Genomics, proteomics & bioinformatics最新文献_第8页

Global Invasion History and Genomic Signatures of Adaptation of the Highly Invasive Sycamore Lace Bug. 高入侵性梧桐花边蝽的全球入侵历史和适应性基因组特征

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae074

Zhenyong Du, Xuan Wang, Yuange Duan, Shanlin Liu, Li Tian, Fan Song, Wanzhi Cai, Hu Li

{"title":"Global Invasion History and Genomic Signatures of Adaptation of the Highly Invasive Sycamore Lace Bug.","authors":"Zhenyong Du, Xuan Wang, Yuange Duan, Shanlin Liu, Li Tian, Fan Song, Wanzhi Cai, Hu Li","doi":"10.1093/gpbjnl/qzae074","DOIUrl":"10.1093/gpbjnl/qzae074","url":null,"abstract":"Invasive species cause massive economic and ecological damages. Climate change has resulted in an unprecedented increase in the number and impact of invasive species; however, the mechanisms underlying these invasions are unclear. The sycamore lace bug, Corythucha ciliata, is a highly invasive species originating from North America and has expanded across the Northern Hemisphere since the 1960s. In this study, we assembled the C. ciliata genome using high-coverage Pacific Biosciences (PacBio), Illumina, and high-throughput chromosome conformation capture (Hi-C) sequencing. A total of 15,278 protein-coding genes were identified, and expansions of gene families with oxidoreductase and metabolic activities were observed. In-depth resequencing of 402 samples from native and nine invaded countries across three continents revealed 2.74 million single nucleotide polymorphisms. Two major invasion routes of C. ciliata were identified from North America to Europe and Japan, with a contact zone forming in East Asia. Genomic signatures of selection associated with invasion and long-term balancing selection in native ranges were identified. These genomic signatures overlapped with each other as well as with expanded genes, suggesting improvements in the oxidative stress and thermal tolerance of C. ciliata. These findings offer valuable insights into the genomic architecture and adaptive evolution underlying the invasive capabilities of species during rapid environmental changes.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11993305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484014","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

Enzyme Repertoires and Genomic Insights into Lycium barbarum Pectin Polysaccharide Biosynthesis. 枸杞果胶多糖生物合成的酵素再现和基因组洞察。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae079

Haiyan Yue, Yiheng Tang, Aixuan Li, Lili Zhang, Yiwei Niu, Yiming Zhang, Hao Wang, Jianjun Luo, Yi Zhao, Shunmin He, Chang Chen, Runsheng Chen

{"title":"Enzyme Repertoires and Genomic Insights into Lycium barbarum Pectin Polysaccharide Biosynthesis.","authors":"Haiyan Yue, Yiheng Tang, Aixuan Li, Lili Zhang, Yiwei Niu, Yiming Zhang, Hao Wang, Jianjun Luo, Yi Zhao, Shunmin He, Chang Chen, Runsheng Chen","doi":"10.1093/gpbjnl/qzae079","DOIUrl":"10.1093/gpbjnl/qzae079","url":null,"abstract":"Lycium barbarum, a member of the Solanaceae family, is an important eudicot with applications in both food and medicine. L. barbarum pectin polysaccharides (LBPPs) are key bioactive compounds of L. barbarum, notable for being among the few polysaccharides with both biocompatibility and biomedical activity. Although studies have analyzed the functional properties of LBPPs, the mechanisms underlying their biosynthesis and transport by key enzymes remain poorly understood. In this study, we assembled a 2.18-Gb reference genome of L. barbarum, reconstructed the first complete biosynthesis pathway of LBPPs, and elucidated the sugar transport system. We also characterized the important genes responsible for backbone extension, sidechain synthesis, and modification of LBPPs. Furthermore, we characterized the long non-coding RNAs (lncRNAs) associated with polysaccharide metabolism. We identified a specific rhamnogalacturonan I (RG-I) rhamnosyltransferase, RRT3020, which enhances RG-I biosynthesis within LBPPs. These newly identified enzymes and pivotal genes endow L. barbarum with unique pectin biosynthesis capabilities, distinguishing it from other Solanaceae species. Our findings thus provide a foundation for evolutionary studies and molecular breeding to expand the diverse applications of L. barbarum.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570796","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

