{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tang J"],"funding":["Innovation Project on Industrial Generic Key Technologies of Chongqing","Fundamental Research Funds for Central University","National Natural Science Foundation of China","National Key Research and Development Program of China"],"pagination":["621-636"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7299298"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(2)"],"pubmed_abstract":["Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first"],"journal":["Briefings in bioinformatics"],"pubmed_title":["ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies."],"pmcid":["PMC7299298"],"funding_grant_id":["cstc2015zdcy-ztzx120003","2018CDQYSG0007","2018QNA7023","CDJZR14468801","10611CDJXZ238826","2018YFC0910500","81872798","CDJKXB14011"],"pubmed_authors":["Tang J","Li X","Xue W","Li Y","Hong J","Fu J","Zhu F","Cui X","Chen Y","Li B","Yang Q","Wang Y"],"additional_accession":[]},"is_claimable":false,"name":"ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.","description":"Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Mar","modification":"2026-04-14T21:21:12.014Z","creation":"2025-06-01T12:28:16.158Z"},"accession":"S-EPMC7299298","cross_references":{"pubmed":["30649171"],"doi":["10.1093/bib/bby127"]}}