How to use open-pFind in deep proteomics data analysis?- A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data.

Guangcan Shao, Yong Cao, Zhenlin Chen, Chao Liu, Shangtong Li, Hao Chi, Meng-Qiu Dong
{"title":"How to use open-pFind in deep proteomics data analysis?- A protocol for rigorous identification and quantitation of peptides and proteins from mass spectrometry data.","authors":"Guangcan Shao,&nbsp;Yong Cao,&nbsp;Zhenlin Chen,&nbsp;Chao Liu,&nbsp;Shangtong Li,&nbsp;Hao Chi,&nbsp;Meng-Qiu Dong","doi":"10.52601/bpr.2021.210004","DOIUrl":null,"url":null,"abstract":"<p><p>High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis. pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using pFind 3 in conjunction with pParse for data pre-processing and if needed, pQuant for quantitation. This streamlined pParse-pFind-pQuant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on <sup>15</sup>N-labeling, SILAC-labeling, or TMT/iTRAQ labeling.</p>","PeriodicalId":59621,"journal":{"name":"生物物理学报:英文版","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244800/pdf/","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"生物物理学报:英文版","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52601/bpr.2021.210004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

High-throughput proteomics based on mass spectrometry (MS) analysis has permeated biomedical science and propelled numerous research projects. pFind 3 is a database search engine for high-speed and in-depth proteomics data analysis. pFind 3 features a swift open search workflow that is adept at uncovering less obvious information such as unexpected modifications or mutations that would have gone unnoticed using a conventional data analysis pipeline. In this protocol, we provide step-by-step instructions to help users mastering various types of data analysis using pFind 3 in conjunction with pParse for data pre-processing and if needed, pQuant for quantitation. This streamlined pParse-pFind-pQuant workflow offers exceptional sensitivity, precision, and speed. It can be easily implemented in any laboratory in need of identifying peptides, proteins, or post-translational modifications, or of quantitation based on 15N-labeling, SILAC-labeling, or TMT/iTRAQ labeling.

Abstract Image

Abstract Image

Abstract Image

如何在深度蛋白质组学数据分析中使用open-pFind ?-从质谱数据中严格鉴定和定量肽和蛋白质的协议。
基于质谱(MS)分析的高通量蛋白质组学已经渗透到生物医学科学中,并推动了许多研究项目。pFind 3是一个用于高速和深入蛋白质组学数据分析的数据库搜索引擎。pFind 3提供了一个快速开放的搜索工作流,它擅长发现不太明显的信息,比如使用传统的数据分析管道无法注意到的意外修改或突变。在本协议中,我们提供分步说明,帮助用户掌握各种类型的数据分析,使用pFind 3与pParse结合进行数据预处理,如果需要,使用pQuant进行定量。这种精简的pParse-pFind-pQuant工作流程提供了卓越的灵敏度,精度和速度。它可以在任何需要鉴定肽,蛋白质或翻译后修饰的实验室中轻松实现,或基于15n标记,silac标记或TMT/iTRAQ标记的定量。
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
1.30
自引率
0.00%
发文量
117
×
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:481959085
Book学术官方微信