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. 2021 Dec 11;18:29. doi: 10.1186/s12014-021-09334-w

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — The software operation process (including phenotype profiling, processing, data quality evaluation, and database annotation) taking the proteome and glycoproteome data of HGSOC as an example. A Phenotype profiling by interactive characterization of the population in different phenotypes. B Phenotype profiling by phenotype correlation for revealing dependencies among different phenotypes. C Box plots of expression values of all normalized samples in the proteome data set of HGSOC. D Data quality evaluation by correlation of QC samples in the proteome data set of HGSOC. E Data quality evaluation by a distribution of coefficient of variant (CV) values of features in three samples in the proteome data set of HGSOC. F Table of feature details of intact glycopeptide identified in the glycoproteome data set of HGSOC associated with linkage to the knowledge database of N-GlycositeAtlas