Genomics, proteomics & bioinformatics最新文献

HemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.

Genomics, proteomics & bioinformatics Pub Date : 2025-01-25 DOI: 10.1093/gpbjnl/qzaf002

Zhenyi Wang, Yuxin Miao, Hongjun Li, Wenyan Cheng, Minglei Shi, Lv Gang, Yating Zhu, Junyi Zhang, Tingting Tan, Jin Gu, Michael Q Zhang, Jianfeng Li, Hai Fang, Zhu Chen, Saijuan Chen

{"title":"HemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.","authors":"Zhenyi Wang, Yuxin Miao, Hongjun Li, Wenyan Cheng, Minglei Shi, Lv Gang, Yating Zhu, Junyi Zhang, Tingting Tan, Jin Gu, Michael Q Zhang, Jianfeng Li, Hai Fang, Zhu Chen, Saijuan Chen","doi":"10.1093/gpbjnl/qzaf002","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf002","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043874","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 Tumor Antigens from Multi-omics Data: Computational Approaches and Resources. 基于多组学数据的肿瘤抗原表征：计算方法和资源。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-20 DOI: 10.1093/gpbjnl/qzaf001

Yunzhe Wang, James Wengler, Yuzhu Fang, Joseph Zhou, Hang Ruan, Zhao Zhang, Leng Han

{"title":"Characterization of Tumor Antigens from Multi-omics Data: Computational Approaches and Resources.","authors":"Yunzhe Wang, James Wengler, Yuzhu Fang, Joseph Zhou, Hang Ruan, Zhao Zhang, Leng Han","doi":"10.1093/gpbjnl/qzaf001","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzaf001","url":null,"abstract":"Tumor-specific antigens, also known as neoantigens, have potential utility in anti-cancer immunotherapy, including immune checkpoint blockade (ICB), neoantigen-specific T cell receptor-engineered T (TCR-T), chimeric antigen receptor T (CAR-T), and therapeutic cancer vaccines (TCVs). After recognizing presented neoantigens, the immune system becomes activated and triggers the death of tumor cells. Neoantigens may be derived from multiple origins, including somatic mutations (single nucleotide variants, insertion/deletions, and gene fusions), circular RNAs, alternative splicing, RNA editing, and polymorphic microbiome. An increasing amount of bioinformatics tools and algorithms are being developed to predict tumor neoantigens derived from different sources, which may require inputs from different multi-omics data. In addition, calculating the peptide-major histocompatibility complex (MHC) affinity can aid in selecting putative neoantigens, as high binding affinities facilitate antigen presentation. Based on these approaches and previous experiments, many resources were developed to reveal the landscape of tumor neoantigens across multiple cancer types. Herein, we summarized these tools, algorithms, and resources to provide an overview of computational analysis for neoantigen discovery and prioritization, as well as the future development of potential clinical utilities in this field.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018219","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

SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research. SoyOD：用于挖掘基因和生物研究的大豆多组学综合数据库。

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae080

Jie Li, Qingyang Ni, Guangqi He, Jiale Huang, Haoyu Chao, Sida Li, Ming Chen, Guoyu Hu, James Whelan, Huixia Shou

{"title":"SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research.","authors":"Jie Li, Qingyang Ni, Guangqi He, Jiale Huang, Haoyu Chao, Sida Li, Ming Chen, Guoyu Hu, James Whelan, Huixia Shou","doi":"10.1093/gpbjnl/qzae080","DOIUrl":"10.1093/gpbjnl/qzae080","url":null,"abstract":"Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics studies have been carried out, generating datasets ranging from genotype to phenotype. In order to efficiently utilize these data for basic and applied research, a soybean multi-omics database with extensive data coverage and comprehensive data analysis tools was established. The Soybean Omics Database (SoyOD) integrates important new datasets with existing public datasets to form the most comprehensive collection of soybean multi-omics information. Compared to existing soybean databases, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptomic datasets from seeds at different developmental stages, 53 phenotypic datasets, and more than 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. Moreover, SoyOD can be used to mine candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod.","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/PMC11757165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635076","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

Decoding Spatial Complexity of Diverse RNA Species in Archival Tissues. 解码档案组织中多种 RNA 的空间复杂性

Genomics, proteomics & bioinformatics Pub Date : 2025-01-15 DOI: 10.1093/gpbjnl/qzae089

Junjie Zhu, Fangqing Zhao

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

Single Cell Sequencing Traces Mitochondrial Transfers. 单细胞测序追踪线粒体转移。

Genomics, proteomics & bioinformatics Pub Date : 2024-12-26 DOI: 10.1093/gpbjnl/qzae092

Mengying Wu, Weilin Pu, Zhenglong Gu

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

A Novel Targeted Long-read Sequencing Approach Boosts Transcriptomic Profiling. 一种新的靶向长读测序方法促进转录组学分析。

Genomics, proteomics & bioinformatics Pub Date : 2024-12-26 DOI: 10.1093/gpbjnl/qzae090

Xiaolong Tian, Rong Fan

引用次数: 0

COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartments with 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 : 2024-12-26 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 Cell-type-specific Chromatin Compartments with Epigenomic Information.","authors":"Kai Li, Ping Zhang, Jinsheng Xu, Zi Wen, Junying Zhang, Zhike Zi, Li Li","doi":"10.1093/gpbjnl/qzae091","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzae091","url":null,"abstract":"Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental 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 1-D track features through bi-directional feature reconstruction after resolution-specific binning 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 with 1 kb resolution high-depth experimental data, COCOA generates clear and detailed compartmental patterns, highlighting its superior performance. Finally, we demonstrated that COCOA enables cell-type-specific prediction of unrevealed chromatin compartment patterns in various biological processes, making it an effective tool for gaining chromatin compartmentalization insights from epigenomics in diverse biological scenarios. The COCOA python code is publicly available at https://github.com/onlybugs/COCOA.","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":"142901425","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

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 : 2024-12-26 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":"https://doi.org/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 (i) consistently outperforms currently-available predictors; (ii) provides accurate.Results: when applied to inferred enzyme structures; and (iii) 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 data sets, 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":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142901428","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