Genomics, proteomics & bioinformatics最新文献_第3页

Evaluative Methodology for HRD Testing: Development of Standard Tools for Consistency Assessment. HRD测试的评估方法：一致性评估标准工具的开发。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-10 DOI: 10.1093/gpbjnl/qzaf017

Zheng Jia, Yaqing Liu, Shoufang Qu, Wenbin Li, Lin Gao, Lin Dong, Yun Xing, Yadi Cheng, Huan Fang, Yuting Yi, Yuxing Chu, Chao Zhang, Yanming Xie, Chunli Wang, Zhe Li, Zhihong Zhang, Zhipeng Xu, Yang Wang, Wenxin Zhang, Xiaoping Gu, Shuang Yang, Jinghua Li, Liangshen Wei, Yuanting Zheng, Guohui Ding, Leming Shi, Xin Yi, Jianming Ying, Jie Huang

{"title":"Evaluative Methodology for HRD Testing: Development of Standard Tools for Consistency Assessment.","authors":"Zheng Jia, Yaqing Liu, Shoufang Qu, Wenbin Li, Lin Gao, Lin Dong, Yun Xing, Yadi Cheng, Huan Fang, Yuting Yi, Yuxing Chu, Chao Zhang, Yanming Xie, Chunli Wang, Zhe Li, Zhihong Zhang, Zhipeng Xu, Yang Wang, Wenxin Zhang, Xiaoping Gu, Shuang Yang, Jinghua Li, Liangshen Wei, Yuanting Zheng, Guohui Ding, Leming Shi, Xin Yi, Jianming Ying, Jie Huang","doi":"10.1093/gpbjnl/qzaf017","DOIUrl":"10.1093/gpbjnl/qzaf017","url":null,"abstract":"Homologous recombination deficiency (HRD) has emerged as a critical prognostic and predictive biomarker in oncology. However, current testing methods, especially those reliant on targeted panels, are plagued by inconsistent results from the same samples. This highlights the urgent need for standardized benchmarks to evaluate HRD assay performance. In phases IIa and IIb of the Chinese HRD Harmonization Project, we developed ten pairs of well-characterized DNA reference materials derived from lung, breast, and melanoma cancer cell lines and their matched normal cell lines, keeping each paired with seven cancer-to-normal mass ratios. Reference datasets for allele-specific copy number variations (ASCNVs) and HRD scores were established and validated using three sequencing methods and nine analytical pipelines. The genomic instability scores (GISs) of the reference materials ranged from 11 to 96, enabling validation across various thresholds. The ASCNV reference datasets covered a genomic span of 2340 to 2749 Mb, equivalent to 81.2% to 95.4% of the autosomes in the 37d5 reference genome. These benchmarks were subsequently utilized to assess the accuracy and reproducibility of four HRD panel assays, revealing significant variability in both ASCNV detection and HRD scores. The concordance between panel-detected GISs and reference GISs ranged from 0.81 to 0.94, with only two assays exhibiting high overall agreement with Myriad MyChoice CDx for HRD classification. This study also identified specific challenges in ASCNV detection in HRD-related regions and the profound impact of high ploidy on consistency. The established HRD reference materials and datasets provide a robust toolkit for objective evaluation of HRD testing.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer. CIEC：癌症跨组织免疫细胞类型富集和表达图谱可视化。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-10 DOI: 10.1093/gpbjnl/qzae067

Jinhua He, Haitao Luo 罗海涛, Wei Wang 王伟, Dechao Bu 卜德超, Zhengkai Zou 邹正楷, Haolin Wang 王浩霖, Hongzhen Tang, Zeping Han, Wenfeng Luo, Jian Shen, Fangmei Xie, Yi Zhao 赵屹, Zhiming Xiang

{"title":"CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.","authors":"Jinhua He, Haitao Luo 罗海涛, Wei Wang 王伟, Dechao Bu 卜德超, Zhengkai Zou 邹正楷, Haolin Wang 王浩霖, Hongzhen Tang, Zeping Han, Wenfeng Luo, Jian Shen, Fangmei Xie, Yi Zhao 赵屹, Zhiming Xiang","doi":"10.1093/gpbjnl/qzae067","DOIUrl":"10.1093/gpbjnl/qzae067","url":null,"abstract":"Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell types or states. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell type/state map for each context, and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell types/states. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar gene detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12065431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics. 更正：ProtPipe：蛋白质组学和肽组学的多功能数据分析管道。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-10 DOI: 10.1093/gpbjnl/qzaf051

