Genomics, proteomics & bioinformatics最新文献

{"title":"VISTA: A Tool for Fast Taxonomic Assignment of Viral Genome Sequences.","authors":"Tao Zhang 张韬, Yiyun Liu 刘依云, Xutong Guo 郭栩彤, Xinran Zhang 张欣然, Xinchang Zheng 郑欣畅, Mochen Zhang 张陌尘, Yiming Bao 鲍一明","doi":"10.1093/gpbjnl/qzae082","DOIUrl":"10.1093/gpbjnl/qzae082","url":null,"abstract":"<p><p>The rapid expansion of the number of viral genome sequences in public databases necessitates a scalable, universal, and automated preliminary taxonomic framework for comprehensive virus studies. Here, we introduce Virus Sequence-based Taxonomy Assignment (VISTA), a computational tool that employs a novel pairwise sequence comparison system and an automatic demarcation threshold identification framework for virus taxonomy. Leveraging physio-chemical property sequences, k-mer profiles, and machine learning techniques, VISTA constructs a robust distance-based framework for taxonomic assignment. Functionally similar to Pairwise Sequence Comparison (PASC), a widely used virus assignment tool based on pairwise sequence comparison, VISTA demonstrates superior performance by providing significantly improved separation for taxonomic groups, more objective taxonomic demarcation thresholds, greatly enhanced speed, and a wider application scope. We successfully applied VISTA to 38 virus families, as well as to the class Caudoviricetes. This demonstrates VISTA's scalability, robustness, and ability to automatically and accurately assign taxonomy to both prokaryotic and eukaryotic viruses. Furthermore, the application of VISTA to 679 unclassified prokaryotic virus genomes recovered from metagenomic data identified 46 novel virus families. VISTA is available as both a command line tool and a user-friendly web portal at https://ngdc.cncb.ac.cn/vista.</p>","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/PMC12212643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635082","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}

{"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":"<p><p>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.</p>","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/PMC12212637/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560404","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}

{"title":"Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.","authors":"Wei Zhang, Xianglin Zhang, Qiao Liu, Lei Wei, Xu Qiao, Rui Gao, Zhiping Liu, Xiaowo Wang","doi":"10.1093/gpbjnl/qzaf009","DOIUrl":"10.1093/gpbjnl/qzaf009","url":null,"abstract":"<p><p>In recent years, computational methods for quantifying cell-type proportions from transcription data have gained significant attention, particularly those reference-based methods which have demonstrated high accuracy. However, there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell-type deconvolution analysis. In this study, we introduce Deconvolution Evaluator (Deconer), a comprehensive toolkit for the evaluation of reference-based deconvolution methods. Deconer provides various simulated and real gene expression datasets, including both bulk and single-cell sequencing data, and offers multiple visualization interfaces. By utilizing Deconer, we conducted systematic comparisons of 16 reference-based deconvolution methods from different perspectives, including method robustness, accuracy in deconvolving rare components, signature gene selection performance, and external reference construction capability. We also performed an in-depth analysis of the application scenarios and challenges in cell-type deconvolution methods. Finally, we provided constructive suggestions for users to select and develop cell-type deconvolution algorithms. This study provides novel insights for researchers, assisting them in choosing appropriate toolkits, applying solutions in clinical contexts, and advancing the development of deconvolution tools tailored to gene expression data. The tutorials, manual, source code, and demo data of Deconer are publicly available at https://honchkrow.github.io/Deconer/ and https://ngdc.cncb.ac.cn/biocode/tool/7577.</p>","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/PMC12221868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442970","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}

{"title":"PhaSeDis: A Manually Curated Database of Phase Separation-disease Associations and Corresponding Small Molecules.","authors":"Taoyu Chen 陈韬宇, Guoguo Tang 唐果菓, Tianhao Li 李天昊, Zhining Yanghong 杨宏芷宁, Chao Hou 侯超, Zezhou Du 杜泽州, Kaiqiang You 游铠强, Liwei Ma 马利伟, Tingting Li 李婷婷","doi":"10.1093/gpbjnl/qzaf014","DOIUrl":"10.1093/gpbjnl/qzaf014","url":null,"abstract":"<p><p>Biomacromolecules form membraneless organelles through liquid-liquid phase separation in order to regulate the efficiency of particular biochemical reactions. Dysregulation of phase separation might result in pathological condensation or sequestration of biomolecules, leading to diseases. Thus, phase separation and phase separating factors may serve as drug targets for disease treatment. Nevertheless, such associations have not yet been integrated into phase separation-related databases. Therefore, based on MloDisDB, a database for membraneless organelle factor-disease associations previously developed by our lab, we constructed PhaSeDis, the phase separation-disease association database. We increased the number of phase separation entries from 52 to 185, and supplemented the evidence provided by the original articles verifying the phase separation nature of the factors. Moreover, we included the information of interacting small molecules with low-throughput or high-throughput evidence that might serve as potential drugs for phase separation entries. PhaSeDis strives to offer comprehensive descriptions of each entry, elucidating how phase separating factors induce pathological conditions via phase separation and the mechanisms by which small molecules intervene. We believe that PhaSeDis would be very important in the application of phase separation regulation in treating related diseases. PhaSeDis is available at http://mlodis.phasep.pro.</p>","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/PMC12208530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560320","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}

