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Differentially methylated sites between Stage 4 HCC and healthy controls. (CSV 3562 kb)

Detailed variant information for all verified variants, including ACMG classification â Table showing details for variant classification such as pathogenicity prediction algorithms results and details of ACMG criteria application. (XLSX 18 kb)

Introduction: The threat bacterial pathogens pose to human health is increasing with the number and distribution of antibiotic-resistant bacteria, while the rate of discovery of new antimicrobials dwindles. Proteomics is playing key roles in understanding the molecular mechanisms of bacterial pathogenesis, and in identifying disease outcome determinants. The physical associations identified by proteomics can provide the means to develop pathogen-specific treatment methods that reduce the spread of antibiotic resistance and alleviate the negative effects of broad-spectrum antibiotics on beneficial bacteria. Areas covered: This review discusses recent trends in proteomics and introduces new and developing approaches that can be applied to the study of protein-protein interactions (PPIs) underlying bacterial pathogenesis. The approaches examined encompass options for mapping proteomes as well as stable and transient interactions in vivo and in vitro. We also explored the coverage of bacterial and human-bacterial PPIs, knowledge gaps in this area, and how they can be filled. Expert commentary: Identifying potential antimicrobial candidates is confounded by the complex molecular biology of bacterial pathogenesis and the lack of knowledge about PPIs underlying this process. Proteomics approaches can offer new perspectives for mechanistic insights and identify essential targets for guiding the discovery of next generation antimicrobials.

Additional file 5: Table S4.. ChromHMM-defined chromatin state enrichment for DMRs.

Additional file 4. Individual data values.

Additional file 10: Table S4. Performance of TAMPfinder program in comparison with Kalbfleisch et al. Both exclusive and common membership was analyzed in terms of GO annotation.

The KEGG pathway of regional DE immune-related genes. (XLSX 34Â kb)

Pathway over-representation analysis results for the WGCNA and Chaussabel modules sets. For each module identification strategy and module in turn, we carried out pathway over-representation analysis against the Broad Instituteâ s MSigDB collections by comparing the gene set and module membership using a simple hypergeometric test. We report both p-value and false discovery rate adjusting for multiple comparisons using the Benjamini-Hochberg procedure. (CSV 196Â kb)

Detailed variant information for all verified variants, including ACMG classification â Table showing details for variant classification such as pathogenicity prediction algorithms results and details of ACMG criteria application. (XLSX 18 kb)

Overview of Included TCDD Datasets. (XLSX 16Â kb)