引用次数: 0

rMATS-cloud: Large-scale Alternative Splicing Analysis in the Cloud. rMATS-cloud：云中的大规模可选拼接分析。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-08 DOI: 10.1093/gpbjnl/qzaf036

Jenea I Adams, Eric Kutschera, Qiang Hu, Chun-Jie Liu, Qian Liu, Kathryn Kadash-Edmondson, Song Liu, Yi Xing

{"title":"rMATS-cloud: Large-scale Alternative Splicing Analysis in the Cloud.","authors":"Jenea I Adams, Eric Kutschera, Qiang Hu, Chun-Jie Liu, Qian Liu, Kathryn Kadash-Edmondson, Song Liu, Yi Xing","doi":"10.1093/gpbjnl/qzaf036","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf036","url":null,"abstract":"Although gene expression analysis pipelines are often a standard part of bioinformatics analysis, with many publicly available cloud workflows, cloud-based alternative splicing analysis tools remain limited. Our lab released rMATS in 2014 and has continuously maintained it, providing a fast and versatile solution for quantifying alternative splicing from RNA sequencing (RNA-seq) data. Here, we present rMATS-cloud, a portable version of the rMATS workflow that can be run in virtually any cloud environment suited for biomedical research. We compared the time and cost of running rMATS-cloud with two RNA-seq datasets on three different platforms (Cavatica, Terra, and Seqera). Our findings demonstrate that rMATS-cloud handles RNA-seq datasets with thousands of samples, and therefore is ideally suited for the storage capacities of many cloud data repositories. rMATS-cloud is available at https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-cwl, https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-wdl, and https://dockstore.org/workflows/github.com/Xinglab/rmats-turbo/rmats-turbo-nextflow.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Implication of The Vaginal Microbiome in Female Infertility and Assisted Conception Outcomes. 阴道微生物组在女性不孕症和辅助受孕结果中的意义。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-05 DOI: 10.1093/gpbjnl/qzaf042

Xiuju Chen, Yanyu Sui, Jiayi Gu, Liang Wang, Ningxia Sun

{"title":"The Implication of The Vaginal Microbiome in Female Infertility and Assisted Conception Outcomes.","authors":"Xiuju Chen, Yanyu Sui, Jiayi Gu, Liang Wang, Ningxia Sun","doi":"10.1093/gpbjnl/qzaf042","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf042","url":null,"abstract":"The rise in infertility rates has prompted research into the impact of vaginal microbiota on female fertility and assisted reproduction technology (ART) success. Our study compares the vaginal microbiome of fertile and infertile women and explores its influence on ART outcomes. We analyzed vaginal secretions from 194 infertile women and 100 healthy controls at Shanghai Changzheng Hospital using polymerase chain reaction (PCR) to amplify the 16S rRNA V3-V4 region. A machine learning model predicted infertility based on genus abundances, and the PICRUSt algorithm predicted metabolic pathways related to infertility and ART outcome. The results showed women with infertility exhibited a significantly different vaginal microbial composition compared to healthy women, with the infertility group showing higher microbial diversity. Burkholderia, Pseudomonas, and Prevotella levels were significantly elevated in the vaginal microbiota of the infertility group, while Bifidobacterium and Lactobacillus abundances were reduced. Recurrent implantation failure (RIF) within the infertile population showed even higher diversity of vaginal microbiota, with specific genera such as Mobiluncus, Peptoniphilus, Prevotella, and Varibaculum being more abundant. Eleven metabolic pathways were associated with RIF and infertility, with Prevotella demonstrating stronger correlations. The present study provides insights into the differences in vaginal microbiome between healthy and infertile women, offering a new understanding of how vaginal microbiota may impact infertility and ART outcomes. Our findings underscore the significance of specific microbial taxa in women with RIF, suggesting avenues for targeted interventions to enhance embryo transplantation success rates.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterization of Chronic Lymphocytic Leukemia Immunoglobulin Rearrangements from Partial Read Sequencing. 慢性淋巴细胞白血病免疫球蛋白重排的部分测序特征。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-02 DOI: 10.1093/gpbjnl/qzaf041

Azahara Fuentes-Trillo, Alicia Serrano-Alcalá, Blanca Ferrer-Lores, Laura Ventura-López, Enrique Seda, Ana-Bárbara García-García, Blanca Navarro, María José Terol, Felipe Javier Chaves