{"title":"The Updated Genome Warehouse: Enhancing Data Value, Security, and Usability to Address Data Expansion.","authors":"Yingke Ma, Xuetong Zhao, Yaokai Jia, Zhenxian Han, Caixia Yu, Zhuojing Fan, Zhang Zhang, Jingfa Xiao, Wenming Zhao, Yiming Bao, Meili Chen","doi":"10.1093/gpbjnl/qzaf010","DOIUrl":"10.1093/gpbjnl/qzaf010","url":null,"abstract":"<p><p>The Genome Warehouse (GWH), accessible at https://ngdc.cncb.ac.cn/gwh, is an extensively-utilized public repository dedicated to the deposition, management, and sharing of genome assembly sequences, annotations, and metadata. This paper highlights noteworthy enhancements to the GWH since the 2021 version, emphasizing substantial advancements in web interfaces for data submission, database functionality updates, and resource integration. Key updates include the reannotation of released prokaryotic genomes, mirroring of genome resources from National Center for Biotechnology Information (NCBI) GenBank and Reference Sequence Database (RefSeq), integration of Poxviridae sequences, implementation of an online batch submission system, enhancements to the quality control system, advanced search capabilities, and the introduction of a controlled-access mechanism for human genome data. These improvements collectively enhance the ease and security of data submission and access as well as genome data value, thereby improving convenience and utility for researchers in the genomics field.</p>","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/PMC12221864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470397","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}

{"title":"FarmGTEx TWAS-server: An Interactive Web Server for Customized TWAS Analysis.","authors":"Zhenyang Zhang, Zitao Chen, Jinyan Teng, Shuli Liu, Qing Lin, Jun Wu, Yahui Gao, Zhonghao Bai, Bingjie Li, George Liu, Zhe Zhang, Yuchun Pan, Zhe Zhang, Lingzhao Fang, Qishan Wang","doi":"10.1093/gpbjnl/qzaf006","DOIUrl":"10.1093/gpbjnl/qzaf006","url":null,"abstract":"<p><p>Transcriptome-wide association study (TWAS) is a powerful approach for investigating the molecular mechanisms linking genetic loci to complex phenotypes. However, the complexity of the TWAS analytical pipeline, including the construction of gene expression reference panels, gene expression prediction, and association analysis using data from genome-wide association studies (GWASs), poses challenges for genetic studies in many species. In this study, we provide the Farm Animal Genotype-Tissue Expression (FarmGTEx) TWAS-server, an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species. The server incorporates gene expression data from 49 human tissues (838 individuals), 34 pig tissues (5457 individuals), and 23 cattle tissues (4889 individuals), providing prediction models for 38,180 human genes, 21,037 pig genes, and 17,942 cattle genes. It supports genotype-based gene expression prediction, GWAS summary statistics imputation, customizable TWAS analysis, functional annotation, and result visualization. Additionally, we provide 479,203, 1208, and 657 tissue-gene-trait associations for 1129 human traits, 41 cattle traits, and 11 pig traits, respectively. Utilizing the TWAS-server, we validated the association of the ABCD4 gene with pig teat number. Furthermore, we identified that pig backfat thickness may share genetic similarities with human diastolic blood pressure, sarcoidosis (Löfgren syndrome), and body mass index. The FarmGTEx TWAS-server offers a comprehensive and accessible platform for researchers to perform TWAS analyses across tissues and species. It is freely available at https://twas.farmgtex.org, with regular updates planned as the FarmGTEx project expands to include more species.</p>","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/PMC12237508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401023","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}