Genome Assembly and Winged Fruit Gene Regulation of Chinese Wingnut: Insights from Genomic and Transcriptomic Analyses. 中国翅果基因组组装和翅果基因调控：来自基因组和转录组学分析的见解。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae087

Fangdong Geng, Xuedong Zhang, Jiayu Ma, Hengzhao Liu, Hang Ye, Fan Hao, Miaoqing Liu, Meng Dang, Huijuan Zhou, Mengdi Li, Peng Zhao

{"title":"Genome Assembly and Winged Fruit Gene Regulation of Chinese Wingnut: Insights from Genomic and Transcriptomic Analyses.","authors":"Fangdong Geng, Xuedong Zhang, Jiayu Ma, Hengzhao Liu, Hang Ye, Fan Hao, Miaoqing Liu, Meng Dang, Huijuan Zhou, Mengdi Li, Peng Zhao","doi":"10.1093/gpbjnl/qzae087","DOIUrl":"10.1093/gpbjnl/qzae087","url":null,"abstract":"The genomic basis and biology of winged fruit are interesting issues in ecological and evolutionary biology. Chinese wingnut (Pterocarya stenoptera) is an important horticultural and economic tree species in China. The genomic resources of this hardwood tree could advance the genomic studies of Juglandaceae species and elucidate their evolutionary relationships. Here, we reported a high-quality reference genome of P. stenoptera (N50 = 35.15 Mb) and performed a comparative genomic analysis across Juglandaceae species. Paralogous relationships among the 16 chromosomes of P. stenoptera revealed eight main duplications representing the subgenomes. Molecular dating suggested that the most recent common ancestor of P. stenoptera and Cyclocarya paliurus diverged from Juglans species around 56.7 million years ago (MYA). The expanded and contracted gene families were associated with cutin, suberine, and wax biosynthesis, cytochrome P450, and anthocyanin biosynthesis. We identified large inversion blocks between P. stenoptera and its relatives, which were enriched with genes involved in lipid biosynthesis and metabolism, as well as starch and sucrose metabolism. Whole-genome resequencing of 28 individuals revealed clearly phylogenetic clustering into three groups corresponding to Pterocarya macroptera, Pterocarya hupehensis, and P. stenoptera. Morphological and transcriptomic analyses showed that CAD, COMT, LOX, and MADS-box play important roles during the five developmental stages of wingnuts. This study highlights the evolutionary history of the P. stenoptera genome and supports P. stenoptera as an appropriate Juglandaceae model for studying winged fruits. Our findings provide a theoretical basis for understanding the evolution, development, and diversity of winged fruits in woody plants.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820285","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

COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information. COCOA：利用表观基因组信息绘制细胞类型特异性染色质区室精细图谱的框架。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae091

Kai Li, Ping Zhang, Jinsheng Xu, Zi Wen, Junying Zhang, Zhike Zi, Li Li

{"title":"COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.","authors":"Kai Li, Ping Zhang, Jinsheng Xu, Zi Wen, Junying Zhang, Zhike Zi, Li Li","doi":"10.1093/gpbjnl/qzae091","DOIUrl":"10.1093/gpbjnl/qzae091","url":null,"abstract":"Chromatin compartmentalization and epigenomic modifications play crucial roles in cell differentiation and disease development. However, precise mapping of chromatin compartment patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartment patterns remains challenging. To address these issues, we present COCOA, a deep neural network framework using convolution and attention mechanisms to infer fine-scale chromatin compartment patterns from six histone modification signals. COCOA extracts 1D track features through bidirectional feature reconstruction after resolution-specific binning of epigenomic signals. These track features are then cross-fused with contact features using an attention mechanism and transformed into chromatin compartment patterns through residual feature reduction. COCOA demonstrates accurate inference of chromatin compartmentalization at a fine-scale resolution and exhibits stable performance on test sets. Additionally, we explored the impact of histone modifications on chromatin compartmentalization prediction through in silico epigenomic perturbation experiments. Unlike obscure compartments observed in high-depth experimental data at 1-kb resolution, COCOA generates clear and detailed compartment patterns, highlighting its superior performance. Finally, we demonstrate that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining insights into chromatin compartmentalization from epigenomics in diverse biological scenarios. The COCOA Python code is publicly available at https://github.com/onlybugs/COCOA and https://ngdc.cncb.ac.cn/biocode/tools/BT007498.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11993304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901425","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

