pubCounteR: an R package for interrogating published literature for experimentally-derived gene lists within a user-defined biological context.

IF 3.9 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in bioinformatics Pub Date : 2025-05-06 eCollection Date: 2025-01-01 DOI:10.3389/fbinf.2025.1523184
Marina Leer, George A Soultoukis, Markus Jähnert, Masoome Oveisi, Dirk Walther, Tim J Schulz
{"title":"pubCounteR: an R package for interrogating published literature for experimentally-derived gene lists within a user-defined biological context.","authors":"Marina Leer, George A Soultoukis, Markus Jähnert, Masoome Oveisi, Dirk Walther, Tim J Schulz","doi":"10.3389/fbinf.2025.1523184","DOIUrl":null,"url":null,"abstract":"<p><p>Basic and clinical biomedical research relies heavily on modern large-scale datasets that include genomics, transcriptomics, epigenomics, metabolomics, and proteomics, among other \"Omics\". These research tools very often generate lists of candidate genes that are hypothesized or shown to be responsible for the biological effect in question. To aid the biological interpretation of experimentally-obtained gene lists, we developed pubCounteR, an R-package and web-based interface that screens publications by a user-defined set of keywords representing a specific biological context for experimentally-derived gene lists.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1523184"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12118352/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2025.1523184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Basic and clinical biomedical research relies heavily on modern large-scale datasets that include genomics, transcriptomics, epigenomics, metabolomics, and proteomics, among other "Omics". These research tools very often generate lists of candidate genes that are hypothesized or shown to be responsible for the biological effect in question. To aid the biological interpretation of experimentally-obtained gene lists, we developed pubCounteR, an R-package and web-based interface that screens publications by a user-defined set of keywords representing a specific biological context for experimentally-derived gene lists.

pubCounteR:一个R包,用于在用户定义的生物学环境中查询已发表的实验衍生基因列表的文献。
基础和临床生物医学研究严重依赖于现代大规模数据集,包括基因组学、转录组学、表观基因组学、代谢组学和蛋白质组学等“组学”。这些研究工具经常生成候选基因列表,这些候选基因是假设的或被证明对所讨论的生物效应负责。为了帮助对实验获得的基因列表进行生物学解释,我们开发了pubCounteR,这是一个r包和基于web的界面,通过用户定义的一组关键字来筛选实验获得的基因列表的特定生物学背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信