Genomics, proteomics & bioinformatics最新文献

Biomedical Big Data and Artificial Intelligence in Blood. 生物医学大数据与血液中的人工智能。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-30 DOI: 10.1093/gpbjnl/qzaf043

Fuhong He, Zhaojun Zhang, Xiangdong Fang, Qian-Fei Wang

引用次数: 0

Unveiling Neonatal Pneumonia Microbiome by High-Throughput Sequencing and Droplet Culturomics. 通过高通量测序和液滴培养揭示新生儿肺炎微生物组。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-29 DOI: 10.1093/gpbjnl/qzaf047

Zerui Wang, Xin Cheng, Yibin Xu, Zhiyi Wang, Liyan Ma, Caiming Li, Shize Jiang, Yuchen Li, Shuilong Guo, Wenbin Du

{"title":"Unveiling Neonatal Pneumonia Microbiome by High-Throughput Sequencing and Droplet Culturomics.","authors":"Zerui Wang, Xin Cheng, Yibin Xu, Zhiyi Wang, Liyan Ma, Caiming Li, Shize Jiang, Yuchen Li, Shuilong Guo, Wenbin Du","doi":"10.1093/gpbjnl/qzaf047","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf047","url":null,"abstract":"Neonatal pneumonia is a leading cause of infant mortality worldwide; however, a lack of microbial profiling, especially of low-abundance species, makes accurate diagnosis challenging. Traditional methods can fail to capture the complexity of the neonatal respiratory microbiota, thereby obscuring its role in disease progression. We describe a novel approach that combines high-throughput sequencing with droplet-based microfluidic cultivation to investigate microbiome shifts in neonates with pneumonia. Using 16S ribosomal RNA (rRNA) gene sequencing of 71 pneumonia cases and 49 controls, we identified 1009 genera, including 930 low-abundance taxa, which showed significant compositional differences between groups. Linear Discriminant Analysis Effect Size analysis identified key pneumonia-associated genera, such as Streptococcus, Rothia, and Corynebacterium. Droplet-based cultivation recovered 299 strains from 94 taxa, including rare species and ESKAPE pathogens, thereby supporting targeted antimicrobial management. Host-pathogen interaction assays showed that Rothia and Corynebacterium induced inflammation in lung epithelial cells, likely via dysregulation of the PI3K-Akt pathway. Integrating these marker taxa with clinical factors, such as gestational age and delivery type, offers the potential for precise diagnosis and treatment. The recovery of diverse species can support the construction of a biobank of neonatal respiratory microbiota to advance mechanistic studies and therapeutic strategies.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144176288","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

PSIA: A Comprehensive Knowledgebase of Plant Self-Incompatibility. PSIA：植物自交不亲和的综合知识库。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-21 DOI: 10.1093/gpbjnl/qzaf046

Chen Wang, Hong Zhao, Hongkui Zhang, Sijie Sun, Yongbiao Xue

{"title":"PSIA: A Comprehensive Knowledgebase of Plant Self-Incompatibility.","authors":"Chen Wang, Hong Zhao, Hongkui Zhang, Sijie Sun, Yongbiao Xue","doi":"10.1093/gpbjnl/qzaf046","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf046","url":null,"abstract":"Self-incompatibility (SI) is an important genetic mechanism in angiosperms that prevents inbreeding and promotes outcrossing, with significant implications for crop breeding, including genetic diversity, hybrid seed production, and yield optimization. In eudicots, SI is typically governed by a single S-locus containing tightly linked pistil and pollen S-determinant genes. Despite major advances in SI research, a centralized, comprehensive resource for SI-related genomic data remains lacking. To address this gap, we developed the Plant Self-Incompatibility Atlas (PSIA), a systematically curated knowledgebase providing an extensive compilation of plant SI, including genomic resources for SI species, S gene annotations, molecular mechanisms, phylogenetic relationships, and comparative genomic analyses. The current release of PSIA includes over 500 genome assemblies from 469 SI species. Using known S genes as queries, we manually identified and rigorously curated 3700 S genes. PSIA provides detailed S-locus information from assembled SI species and offers an interactive platform for browsing, BLAST searches, S gene analysis, and data retrieval. Additionally, PSIA serves as a unique platform for comparative genomic studies of S-loci, facilitating exploration of the dynamic processes underlying the origin, loss, and regain of SI. As a comprehensive and user-friendly resource, PSIA will greatly advance our understanding of angiosperm SI and serve as a valuable tool for crop breeding and hybrid seed production. PSIA is freely available at http://www.plantsi.cn.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113067","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

GDBIG: A Pioneering Birth Cohort Genomic Platform Facilitating Intergenerational Genetic Research. GDBIG：促进代际遗传研究的开创性出生队列基因组平台。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-15 DOI: 10.1093/gpbjnl/qzaf045

Shujia Huang, Chengrui Wang, Mingxi Huang, Jinhua Lu, Jian-Rong He, Shanshan Lin, Siyang Liu, Huimin Xia, Xiu Qiu