Harnessing Type II Cytokines to Reinvigorate Exhausted T Cells for Durable Cancer Immunotherapy. 利用II型细胞因子重新激活耗尽的T细胞用于持久的癌症免疫治疗。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae093

Wenle Zhang, Yanwen Wang, Bin Li

引用次数: 0

Pangenome Reveals Gene Content Variations and Structural Variants Contributing to Pig Characteristics. 猪基因组揭示了导致猪特征的基因内容变异和结构变异。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae081

Heng Du, Yue Zhuo, Shiyu Lu, Wanying Li, Lei Zhou, Feizhou Sun, Gang Liu, Jian-Feng Liu

{"title":"Pangenome Reveals Gene Content Variations and Structural Variants Contributing to Pig Characteristics.","authors":"Heng Du, Yue Zhuo, Shiyu Lu, Wanying Li, Lei Zhou, Feizhou Sun, Gang Liu, Jian-Feng Liu","doi":"10.1093/gpbjnl/qzae081","DOIUrl":"10.1093/gpbjnl/qzae081","url":null,"abstract":"Pigs are one of the most essential sources of high-quality proteins in human diets. Structural variants (SVs) are a major source of genetic variants associated with diverse traits and evolutionary events. However, the current linear reference genome of pigs restricts the accurate presentation of position information for SVs. In this study, we generated a pangenome of pigs and a genome variation map of 599 deeply sequenced genomes across Eurasia. Additionally, we established a section-wide gene repertoire, revealing that core genes are more evolutionarily conserved than variable genes. Furthermore, we identified 546,137 SVs, their enrichment regions, and relationships with genomic features and found significant divergence across Eurasian pigs. More importantly, the pangenome-detected SVs could complement heritability estimates and genome-wide association studies based only on single nucleotide polymorphisms. Among the SVs shaped by selection, we identified an insertion in the promoter region of the TBX19 gene, which may be related to the development, growth, and timidity traits of Asian pigs and may affect the gene expression. The constructed pig pangenome and the identified SVs in this study provide rich resources for future functional genomic research on pigs.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635075","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

Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data. 基于scRNA-Seq数据的T细胞受体构建方法评价

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae086

Ruonan Tian, Zhejian Yu, Ziwei Xue, Jiaxin Wu, Lize Wu, Shuo Cai, Bing Gao, Bing He, Yu Zhao, Jianhua Yao, Linrong Lu, Wanlu Liu

{"title":"Evaluation of T Cell Receptor Construction Methods from scRNA-Seq Data.","authors":"Ruonan Tian, Zhejian Yu, Ziwei Xue, Jiaxin Wu, Lize Wu, Shuo Cai, Bing Gao, Bing He, Yu Zhao, Jianhua Yao, Linrong Lu, Wanlu Liu","doi":"10.1093/gpbjnl/qzae086","DOIUrl":"10.1093/gpbjnl/qzae086","url":null,"abstract":"T cell receptors (TCRs) serve key roles in the adaptive immune system by enabling recognition and response to pathogens and irregular cells. Various methods have been developed for TCR construction from single-cell RNA sequencing (scRNA-seq) datasets, each with its unique characteristics. Yet, a comprehensive evaluation of their relative performance under different conditions remains elusive. In this study, we conducted a benchmark analysis utilizing experimental single-cell immune profiling datasets. Additionally, we introduced a novel simulator, YASIM-scTCR (Yet Another SIMulator for single-cell TCR), capable of generating scTCR-seq reads containing diverse TCR-derived sequences with different sequencing depths and read lengths. Our results consistently showed that TRUST4 and MiXCR outperformed others across multiple datasets, while DeRR demonstrated considerable accuracy. We also discovered that the sequencing depth inherently imposes a critical constraint on successful TCR construction from scRNA-seq data. In summary, we present a benchmark study to aid researchers in choosing the appropriate method for reconstructing TCRs from scRNA-seq data.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820279","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

SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations. 筛选：一个基于图的对比学习工具，以推断催化残基和评估酶突变。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae094

Tong Pan, Yue Bi, Xiaoyu Wang, Ying Zhang, Geoffrey I Webb, Robin B Gasser, Lukasz Kurgan, Jiangning Song