{"title":"A Developmental Gene Expression Atlas Reveals Novel Biological Basis of Complex Phenotypes in Sheep.","authors":"Bingru Zhao, Hanpeng Luo, Xuefeng Fu, Guoming Zhang, Emily L Clark, Feng Wang, Brian Paul Dalrymple, V Hutton Oddy, Philip E Vercoe, Cuiling Wu, George E Liu, Cong-Jun Li, Ruidong Xiang, Kechuan Tian, Yanli Zhang, Lingzhao Fang","doi":"10.1093/gpbjnl/qzaf020","DOIUrl":"10.1093/gpbjnl/qzaf020","url":null,"abstract":"<p><p>Sheep (Ovis aries) represent one of the most important livestock species for global animal protein and wool production. However, little is known about the genetic and biological basis of ovine phenotypes, particularly those with high economic value and environmental impact. Here, by integrating 1413 RNA sequencing (RNA-seq) samples from 51 distinct tissues across 14 developmental time points, representing early-prenatal, late-prenatal, neonatal, lamb, juvenile, adult, and elderly stages, we constructed a high-resolution Developmental Gene Expression Atlas (dGEA) in sheep. We observed dynamic patterns of gene expression and regulatory networks across tissues and developmental stages. Leveraging this resource to interpret genetic associations for 48 monogenic and 12 complex traits in sheep, we found that genes upregulated at prenatal developmental stages played more important roles in shaping these phenotypes than those upregulated at postnatal stages. For instance, genetic associations of crimp number, mean staple length (MSL), and individual birthweight were significantly enriched in the prenatal rather than postnatal skin and immune tissues. By comprehensively integrating genome-wide association study (GWAS) fine-mapping results with the sheep dGEA, we identified several candidate genes for complex traits in sheep, such as SOX9 for MSL, GNRHR for litter size at birth, and PRKDC for live weight. These results provide novel insights into the developmental and molecular architecture of ovine phenotypes. The dGEA (https://sheepdgea.njau.edu.cn/) will serve as an invaluable resource for sheep developmental biology, genetics, genomics, and selective breeding.</p>","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/PMC12228968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560395","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}

{"title":"HumanTestisDB: A Comprehensive Atlas of Testicular Transcriptomes and Cellular Interactions.","authors":"Mengjie Wang, Laihua Li, Qing Cheng, Hao Zhang, Zhaode Liu, Yiqiang Cui, Jiahao Sha, Yan Yuan","doi":"10.1093/gpbjnl/qzaf015","DOIUrl":"10.1093/gpbjnl/qzaf015","url":null,"abstract":"<p><p>Advances in single-cell technology have enabled the detailed mapping of testicular cell transcriptomes, which is essential for understanding spermatogenesis. However, the fragmented nature of age-specific data from various literature sources has hindered comprehensive analysis. To overcome this, the Human Testis Database (HumanTestisDB) was developed, consolidating multiple human testicular sequencing datasets to address this limitation. Through extensive investigation, 38 unique cell types were identified, providing a detailed perspective on cellular variety. Furthermore, the database systematically categorizes samples into eight developmental stages, offering a structured framework to comprehend the temporal dynamics of testicular development. Each stage features comprehensive maps of cell-cell interactions, elucidating the complex communication network inside the testicular microenvironment at particular developmental stages. Moreover, by facilitating comparisons of interactions among various cell types at different stages, the database enables examining alterations that occur during critical transitions in spermatogenesis. HumanTestisDB, available at https://shalab.njmu.edu.cn/humantestisdb, offers vital insights into testicular transcriptomes and cellular interactions, serving as an essential resource for advancing research in reproductive biology.</p>","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/PMC12221866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569060","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}

{"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":"<p><p>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/.</p>","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}

{"title":"Identification of Small Open Reading Frame-encoded Proteins in the Human Genome.","authors":"Hitesh Kore, Satomi Okano, Keshava K Datta, Jackson Thorp, Parthiban Periasamy, Mayur Divate, Upekha Liyanage, Gunter Hartel, Shivashankar H Nagaraj, Harsha Gowda","doi":"10.1093/gpbjnl/qzaf004","DOIUrl":"10.1093/gpbjnl/qzaf004","url":null,"abstract":"<p><p>One of the main goals of the Human Genome Project is to identify all protein-coding genes. There are ∼ 20,500 protein-coding genes annotated in the human reference databases. However, in the last few years, proteogenomics studies have predicted thousands of novel protein-coding regions, including low-molecular-weight proteins encoded by small open reading frames (sORFs) in untranslated regions of messenger RNAs and non-coding RNAs. Most of these predictions are based on bioinformatics analyses and ribosome footprint data. The validity of some of these sORF-encoded proteins (SEPs) has been established through functional characterization. With the growing number of predicted novel proteins, a strategy to identify reliable candidates that warrant further studies is needed. In this study, we developed an integrated proteogenomics workflow to identify a reliable set of novel protein-coding regions in the human genome based on their recurrent observations across multiple samples. Publicly available ribosome profiling and global proteomic datasets were used to establish protein-coding evidence. We predicted protein translation from 4008 sORFs based on recurrent ribosome occupancy signals across samples. In addition, we identified 825 SEPs based on proteomic data. Some of the novel protein-coding regions identified were located in genome-wide association study (GWAS) loci associated with various traits and disease phenotypes. Peptides from SEPs are also presented by major histocompatibility complex class I (MHC-I), similar to canonical proteins. Novel protein-coding regions reported in this study expand the current catalog of protein-coding genes and warrant experimental studies to elucidate their cellular functions and potential roles in human diseases.</p>","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/PMC12236067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371456","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}