biostudies-literatureTang JInnovation Project on Industrial Generic Key Technologies of ChongqingFundamental Research Funds for Central UniversityNational Natural Science Foundation of ChinaNational Key Research and Development Program of China621-636https://www.ebi.ac.uk/biostudies/studies/S-EPMC7299298biostudies-literatureUnknown21(2)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 firstBriefings in bioinformaticsANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.PMC7299298cstc2015zdcy-ztzx1200032018CDQYSG00072018QNA7023CDJZR1446880110611CDJXZ2388262018YFC091050081872798CDJKXB14011Tang JLi XXue WLi YHong JFu JZhu FCui XChen YLi BYang QWang YfalseANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.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 first2020-01-01T00:00:00Z2020 Mar2026-04-14T21:21:12.014Z2025-06-01T12:28:16.158ZS-EPMC72992983064917110.1093/bib/bby127