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Objective: To explore the potential pathogenic genes of intestinal metaplasia., Methods: Twenty-one patients with intestinal metaplasia admitted to the Department of Gastroenterology at the Second Affiliated Hospital of Anhui University of Chinese Medicine from January, 2022 to June, 2022, and 21 healthy subjects undergoing gastroscopic examination during the same period were enrolled in this study. All the participants underwent gastroscopy and pathological examination, and gastric tissue samples were collected for transcriptome sequencing to screen for differentially expressed genes (DEGs). The biological functions of the DEGs were analyzed using bioinformatics analysis, and qRT-PCR was used to validate the results., Results: Transcriptomic sequencing identified a total of 1373 DEGs, including 827 upregulated and 546 downregulated ones. The top 6 upregulated genes ( AGMAT , CCL25 , FABP1 , CDX1 , SPINK4 , and MUC2 ), ranked based on their significance and average expression level, were selected for validation, and qRT-PCR showed significant upregulation of their mRNAs in the gastric tissues of patients with intestinal metaplasia ( P < 0.05)., Conclusion: AGMAT , CCL25 , FABP1 , CDX1 , SPINK4 , and MUC2 participate in the occurrence and development of intestinal metaplasia, and may serve as potential biomarkers for diagnosing intestinal metaplasia.

The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Bayesian network model is widely used for reverse engineering of biological network structures. An advantage of this model is its capability to integrate prior knowledge into the model learning process, which can lead to improving the quality of the network reconstruction outcome. Some previous works have explored this area with focus on using prior knowledge of the direct molecular links, except for a few recent ones proposing to examine the effects of molecular orderings. In this study, we propose a Bayesian network model that can integrate both direct links and orderings into the model. Random weights are assigned to these two types of prior knowledge to alleviate bias toward certain types of information. We evaluate our model performance using both synthetic data and biological data for the RAF signaling network, and illustrate the significant improvement on network structure reconstruction of the proposing models over the existing methods. We also examine the correlation between the improvement and the abundance of ordering prior knowledge. To address the issue of generating prior knowledge, we propose an approach to automatically extract potential molecular orderings from knowledge resources such as Kyoto Encyclopedia of Genes and Genomes (KEGG) database and Gene Ontology (GO) annotation.

A Bayesian network model can be used to study the structures of gene regulatory networks. It has the ability to integrate information from both prior knowledge and experimental data. In this study, we propose an approach to efficiently integrate global ordering information into model learning, where the ordering information specifies the indirect relationships among genes. We demonstrate that, compared with a traditional Bayesian network model that uses only local prior knowledge, utilising additional global ordering knowledge can significantly improve the model's performance. The magnitude of this improvement depends on abundance of global ordering information and data quality.

Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.