{"title":"Characterization of Chronic Lymphocytic Leukemia Immunoglobulin Rearrangements from Partial Read Sequencing.","authors":"Azahara Fuentes-Trillo, Alicia Serrano-Alcalá, Blanca Ferrer-Lores, Laura Ventura-López, Enrique Seda, Ana-Bárbara García-García, Blanca Navarro, María José Terol, Felipe Javier Chaves","doi":"10.1093/gpbjnl/qzaf041","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf041","url":null,"abstract":"The determination of the mutational status in the immunoglobulin variable region is an established prognostic biomarker for chronic lymphocytic leukemia (CLL). The length and inner variability of the variable, diversity, and joining (VDJ) rearranged sequences compromises B-cell clones characterization using next-generation sequencing (NGS), and a standardization is needed to adapt the procedure to the current clinical guidelines. We have developed a complete strategy for sequencing the variable domain of the immunoglobulin heavy chain gene (IGH) locus with a simple, low-cost, and efficient method that allows sequencing using shorter reads (MiSeq 150 × 2) and thus faster obtention of results. Clonality and mutational status determination are performed within the same analysis pipeline. We tested and validated the method using 319 CLL patients previously diagnosed and IGH locus characterized using Sanger sequencing, and 47 healthy donor samples. The analysis method follows a clone-centered consensus sequence strategy, to identify B-cell clones and establish a clonal threshold specific for each patient clonality profile, overcoming limitations of Sanger sequencing which is the gold standard used for immunoglobulin heavy variable (IGHV) mutational status determination.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Leukemia-related Genes based on Time-series Dynamical Network by Integrating Differential Genes. 基于差分基因整合的时间序列动态网络识别白血病相关基因。

Genomics, proteomics & bioinformatics Pub Date : 2025-04-29 DOI: 10.1093/gpbjnl/qzaf037

Jin A, Ju Xiang, Xiangmao Meng, Yue Sheng, Hongling Peng, Min Li

{"title":"Identifying Leukemia-related Genes based on Time-series Dynamical Network by Integrating Differential Genes.","authors":"Jin A, Ju Xiang, Xiangmao Meng, Yue Sheng, Hongling Peng, Min Li","doi":"10.1093/gpbjnl/qzaf037","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf037","url":null,"abstract":"Leukemia is a malignant disease of progressive accumulation characterized by high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed DyNDG, a novel dynamic network-based model, which integrates differentially expressed genes to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The experimental results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/D yNDG.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrative Genome-wide Association Meta-analysis of Aortic Aneurysm and Dissection Identifies Five Novel Genes. 主动脉瘤和夹层的全基因组关联荟萃分析鉴定出五个新基因。

Genomics, proteomics & bioinformatics Pub Date : 2025-04-29 DOI: 10.1093/gpbjnl/qzaf039

Yifan Du, Yunlong Guan, Zhonghe Shao, Minghui Jiang, Minghan Qu, Yifan Kong, Hongji Wu, Da Luo, Shu Peng, Si Li, Xi Cao, Jing Chen, Ping Ye, Jiahong Xia, Xingjie Hao

{"title":"Integrative Genome-wide Association Meta-analysis of Aortic Aneurysm and Dissection Identifies Five Novel Genes.","authors":"Yifan Du, Yunlong Guan, Zhonghe Shao, Minghui Jiang, Minghan Qu, Yifan Kong, Hongji Wu, Da Luo, Shu Peng, Si Li, Xi Cao, Jing Chen, Ping Ye, Jiahong Xia, Xingjie Hao","doi":"10.1093/gpbjnl/qzaf039","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf039","url":null,"abstract":"Aortic aneurysm and dissection (AAD) is a multifaceted condition characterized by significant genetic predisposition and a considerable contribution to cardiovascular-related mortality. Previous studies have suggested that AAD subtypes share similar genetic mechanisms, however, these studies investigated the subtypes separately. Here, we performed a large genome-wide association study (GWAS) meta-analysis for AAD by combining its subtypes, including 11,148 cases and 708,468 controls of European ancestry. We identified 24 susceptibility loci, including four novel loci at 1p21.2 (PALMD), 2p22.2 (CRIM1), 6q22.1 (FRK), and 12q14.3 (HMGA2), which were partially validated in both internal and external populations. Cell type-specific analysis highlighted the artery as the most relevant tissue where the susceptibility variants may exert their effects in a tissue-specific manner. By using four approaches, we prioritized 53 genes, reinforcing the importance of elastic fiber formation and transforming growth factor-beta (TGF-β) signaling in the formation of AAD, and suggested potential target drugs for the treatment. Additionally, various cardiovascular diseases were genetically correlated with AAD, and several cardiovascular risk factors [e.g., body mass index (BMI), lipid levels, and pulse pressure] showed causal associations with AAD, underscoring their shared genetic structures and mechanisms underlying the comorbidity. Moreover, five prioritized genes (PALMD, CRIM1, FRK, HMGA2, and NT5DC1) at the novel loci were supported as regulators of smooth muscle and endothelial cell functions through ex vivo and in vitro experiments. Together, these findings enhance our understanding of the genetic architecture of AAD and provide novel insights into future biological mechanism studies and therapeutic strategies.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UNISOM: Unified Somatic Calling and Machine Learning-based Classification Enhance the Discovery of CHIP. UNISOM：统一的躯体呼叫和基于机器学习的分类增强了对CHIP的发现。

