular characteristics of ccRCC patients and to improve therapeutic regimens, and these risk characteristics might be further applied in the clinic.

Asunto(s)

Biomarcadores de Tumor , Carcinoma de Células Renales , Regulación Neoplásica de la Expresión Génica , Neoplasias Renales , Fosfatidilinositol 3-Quinasas , Proteínas Proto-Oncogénicas c-akt , Transducción de Señal , Humanos , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/mortalidad , Proteínas Proto-Oncogénicas c-akt/genética , Proteínas Proto-Oncogénicas c-akt/metabolismo , Pronóstico , Neoplasias Renales/genética , Neoplasias Renales/mortalidad , Neoplasias Renales/patología , Fosfatidilinositol 3-Quinasas/genética , Fosfatidilinositol 3-Quinasas/metabolismo , Biomarcadores de Tumor/genética , Transducción de Señal/genética , Masculino , Femenino , Biología Computacional , Perfilación de la Expresión Génica , Bases de Datos Genéticas , Persona de Mediana Edad , Estimación de Kaplan-Meier

RESUMEN

Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.

Asunto(s)

Biología Computacional , Perfilación de la Expresión Génica , Transcriptoma , Transcriptoma/genética , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Humanos , Análisis por Conglomerados , Algoritmos , Redes Neurales de la Computación , Animales , Bases de Datos Genéticas

RESUMEN

BACKGROUND: The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration. RESULTS: To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data. CONCLUSIONS: In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC .

Asunto(s)

Variación Genética , Programas Informáticos , Humanos , Variación Genética/genética , Bases de Datos Genéticas , Genómica/métodos , Genotipo

RESUMEN

Background: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong association between metabolic disorders and sPTB. To determine the metabolic alterations in sPTB patients, we used various bioinformatics methods to analyze the abnormal changes in metabolic pathways in the preterm placenta via existing datasets. Methods: In this study, we integrated two datasets (GSE203507 and GSE174415) from the NCBI GEO database for the following analysis. We utilized the "Deseq2" R package and WGCNA for differentially expressed genes (DEGs) analysis; the identified DEGs were subsequently compared with metabolism-related genes. To identify the altered metabolism-related pathways and hub genes in sPTB patients, we performed multiple functional enrichment analysis and applied three machine learning algorithms, LASSO, SVM-RFE, and RF, with the hub genes that were verified by immunohistochemistry. Additionally, we conducted single-sample gene set enrichment analysis to assess immune infiltration in the placenta. Results: We identified 228 sPTB-related DEGs that were enriched in pathways such as arachidonic acid and glutathione metabolism. A total of 3 metabolism-related hub genes, namely, ANPEP, CKMT1B, and PLA2G4A, were identified and validated in external datasets and experiments. A nomogram model was developed and evaluated with 3 hub genes; the model could reliably distinguish sPTB patients and term labor patients with an area under the curve (AUC) > 0.75 for both the training and validation sets. Immune infiltration analysis revealed immune dysregulation in sPTB patients. Conclusion: Three potential hub genes that influence the occurrence of sPTB through shadow participation in placental metabolism were identified; these results provide a new perspective for the development and targeting of treatments for sPTB.

Asunto(s)

Biología Computacional , Aprendizaje Automático , Placenta , Nacimiento Prematuro , Humanos , Nacimiento Prematuro/genética , Nacimiento Prematuro/metabolismo , Femenino , Biología Computacional/métodos , Embarazo , Placenta/metabolismo , Perfilación de la Expresión Génica , Recién Nacido , Redes y Vías Metabólicas/genética , Redes Reguladoras de Genes , Bases de Datos Genéticas

RESUMEN

Copper-induced cell death is a novel mechanism of cell death, which is defined as cuproptosis. The increasing level of copper can produce toxicity in cells and may cause the occurrence of cell death. Several previous studies have proved that cuproptosis has a tight association with various cancers. Thus, the discovery of relationships between cuproptosis-related genes (CRGs) and human cancers is of great importance. Pan-cancer analysis can efficiently help researchers find out the relationship between multiple cancers and target genes precisely and make various prognostic analyses on cancers and cancer patients. Pan-cancer web servers can provide researchers with direct results of pan-cancer prognostic analyses, which can greatly improve the efficiency of their work. However, to date, no web server provides pan-cancer analysis about CRGs. Therefore, we introduce the cuproptosis pan-cancer analysis database (CuPCA), the first database for various analysis results of CRGs through 33 cancer types. CuPCA is a user-friendly resource for cancer researchers to gain various prognostic analyses between cuproptosis and cancers. It provides single CRG pan-cancer analysis, multi-CRGs pan-cancer analysis, multi-CRlncRNA pan-cancer analysis, and mRNA-circRNA-lncRNA conjoint analysis. These analysis results can not only indicate the relationship between cancers and cuproptosis at both gene level and protein level, but also predict the conditions of different cancer patients, which include their clinical condition, survival condition, and their immunological condition. CuPCA procures the delivery of analyzed data to end users, which improves the efficiency of wide research as well as releases the value of data resources. Database URL: http://cupca.cn/.

