Biomedical subjects

Cheng Peng

Publications and source records attributed to Cheng Peng.

3 recordsLinked to original sources

PubMed2026

ViralQC: a tool for assessing completeness and contamination of predicted viral contigs.

MOTIVATION: Viruses represent the most abundant biological entities on Earth, playing vital roles in diverse ecosystems. Cataloging viruses across various environments is essential for understanding their properties and functions. Metagenomic sequencing has emerged as the most comprehensive method for virus discovery. However, distinguishing viral sequences from the vast background of microbial organisms in metagenomic data remains a significant challenge. Existing tools experience varying degrees of false positive rates due to noise in sequencing and assembly, and the integration of proviruses into microbial genomes. This highlights the urgent need for an accurate and efficient method to evaluate the quality of viral contigs. RESULTS: To address these challenges, we introduce ViralQC, a tool designed to assess the quality of viral contigs or bins. ViralQC identifies microbial contamination within putative viral sequences using an ensemble framework powered by DNA and protein foundation models and estimates completeness by analyzing protein organization. We evaluated ViralQC on multiple datasets and compared its performance against the state-of-the-art tool, CheckV. Leveraging both DNA and protein foundation models, ViralQC achieves higher sensitivity on contamination detection for contigs longer than 10 kbp while maintaining comparable accuracy. Additionally, ViralQC delivers more accurate estimation on contigs with completeness > 50%. AVAILABILITY: The source code of ViralQC is available via: https://github.com/ChengPENG-wolf/ViralQC.

Software↗

PubMed2026

[Effect of Cancer Antigen-125 Elimination Rate Constant K and BRCA Mutation Status on the Prognosis of Interval Debulking Surgery in Advanced High-Grade Serous Ovarian Cancer].

OBJECTIVE: To investigate the predictive value of the cancer antigen-125 elimination rate constant K (KELIM) for treatment response and prognosis in patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS), and to analyze the combined prognostic significance of KELIM and the mutation status of breast cancer susceptibility gene (BRCA). METHODS: A total of 106 patients with advanced HGSOC who had undergone NACT-IDS were retrospectively enrolled. The KELIM values during neoadjuvant chemotherapy were calculated, and patients were divided into high- and low-KELIM groups using a cutoff value of 1.0. Clinicopathological characteristics, R0 resection rates, and platinum sensitivity rates were compared between the two groups. Logistic regression analysis was performed to identify predictive factors for R0 resection, while Kaplan-Meier survival analysis and Cox proportional hazards regression were performed to evaluate factors associated with progression-free survival (PFS). Furthermore, the patients were stratified according to both KELIM and BRCA status to assess the risk of platinum-resistant recurrence in each subgroup. RESULTS: The R0 resection rate was higher in the KELIM ≥ 1 group than in the KELIM < 1 group (77.1% vs 55.2%), and the difference was statistically significant (P = 0.024). Multivariate logistic regression analysis showed that KELIM was an independent predictor of R0 resection (odds ratio [OR] = 2.922, 95% CI: 1.112-7.678). Survival analysis demonstrated longer PFS in the KELIM ≥1 group compared with that in the KELIM <1 group (33.0 months vs 18.0 months), and the difference was statistically significant (P < 0.001). Multivariate Cox regression analysis showed that KELIM ≥ 1 was associated with a reduced risk of disease progression (hazard ratio [HR] = 0.481, 95% CI: 0.280-0.826). Combined stratification analysis revealed that no platinum-resistant recurrence was observed in the subgroup with both KELIM ≥1 and a BRCA-positive status (0/21). Compared with patients with KELIM <1 and a BRCA-negative status, this subgroup exhibited a lower risk of platinum-resistant recurrence (OR = 0.053, 95% CI: 0.003-0.932, P = 0.006). CONCLUSION: KELIM is an effective dynamic biomarker for predicting surgical outcomes and PFS in patients undergoing NACT-IDS. Combined stratification by KELIM and BRCA status allows more precise identification of the patient population with both KELIM ≥1 and BRCA-positive status, who have an extremely low risk of platinum-resistant recurrence, thereby providing an important basis for individualized treatment and risk stratification management in patients with advanced HGSOC.

Humans↗

PubMed2025

GiantHunter: accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search.

MOTIVATION: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to their widespread environmental presence and critical roles in processes such as host metabolic reprogramming and nutrient cycling. Metagenomic sequencing has emerged as a powerful tool for uncovering novel NCLDVs in environmental samples. However, identifying NCLDV sequences in metagenomic data remains challenging due to their high genomic diversity, limited reference genomes, and shared regions with other microbes. Existing alignment-based and machine learning methods struggle with achieving optimal trade-offs between sensitivity and precision. RESULTS: In this work, we present GiantHunter, a reinforcement learning-based tool for identifying NCLDVs from metagenomic data. By employing a Monte Carlo tree search strategy, GiantHunter dynamically selects representative non-NCLDV sequences as the negative training data, enabling the model to establish a robust decision boundary. Benchmarking on rigorously designed experiments shows that GiantHunter achieves high precision while maintaining competitive sensitivity, improving the F1-score by 10% and reducing computational cost by 90% compared to the second-best method. To demonstrate its real-world utility, we applied GiantHunter to 60 metagenomic datasets collected from six cities along the Yangtze River, located both upstream and downstream of the Three Gorges Dam. The results reveal significant differences in NCLDV diversity correlated with proximity to the dam, likely influenced by reduced flow velocity caused by the dam. These findings highlight GiantHunter's potential to advance our understanding of NCLDVs and their ecological roles in diverse environments. AVAILABILITY AND IMPLEMENTATION: The source code of GiantHunter is available via: https://github.com/FuchuanQu/GiantHunter.

Metagenomics↗