{"title":"GDBIG: A Pioneering Birth Cohort Genomic Platform Facilitating Intergenerational Genetic Research.","authors":"Shujia Huang, Chengrui Wang, Mingxi Huang, Jinhua Lu, Jian-Rong He, Shanshan Lin, Siyang Liu, Huimin Xia, Xiu Qiu","doi":"10.1093/gpbjnl/qzaf045","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf045","url":null,"abstract":"High-quality genome databases derived from large-scale, family-based birth cohorts are vital resources for investigating the genetic determinants of early-life traits and the impact of early-life environments on the health of both parents and offspring. Here, we established the genomic platform of the Born in Guangzhou Cohort Study (BIGCS), named the Genome Database of BIGCS (GDBIG), which represents the first birth cohort-based genomic database in China and is designed to facilitate intergenerational genetic research. Based on the phase I results of the BIGCS, GDBIG includes low-coverage (∼ 6.63×) whole genome sequencing (WGS) data and extensive pregnancy phenotypes from 4053 Chinese participants. These participants are from 30 of China's 34 administrative divisions, encompassing Han and 12 minority ethnic groups. Currently, GDBIG provides a range of services, including allele frequency queries for 56.23 million variants across two generations, a genotype imputation server featuring a high-quality family-based reference panel, and a genome-wide association study (GWAS) meta-analysis interface for various maternal and infant phenotypes. The GDBIG database addresses the lack of Asian birth cohort-based genomic resources and provides a valuable platform for conducting genetic analysis, accessible online or via application programming interfaces (API) at http://gdbig.bigcs.com.cn/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083031","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

CircAge: A Comprehensive Resource for Aging-associated Circular RNAs Across Species and Tissues. CircAge：跨物种和组织的衰老相关环状rna的综合资源。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-12 DOI: 10.1093/gpbjnl/qzaf044

Xin Dong, Zhen Zhou, Yanan Wang, Ayesha Nisar, Shaoyan Pu, Longbao Lv, Yijiang Li, Xuemei Lu, Yonghan He

{"title":"CircAge: A Comprehensive Resource for Aging-associated Circular RNAs Across Species and Tissues.","authors":"Xin Dong, Zhen Zhou, Yanan Wang, Ayesha Nisar, Shaoyan Pu, Longbao Lv, Yijiang Li, Xuemei Lu, Yonghan He","doi":"10.1093/gpbjnl/qzaf044","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf044","url":null,"abstract":"Circular RNAs (circRNAs) represent a novel class of RNA molecules characterized by a circular structure and enhanced stability. Emerging evidence indicates that circRNAs play pivotal regulatory roles in the aging process. Despite this, there is a lack of a systematic resource that integrates aging-associated circRNA data. Therefore, we developed a comprehensive database named CircAge, which encompasses 803 aging-related samples from 7 species and 24 tissue types. Through high-throughput sequencing, we also generated 47 new tissue samples from mice and rhesus monkeys. Integrating predictions from multiple bioinformatics tools, we identified over 529,856 unique circRNAs. Our data analysis revealed a general increase in circRNA expression levels with age, with approximately 23% of circRNAs demonstrating sequence conservation across species. The CircAge database systematically predicts potential interactions between circRNAs, microRNAs (miRNAs), and RNA-binding proteins (RBPs), and assesses the coding potential of circRNAs. This resource lays a foundation for elucidating the regulatory mechanisms of circRNAs in aging. As a comprehensive repository of aging-associated circRNAs, CircAge will significantly accelerate research in this field, facilitating the discovery of novel biomarkers and therapeutic targets for aging biology and developing diagnostic and therapeutic strategies for aging and age-related diseases. CircAge is publicly available at https://circage.kiz.ac.cn.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033265","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

PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies. PIGOME：猪功能基因组学研究的综合多组学数据库。

Genomics, proteomics & bioinformatics Pub Date : 2025-05-10 DOI: 10.1093/gpbjnl/qzaf016

Guohao Han 韩郭皓, Peng Yang 杨朋, Yongjin Zhang 张永进, Qiaowei Li 李巧伟, Xinhao Fan 范新浩, Ruipu Chen 陈锐朴, Chao Yan 闫超, Mu Zeng 曾木, Yalan Yang 杨亚岚, Zhonglin Tang 唐中林

{"title":"PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies.","authors":"Guohao Han 韩郭皓, Peng Yang 杨朋, Yongjin Zhang 张永进, Qiaowei Li 李巧伟, Xinhao Fan 范新浩, Ruipu Chen 陈锐朴, Chao Yan 闫超, Mu Zeng 曾木, Yalan Yang 杨亚岚, Zhonglin Tang 唐中林","doi":"10.1093/gpbjnl/qzaf016","DOIUrl":"10.1093/gpbjnl/qzaf016","url":null,"abstract":"In addition to being a major source of animal protein, pigs are an important model for studying development and diseases in humans. Over the past two decades, thousands of high-throughput sequencing studies in pigs have been performed using a variety of tissues from different breeds and developmental stages. However, multi-omics databases specifically designed for pig functional genomics research are still limited. Here, we present PIGOME, a user-friendly database of pig multi-omes. PIGOME currently contains seven types of pig omics datasets, including whole-genome sequencing (WGS), RNA sequencing (RNA-seq), microRNA sequencing (miRNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin sequencing (ATAC-seq), bisulfite sequencing (BS-seq), and methylated RNA immunoprecipitation sequencing (MeRIP-seq), from 6901 samples and 392 projects with manually curated metadata, integrated gene annotation, and quantitative trait locus information. Furthermore, various \"Explore\" and \"Browse\" functions have been established to provide user-friendly access to omics information. PIGOME implements several tools to visualize genomic variants, gene expression, and epigenetic signals of a given gene in the pig genome, enabling efficient exploration of spatiotemporal gene expression/epigenetic patterns, functions, regulatory mechanisms, and associated economic traits. Collectively, PIGOME provides valuable resources for pig breeding and is helpful for human biomedical research. PIGOME is available at https://pigome.com.","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/PMC12122082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560323","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

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

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