{"title":"SCREEN: A Graph-based Contrastive Learning Tool to Infer Catalytic Residues and Assess Enzyme Mutations.","authors":"Tong Pan, Yue Bi, Xiaoyu Wang, Ying Zhang, Geoffrey I Webb, Robin B Gasser, Lukasz Kurgan, Jiangning Song","doi":"10.1093/gpbjnl/qzae094","DOIUrl":"10.1093/gpbjnl/qzae094","url":null,"abstract":"The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (1) consistently outperforms currently-available predictors; (2) provides accurate results when applied to inferred enzyme structures; and (3) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental datasets, we show that SCREEN's predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations. SCREEN is publicly available at https://github.com/BioColLab/SCREEN and https://ngdc.cncb.ac.cn/biocode/tool/7580.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901428","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

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

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae083

Ziyi Li, Cory A Weller, Syed Shah, Nicholas L Johnson, Ying Hao, Paige B Jarreau, Jessica Roberts, Deyaan Guha, Colleen Bereda, Sydney Klaisner, Pedro Machado, Matteo Zanovello, Mercedes Prudencio, Björn Oskarsson, Nathan P Staff, Dennis W Dickson, Pietro Fratta, Leonard Petrucelli, Priyanka Narayan, Mark R Cookson, Michael E Ward, Andrew B Singleton, Mike A Nalls, Yue A Qi

{"title":"ProtPipe: A Multifunctional Data Analysis Pipeline for Proteomics and Peptidomics.","authors":"Ziyi Li, Cory A Weller, Syed Shah, Nicholas L Johnson, Ying Hao, Paige B Jarreau, Jessica Roberts, Deyaan Guha, Colleen Bereda, Sydney Klaisner, Pedro Machado, Matteo Zanovello, Mercedes Prudencio, Björn Oskarsson, Nathan P Staff, Dennis W Dickson, Pietro Fratta, Leonard Petrucelli, Priyanka Narayan, Mark R Cookson, Michael E Ward, Andrew B Singleton, Mike A Nalls, Yue A Qi","doi":"10.1093/gpbjnl/qzae083","DOIUrl":"10.1093/gpbjnl/qzae083","url":null,"abstract":"Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein-protein interaction analysis, and major histocompatibility complex (MHC)-peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11842048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690171","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

HemaCisDB: An Interactive Database for Analyzing Cis-Regulatory Elements Across Hematopoietic Malignancies. HemaCisDB：分析各种造血恶性肿瘤顺式调节元件的交互式数据库。

Genomics, proteomics & bioinformatics Pub Date : 2024-12-26 DOI: 10.1093/gpbjnl/qzae088

Xinping Cai, Qianru Zhang, Bolin Liu, Lu Sun, Yuxuan Liu

{"title":"HemaCisDB: An Interactive Database for Analyzing Cis-Regulatory Elements Across Hematopoietic Malignancies.","authors":"Xinping Cai, Qianru Zhang, Bolin Liu, Lu Sun, Yuxuan Liu","doi":"10.1093/gpbjnl/qzae088","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae088","url":null,"abstract":"Noncoding cis-regulatory elements (CREs), such as transcriptional enhancers, are key regulators of gene expression programs. Accessible chromatin and H3K27ac are well-recognized markers for CREs associated with their biological function. Deregulation of CREs is commonly found in hematopoietic malignancies yet the extent to which CRE dysfunction contributes to pathophysiology remains incompletely understood. Here, we developed HemaCisDB, an interactive, comprehensive, and centralized online resource for CRE characterization across hematopoietic malignancies, serving as a useful resource for investigating the pathological roles of CREs in blood disorders. Currently, we collected 922 ATAC-seq, 190 DNase-seq, and 531 H3K27ac ChIP-seq datasets from patient samples and cell lines across different myeloid and lymphoid neoplasms. HemaCisDB provides comprehensive quality control metrics to assess ATAC-seq, DNase-seq, and H3K27ac ChIP-seq data quality. The analytic modules in HemaCisDB include transcription factor (TF) footprinting inference, super-enhancer identification, and core transcriptional regulatory circuitry analysis. Moreover, HemaCisDB also enables the study of TF binding dynamics by comparing TF footprints across different disease types or conditions via web-based interactive analysis. Together, HemaCisDB provides an interactive platform for CRE characterization to facilitate mechanistic studies of transcriptional regulation in hematopoietic malignancies. HemaCisDB is available at https://hemacisdb.chinablood.com.cn/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901427","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