Asunto(s)

Bases de Datos Genéticas , Internet , Neoplasias , Humanos , Neoplasias/genética , Programas Informáticos

RESUMEN

Thalassemia is one of the most prevalent monogenic disorders in low- and middle-income countries (LMICs). There are an estimated 270 million carriers of hemoglobinopathies (abnormal hemoglobins and/or thalassemia) worldwide, necessitating global methods and solutions for effective and optimal therapy. LMICs are disproportionately impacted by thalassemia, and due to disparities in genomics awareness and diagnostic resources, certain LMICs lag behind high-income countries (HICs). This spurred the establishment of the Global Globin Network (GGN) in 2015 at UNESCO, Paris, as a project-wide endeavor within the Human Variome Project (HVP). Primarily aimed at enhancing thalassemia clinical services, research, and genomic diagnostic capabilities with a focus on LMIC needs, GGN aims to foster data collection in a shared database by all affected nations, thus improving data sharing and thalassemia management. In this paper, we propose a minimum requirement for establishing a genomic database in thalassemia based on the HVP database guidelines. We suggest using an existing platform recommended by HVP, the Leiden Open Variation Database (LOVD) (https://www.lovd.nl/). Adoption of our proposed criteria will assist in improving or supplementing the existing databases, allowing for better-quality services for individuals with thalassemia. Database URL: https://www.lovd.nl/.

Asunto(s)

Bases de Datos Genéticas , Talasemia , Humanos , Talasemia/genética , Globinas/genética , Genómica/métodos , Variación Genética

RESUMEN

Porphyria is a group of rare metabolic disorders caused by mutations in the genes encoding crucial enzymes in the heme biosynthetic pathway. However, the lack of comprehensive genetic analysis of porphyria patients in the Chinese population makes identifying and diagnosing carriers of the condition challenging. Using the ChinaMAP database, we determined the frequencies of P/LP porphyria-associated gene variants according to the ACMG guidelines. We also calculated the carrier rates and prevalence of each type of porphyria in the Chinese population under Hardy-Weinberg equilibrium. Compared with the variants in the gnomAD database, the genetic spectrum of porphyria-related P/LP variants in the Chinese population is distinct. In the ChinaMAP database, we identified 23 variants. We estimated the carrier rates for autosomal dominant porphyrias (AIP, HCP, VP, PCT) in the Chinese population to be 1/1059, 1/1513, 1/10588, and 1/1765, respectively. For autosomal recessive porphyrias (ADP, EPP, HEP, CEP), the estimated carrier rates were 1/5294, 1/2117, 1/1765, and 1/2647, respectively, with predicted prevalence rates of 8.92 × 10-9, 7.51 × 10-5, 8.02 × 10-8, and 3.57 × 10-8, respectively. Notably, 12 of the variants we identified were unique to the Chinese population. The predicted prevalence rate of EPP was the highest among the various types of porphyria in the Chinese population, while the others were moderate to low. This is the first comprehensive genetic study on porphyria in the Chinese population. Clarifying the genetic characteristics of various porphyria types among the Chinese population provides scientifically sound reference data for both research and genetic screening to identify porphyria carriers.

Asunto(s)

Pueblos del Este de Asia , Porfirias , Humanos , China/epidemiología , Bases de Datos Genéticas , Pueblos del Este de Asia/genética , Mutación , Porfirias/genética , Porfirias/epidemiología , Prevalencia

RESUMEN

Clustering is widely used in bioinformatics and many other fields, with applications from exploratory analysis to prediction. Many types of data have associated uncertainty or measurement error, but this is rarely used to inform the clustering. We present Dirichlet Process Mixtures with Uncertainty (DPMUnc), an extension of a Bayesian nonparametric clustering algorithm which makes use of the uncertainty associated with data points. We show that DPMUnc out-performs existing methods on simulated data. We cluster immune-mediated diseases (IMD) using GWAS summary statistics, which have uncertainty linked with the sample size of the study. DPMUnc separates autoimmune from autoinflammatory diseases and isolates other subgroups such as adult-onset arthritis. We additionally consider how DPMUnc can be used to cluster gene expression datasets that have been summarised using gene signatures. We first introduce a novel procedure for generating a summary of a gene signature on a dataset different to the one where it was discovered, which incorporates a measure of the variability in expression across signature genes within each individual. We summarise three public gene expression datasets containing patients with a range of IMD, using three relevant gene signatures. We find association between disease and the clusters returned by DPMUnc, with clustering structure replicated across the datasets. The significance of this work is two-fold. Firstly, we demonstrate that when data has associated uncertainty, this uncertainty should be used to inform clustering and we present a method which does this, DPMUnc. Secondly, we present a procedure for using gene signatures in datasets other than where they were originally defined. We show the value of this procedure by summarising gene expression data from patients with immune-mediated diseases using relevant gene signatures, and clustering these patients using DPMUnc.

Asunto(s)

Algoritmos , Teorema de Bayes , Biología Computacional , Humanos , Análisis por Conglomerados , Incertidumbre , Biología Computacional/métodos , Estudio de Asociación del Genoma Completo/métodos , Estudio de Asociación del Genoma Completo/estadística & datos numéricos , Perfilación de la Expresión Génica/estadística & datos numéricos , Perfilación de la Expresión Génica/métodos , Bases de Datos Genéticas/estadística & datos numéricos , Simulación por Computador