Genomics, proteomics & bioinformatics最新文献_第4页

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction. 蛋白质磷酸化位点预测的机器学习和算法方法综述。

Genomics, proteomics & bioinformatics Pub Date : 2023-12-01 Epub Date: 2023-10-19 DOI: 10.1016/j.gpb.2023.03.007

Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Seyedehsamaneh Shojaeilangari, Elham Yavari

{"title":"A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.","authors":"Farzaneh Esmaili, Mahdi Pourmirzaei, Shahin Ramazi, Seyedehsamaneh Shojaeilangari, Elham Yavari","doi":"10.1016/j.gpb.2023.03.007","DOIUrl":"10.1016/j.gpb.2023.03.007","url":null,"abstract":"Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11082408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686555","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

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes. HPC图谱：计算构建人类蛋白质复合物的综合图谱。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-09-18 DOI: 10.1016/j.gpb.2023.05.001

Yuliang Pan, Ruiyi Li, Wengen Li, Liuzhenghao Lv, Jihong Guan, Shuigeng Zhou

{"title":"HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes.","authors":"Yuliang Pan, Ruiyi Li, Wengen Li, Liuzhenghao Lv, Jihong Guan, Shuigeng Zhou","doi":"10.1016/j.gpb.2023.05.001","DOIUrl":"10.1016/j.gpb.2023.05.001","url":null,"abstract":"A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41124619","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

OBIA: An Open Biomedical Imaging Archive. OBIA：一个开放的生物医学成像档案。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-10-06 DOI: 10.1016/j.gpb.2023.09.003

Enhui Jin, Dongli Zhao, Gangao Wu, Junwei Zhu, Zhonghuang Wang, Zhiyao Wei, Sisi Zhang, Anke Wang, Bixia Tang, Xu Chen, Yanling Sun, Zhe Zhang, Wenming Zhao, Yuanguang Meng

{"title":"OBIA: An Open Biomedical Imaging Archive.","authors":"Enhui Jin, Dongli Zhao, Gangao Wu, Junwei Zhu, Zhonghuang Wang, Zhiyao Wei, Sisi Zhang, Anke Wang, Bixia Tang, Xu Chen, Yanling Sun, Zhe Zhang, Wenming Zhao, Yuanguang Meng","doi":"10.1016/j.gpb.2023.09.003","DOIUrl":"10.1016/j.gpb.2023.09.003","url":null,"abstract":"With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147344","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

From BIG Data Center to China National Center for Bioinformation. 从BIG数据中心到中国生物信息中心。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-10-12 DOI: 10.1016/j.gpb.2023.10.001

Yiming Bao, Yongbiao Xue

引用次数: 0

Toward A New Paradigm of Genomics Research 迈向基因组学研究的新范式

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 DOI: 10.1016/j.gpb.2023.10.005

Zhang Zhang, Songnian Hu, Jun Yu

引用次数: 0

Decoding Human Biology and Disease Using Single-cell Omics Technologies. 使用单细胞奥密克戎技术解码人类生物学和疾病。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-09-20 DOI: 10.1016/j.gpb.2023.06.003

Qiang Shi, Xueyan Chen, Zemin Zhang

引用次数: 0

RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning. RCoV19：严重急性呼吸系统综合征冠状病毒2型基因组数据整合、变异监测和风险预警的一站式中心。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-10-26 DOI: 10.1016/j.gpb.2023.10.004

Cuiping Li, Lina Ma, Dong Zou, Rongqin Zhang, Xue Bai, Lun Li, Gangao Wu, Tianhao Huang, Wei Zhao, Enhui Jin, Yiming Bao, Shuhui Song

{"title":"RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-warning.","authors":"Cuiping Li, Lina Ma, Dong Zou, Rongqin Zhang, Xue Bai, Lun Li, Gangao Wu, Tianhao Huang, Wei Zhao, Enhui Jin, Yiming Bao, Shuhui Song","doi":"10.1016/j.gpb.2023.10.004","DOIUrl":"10.1016/j.gpb.2023.10.004","url":null,"abstract":"The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66784787","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

Non-small Cell Lung Cancer Epigenomes Exhibit Altered DNA Methylation in Smokers and Never-smokers. 非小细胞肺癌癌症表观基因组显示吸烟者和从不吸烟者的DNA甲基化改变。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-09-22 DOI: 10.1016/j.gpb.2023.03.006

Jennifer A Karlow, Erica C Pehrsson, Xiaoyun Xing, Mark Watson, Siddhartha Devarakonda, Ramaswamy Govindan, Ting Wang

{"title":"Non-small Cell Lung Cancer Epigenomes Exhibit Altered DNA Methylation in Smokers and Never-smokers.","authors":"Jennifer A Karlow, Erica C Pehrsson, Xiaoyun Xing, Mark Watson, Siddhartha Devarakonda, Ramaswamy Govindan, Ting Wang","doi":"10.1016/j.gpb.2023.03.006","DOIUrl":"10.1016/j.gpb.2023.03.006","url":null,"abstract":"Epigenetic alterations are widespread in cancer and can complement genetic alterations to influence cancer progression and treatment outcome. To determine the potential contribution of DNAmethylation alterations to tumor phenotype in non-small cell lung cancer (NSCLC) in both smoker and never-smoker patients, we performed genome-wide profiling of DNA methylation in 17 primary NSCLC tumors and 10 matched normal lung samples using the complementary assays, methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylation sensitive restriction enzyme sequencing (MRE-seq). We reported recurrent methylation changes in the promoters of several genes, many previously implicated in cancer, including FAM83A and SEPT9 (hypomethylation), as well as PCDH7, NKX2-1, and SOX17 (hypermethylation). Although many methylation changes between tumors and their paired normal samples were shared across patients, several were specific to a particular smoking status. For example, never-smokers displayed a greater proportion of hypomethylated differentially methylated regions (hypoDMRs) and a greater number of recurrently hypomethylated promoters, including those of ASPSCR1, TOP2A, DPP9, and USP39, all previously linked to cancer. Changes outside of promoters were also widespread and often recurrent, particularly methylation loss over repetitive elements, highly enriched for ERV1 subfamilies. Recurrent hypoDMRs were enriched for several transcription factor binding motifs, often for genes involved in signaling and cell proliferation. For example, 71% of recurrent promoter hypoDMRs contained a motif for NKX2-1. Finally, the majority of DMRs were located within an active chromatin state in tissues profiled using the Roadmap Epigenomics data, suggesting that methylation changes may contribute to altered regulatory programs through the adaptation of cell type-specific expression programs.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10928376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41147317","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

Toward Inclusiveness and Thoroughness: A Paradigm Shift from More-ever-omics to Holovivology. 走向包容性和全面性：从越来越多的经济学到整体学的范式转变。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-10-24 DOI: 10.1016/j.gpb.2023.10.003

Jun Yu

引用次数: 0

A Historic Retrospective on the Early Bioinformatics Research in China. 中国早期生物信息学研究的历史回顾。

Genomics, proteomics & bioinformatics Pub Date : 2023-10-01 Epub Date: 2023-11-03 DOI: 10.1016/j.gpb.2023.10.006

Runsheng Chen

引用次数: 0