Antibiotic resistance of Helicobacter pylori in Chinese children: A multicenter study from 2016 to 2023.

BACKGROUND: To investigate the antibiotic resistance of Helicobacter pylori (H. pylori) strains to clarithromycin, metronidazole, amoxicillin, levofloxacin, furazolidone, and tetracycline in Chinese children. MATERIALS AND METHODS: This multicenter, retrospective study was conducted from January 2016 through May 2023. Gastric mucosa biopsies were obtained from pediatric participants who underwent upper gastrointestinal endoscopy at 96 hospitals in northern, southwestern, and southeastern China. The susceptibility of H. pylori to six commonly used antibiotics was determined by agar dilution method. RESULTS: Among the 3074 H. pylori isolates, 36.7% were resistant to clarithromycin, 77.3% to metronidazole, 16.6% to levofloxacin, and 0.3% to amoxicillin. No strains were detected to be resistant to furazolidone or tetracycline. During the 8-year study period, resistance to clarithromycin and metronidazole showed a significant upward trend, while the resistance pattern of the other antibiotics demonstrated a slight but nonsignificant fluctuation. Significant regional differences were found in the distribution of clarithromycin resistance among the northern (66.0%), southwestern (48.2%), and southeastern (34.6%) regions. The metronidazole resistance rate was significantly lower in the southeastern coastal region (76.3%) than in the other two regions (88.2% in the north and 87.7% in the southwest). Multi-drug resistance for two or more antibiotics was detected in 36.3% of the H. pylori strains, and the predominant multi-resistance pattern was the dual resistance to clarithromycin and metronidazole. CONCLUSIONS: The prevalence of H. pylori resistance to clarithromycin and metronidazole is rather high in Chinese children and has been increasing over time. A relatively high resistance rate to levofloxacin was also noticed in children, while almost all strains were susceptible to amoxicillin, furazolidone, and tetracycline. It will be of great clinical significance to continuously monitor the antibiotic-resistance patterns of H. pylori in the pediatric population.

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

MOTIVATION: Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. RESULTS: We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. AVAILABILITY AND IMPLEMENTATION: Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . CONTACT: shuwj@bmi.ac.cn or boxc@bmi.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO.

MOTIVATION: Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved. RESULTS: ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest. AVAILABILITY AND IMPLEMENTATION: The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache.

iFORM: Incorporating Find Occurrence of Regulatory Motifs.

Accurately identifying the binding sites of transcription factors (TFs) is crucial to understanding the mechanisms of transcriptional regulation and human disease. We present incorporating Find Occurrence of Regulatory Motifs (iFORM), an easy-to-use and efficient tool for scanning DNA sequences with TF motifs described as position weight matrices (PWMs). Both performance assessment with a receiver operating characteristic (ROC) curve and a correlation-based approach demonstrated that iFORM achieves higher accuracy and sensitivity by integrating five classical motif discovery programs using Fisher's combined probability test. We have used iFORM to provide accurate results on a variety of data in the ENCODE Project and the NIH Roadmap Epigenomics Project, and the tool has demonstrated its utility in further elucidating individual roles of functional elements. Both the source and binary codes for iFORM can be freely accessed at https://github.com/wenjiegroup/iFORM. The identified TF binding sites across human cell and tissue types using iFORM have been deposited in the Gene Expression Omnibus under the accession ID GSE53962.