The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed. However, the reliability of these tools still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, to analyze the characteristics of the short eukaryotic promoter sequences, and accurately recognize the human and mouse promoter sequences. DeePromoter combines a convolutional neural network (CNN) and a long short-term memory (LSTM). Additionally, instead of using non-promoter regions of the genome as a negative set, we derive a more challenging negative set from the promoter sequences. The proposed negative set reconstruction method improves the discrimination ability and significantly reduces the number of false positive predictions. Consequently, DeePromoter outperforms the previously proposed promoter prediction tools. In addition, a web-server for promoter prediction is developed based on the proposed methods and made available at https://home.jbnu.ac.kr/NSCL/deepromoter.htm.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6460014 | PMC |
http://dx.doi.org/10.3389/fgene.2019.00286 | DOI Listing |
Biomolecules
January 2025
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
The DNA methylation of can regulate its gene expression and may play a role in the occurrence and progression of colorectal cancer (CRC). However, the association between DNA methylation and the prognosis of CRC patients has not yet been reported. In this study, differential methylation analysis was conducted in both blood and tissue cohorts, and differential expression analysis was performed in the tissue cohort with in vitro validation.
View Article and Find Full Text PDFBiomolecules
January 2025
Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA.
(1) Background: Prostate cancer treatment efficacy is significantly influenced by androgen receptor (AR) signaling pathways. SLC22A3, a membrane transporter, has been linked to SNP rs9364554 risk loci for drug efficacy in prostate cancer. (2) Methods: We examined the location of SNP rs9364554 in the genome and utilized TCGA and other publicly available datasets to analyze the association of this SNP with transcription levels.
View Article and Find Full Text PDFActa Neuropathol Commun
January 2025
Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Background: Meningioma represents the most common intracranial tumor in adults. However, it is rare in pediatric patients. We aimed to demonstrate the clinicopathological characteristics and long-term outcome of pediatric meningiomas (PMs).
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, People's Republic of China.
Cellular senescence is a key promoter of tumorigenesis and malignant progression. This study aimed to develop a predictive model for assessing cellular senescence in gastric cancer (GC) outcomes. We identified senescence-related genes and lncRNAs from 375 stomach adenocarcinoma (STAD) patients and established a prognostic senescence score using multivariate Cox regression, validated in testing, TCGA-STAD and the combined TCGA-COAD and READ cohorts.
View Article and Find Full Text PDFAnn Clin Lab Sci
November 2024
Department of Thoracic Surgery, First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Objective: To identify key genes associated with the prognosis of non-small cell lung cancer (NSCLC) through bioinformatics analysis and experimental validation, exploring the expression of the TRIM58 gene and its potential effect as a tumor suppressor.
Methods: In this study, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) related to lung adenocarcinoma and lung squamous cell carcinoma were selected from the TCGA dataset, with the Limma package in R software used for further filtering and intersection, followed by the assessment of the relationship between these genes and NSCLC prognosis using log-rank tests and univariate Cox regression analysis. Meanwhile, six clinical NSCLC cancer and adjacent tissue samples were collected, along with the detection of TRIM58 mRNA and protein levels using RT-PCR and Western blot.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!