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Abstract BackgroundHistone post-translational modifications (PTMs) play an important role in our system by regulating the structure of chromatin and therefore contribute to the regulation of gene and protein expression. Irregularities in histone PTMs can lead to a variety of different diseases including various forms of cancer. Histone modifications are analyzed using high resolution mass spectrometry, which generate large amounts of data that requires sophisticated bioinformatics tools for analysis and visualization. PTMViz is designed for downstream differential abundance analysis and visualization of both protein and/or histone modifications. ResultsPTMViz provides users with data tables and visualization plots of significantly differentiated proteins and histone PTMs between two sample groups. All the data is packaged into interactive data tables and graphs using the Shiny platform to help the user explore the results in a fast and efficient manner to assess if changes in the system are due to protein abundance changes or epigenetic changes. In the example data provided, we identified several proteins differentially regulated in the dopaminergic pathway between mice treated with methamphetamine compared to a saline control. We also identified histone post-translational modifications including histone H3K9me, H3K27me3, H4K16ac, and that were regulated due to drug exposure. ConclusionsHistone modifications play an integral role in the regulation of gene expression. PTMViz provides an interactive platform for analyzing proteins and histone post-translational modifications from mass spectrometry data in order to quickly identify differentially expressed proteins and PTMs.
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C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data
Abstract BackgroundStudying the co-occurrence network structure of microbial samples is one of the critical approaches to understanding the perplexing and delicate relationship between the microbe, host, and diseases. It is also critical to develop a tool for investigating co-occurrence networks and differential abundance analyses to reveal the disease-related taxa–taxa relationship. In addition, it is also necessary to tighten the co-occurrence network into smaller modules to increase the ability for functional annotation and interpretability of these taxa-taxa relationships. Also, it is critical to retain the phylogenetic relationship among the taxa to identify differential abundance patterns, which can be used to resolve contradicting functions reported by different studies. ResultsIn this article, we present Correlation and Consensus-based Cross-taxonomy Network Analysis (C3NA), a user-friendly R package for investigating compositional microbial sequencing data to identify and compare co-occurrence patterns across different taxonomic levels. C3NA contains two interactive graphic user interfaces (Shiny applications), one of them dedicated to the comparison between two diagnoses, e.g., disease versus control. We used C3NA to analyze two well-studied diseases, colorectal cancer, and Crohn’s disease. We discovered clusters of study and disease-dependent taxa that overlap with known functional taxa studied by other discovery studies and differential abundance analyses. ConclusionC3NA offers a new microbial data analyses pipeline for refined and enriched taxa–taxa co-occurrence network analyses, and the usability was further expanded via the built-in Shiny applications for interactive investigation.
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- Award ID(s):
- 2133504
- PAR ID:
- 10379315
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 23
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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