Genomics, proteomics & bioinformatics Pub Date : 2025-04-29 DOI: 10.1093/gpbjnl/qzaf040

Shulan Tian, Garrett Jenkinson, Alejandro Ferrer, Huihuang Yan, Joel A Morales-Rosado, Kevin L Wang, Terra L Lasho, Benjamin B Yan, Saurabh Baheti, Janet E Olson, Linda B Baughn, Wei Ding, Susan L Slager, Mrinal S Patnaik, Konstantinos N Lazaridis, Eric W Klee

{"title":"UNISOM: Unified Somatic Calling and Machine Learning-based Classification Enhance the Discovery of CHIP.","authors":"Shulan Tian, Garrett Jenkinson, Alejandro Ferrer, Huihuang Yan, Joel A Morales-Rosado, Kevin L Wang, Terra L Lasho, Benjamin B Yan, Saurabh Baheti, Janet E Olson, Linda B Baughn, Wei Ding, Susan L Slager, Mrinal S Patnaik, Konstantinos N Lazaridis, Eric W Klee","doi":"10.1093/gpbjnl/qzaf040","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf040","url":null,"abstract":"Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes, predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Computational and Functional Screening Identify G9a Inhibitors for SETD2-Mutant Leukemia. 综合计算和功能筛选确定setd2突变白血病的G9a抑制剂。

Genomics, proteomics & bioinformatics Pub Date : 2025-04-29 DOI: 10.1093/gpbjnl/qzaf035

Ya Zhang, Mengfang Xia, Zhenyi Yi, Pinpin Sui, Xudong He, Liping Wang, Qiyi Chen, Hong-Hu Zhu, Gang Huang, Qian-Fei Wang

{"title":"Integrated Computational and Functional Screening Identify G9a Inhibitors for SETD2-Mutant Leukemia.","authors":"Ya Zhang, Mengfang Xia, Zhenyi Yi, Pinpin Sui, Xudong He, Liping Wang, Qiyi Chen, Hong-Hu Zhu, Gang Huang, Qian-Fei Wang","doi":"10.1093/gpbjnl/qzaf035","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf035","url":null,"abstract":"SETD2, a frequently mutated epigenetic tumor suppressor in acute leukemia, is associated with chemotherapy resistance and poor patient outcomes. To explore potential therapeutics for SETD2-mutant leukemia, we employed an integrated approach combining computational prediction with epigenetic compound library screening. This approach identified G9a inhibitors as promising candidates, capable of reversing gene expression signatures associated with Setd2 deficiency and selectively inhibiting SETD2-deficient cells. RNA-sequencing analysis revealed that G9a inhibitor significantly downregulated Myc and Myc-regulated genes involved in translation, DNA replication, and G1/S transition in Setd2-mutant cells. Further chromatin immunoprecipitation sequencing analysis showed that G9a inhibition reduced H3K9me2 levels at the long non-coding RNA Mir100hg locus, coinciding with specific upregulation of the embedded microRNA let-7a-2 in Setd2-mutant cells. Given the established role of let-7a in MYC suppression, these findings suggest a potential mechanism by which G9a inhibitors induce MYC downregulation in SETD2-mutant leukemia. Additionally, correlation analysis between computational prediction and phenotypic outcomes highlighted the MYC signature as a key predictor of drug efficacy. Collectively, our study identifies G9a inhibitors as a promising therapeutic avenue for SETD2-mutant leukemia and provides novel insights into refining drug prediction strategies.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144039579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0