Search

Efficient visualization helps researchers obtain valuable mechanistic insights and present interesting results with regard to the functional enrichment analysis of omics data. However, the functions of existing published tools used to implement relevant visualization are neither sufficiently comprehensive nor easily accessible. Most of these tools require users to have professional programming skills. This study alleviates this issue by proposing EnrichVisBox, a web application developed for integrative and versatile data visualization, including bubble plots, UpSet plots, polar bar plots, rectangle plots, ridgeline plots, network plots, and variant chord plots. Specifically, scientists can use these insightful plots to conveniently present functional enrichment analysis results of omics data with a simple mouse click through a user-friendly interface.

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

With an exponential growth in applications identifying protein post-translational modifications via mass spectrometry, discovery and presentation of motifs surrounding those modification sites have become increasingly desirable. Despite a few tools being designed, there is still a scarcity of effective and polyfunctional software for such analysis and illustrations. In this study, a versatile and user-friendly web tool is developed, motifeR, for extracting and visualizing statistically significant motifs from large datasets. Particularly, several functions are also integrated for processing multi-modification sites enrichment. Public datasets are applied to test their usability, indicating that some concurrent modification sites may form motifs and that peptides with low location probability may be not identified randomly and can be included to support motif discovery. In addition, for human phosphoproteomics datasets, the characterization of differential kinase signaling networks can be estimated and modeled by combining kinase-substrate relations based on the NetworKIN database as an optional feature for users. The motifeR toolkit can be conveniently operated by any scientific community or individuals, even those without any bioinformatics background and is freely available at https://www.omicsolution.org/wukong/motifeR., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Various types of unwanted and uncontrollable signal variations in MS-based metabolomics and proteomics datasets severely disturb the accuracies of metabolite and protein profiling. Therefore, pooled quality control (QC) samples are often employed in quality management processes, which are indispensable to the success of metabolomics and proteomics experiments, especially in high-throughput cases and long-term projects. However, data consistency and QC sample stability are still difficult to guarantee because of the experimental operation complexity and differences between experimenters. To make things worse, numerous proteomics projects do not take QC samples into consideration at the beginning of experimental design. Herein, a powerful and interactive web-based software, named pseudoQC, is presented to simulate QC sample data for actual metabolomics and proteomics datasets using four different machine learning-based regression methods. The simulated data are used for correction and normalization of the two published datasets, and the obtained results suggest that nonlinear regression methods perform better than linear ones. Additionally, the above software is available as a web-based graphical user interface and can be utilized by scientists without a bioinformatics background. pseudoQC is open-source software and freely available at https://www.omicsolution.org/wukong/pseudoQC/., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Deciphering and visualizing proteomics data are a big challenge for high-throughput proteomics research. In this work, we develop a free interactive web software platform, MixProTool, for processing multigroup proteomics data sets. This tool provides integrated data analysis workflow, including quality control assessment, normalization, soft independent modeling of class analogy, statistics, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. This software is also highly compatible with the identification and quantification results of various frequently used search engines, such as MaxQuant, Proteome Discoverer, or Mascot. Moreover, all analyzed results can be visualized as vector graphs and tables for further analysis. MixProTool can be conveniently operated by users, even those without bioinformatics training, and it is extremely useful for mining the most relevant features among different samples. MixProTool is deployed at the public shinyapps io server.

Because of inevitable and complicated signal variations in LC-MS n -based nontargeted metabolomics, normalization of metabolites data is a highly recommended procedure to assist in improving accuracies in metabolic profiling and discovery of potential biomarkers. Despite various normalization methods having been developed and applied for processing these data sets, it is still difficult to assess their performance. Moreover, such methods are elusive and difficult to choose for users, especially those without bioinformatics training. In this study, we present a powerful and user-friendly web platform, named MetaboGroup S, for comparison and evaluation of seven popular normalization methods and provide an optimal one automatically for end users based on the group entropies of every sample data point. For examination and application of this tool, we analyzed a complex clinical human data set from maintenance hemodialysis patients with erythrin resistance. Metabolite peaks (11 027) were extracted from the experimental data and then imported into this platform; the entire analysis process was completed sequentially within 5 min. To further test the performance and universality of MetaboGroup S, we analyzed two more published data sets including a nuclear magnetic resonance (NMR) data set on this platform. The results indicated that the method with a lower intragroup entropy and higher intergroup entropy would be preferable. In addition, MetaboGroup S can be quite conveniently operated by users and does not require any profound computational expertise or background for scientists in many fields. MetaboGroup S is freely available at https://omicstools.shinyapps.io/MetaboGroupSapp/ .