• Canada
• Research dataclose
• 2017-2021close
• figshare close

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

Read me file containing the information about the data presented in the data set document.

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.

Describes pipeline for variant discovery and provides scripts

Additional file 8: Figure S5. Original unmodified image of the revelation of GAD65 by western blot in the TBS soluble fraction. This revelation preceded that of actin, which is also present in the image.

Additional file 3. File used to create PAUP* phylogenetic tree containing data from this study as well as Weaver et al. [12]. (FAS 213 kb)

This file contains all sensitivity, precision, and F 1-score values for our tests. The averages were included in Table 2. (XLSX 78 KB)

Reverse transcriptases and UHR sequence divergence. UHR whole transcriptome data was assessed for divergence of sequence relative to a compendium of human reference comprised of known single nucleotide polymorphisms (SNPs). n = 3; error bars = Standard Deviation. (JPEG 24 kb)

Additional file 6: Table S1. All metrics for AWB and BGS long-read assemblies. Table S2. All metrics for AWB hybrid assemblies. Table S3. Overlapping structural variants and genes in AWB. Table S4. Overlapping structural variants and genes in BGS. Table S5. Overlapping structural variants and genes in Giardia beaver. Table S6. Top performing AWB and BGS long-read assemblies. Table S7. Summarized assembler metrics in AWB long-read assemblies. Table S8. Summarized assembler metrics in BGS long-read assemblies. Table S9. Summarized metrics for 1D vs 1Dsq AWB long-read assemblies. Table S10. Summarized metrics for 1D vs 1Dsq BGS long-read assemblies. Table S11. All metrics for corresponding 1D and 1Dsq AWB and BGS long-read assemblies. Table S12. Summarized metrics for pooling input AWB long-read assemblies. Table S13. Summarized metrics for pooling input BGS long-read assemblies. Table S14. Summarized metrics for polishing AWB long-read assemblies. Table S15. Summarized metrics for polishing BGS long-read assemblies.