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The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.
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KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome
The microbes present in the human gastrointestinal tract are regularly linked to humanhealth and disease outcomes. Thanks to technological and methodological advances in re-cent years, metagenomic sequencing data, and computational methods designed to analyzemetagenomic data, have contributed to improved understanding of the link between thehuman gut microbiome and disease. However, while numerous methods have been recentlydeveloped to extract quantitative and qualitative results from host-associated microbiomedata, improved computational tools are still needed to track microbiome dynamics withshort-read sequencing data. Previously we have proposed KOMB as ade novotool foridentifying copy number variations in metagenomes for characterizing microbial genomedynamics in response to perturbations. In this work, we present KombOver (KO), whichincludes four key contributions with respect to our previous work: (i) it scales to largemicrobiome study cohorts, (ii) it includes bothk-core andK-truss based analysis, (iii)we provide the foundation of a theoretical understanding of the relation between variousgraph-based metagenome representations, and (iv) we provide an improved user experiencewith easier-to-run code and more descriptive outputs/results. To highlight the aforemen-tioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring lessthan 10 minutes and 10 GB RAM per sample to process these data. Furthermore, wehighlight how graph-based approaches such ask-core andK-truss can be informative forpinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fa-tigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at:https://github.com/treangenlab/komb
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- PAR ID:
- 10502790
- Publisher / Repository:
- WORLD SCIENTIFIC
- Date Published:
- Journal Name:
- Pacific Symposium on Biocomputing 2024
- ISBN:
- 978-981-12-8641-4
- Page Range / eLocation ID:
- 506 to 520
- Format(s):
- Medium: X
- Location:
- Kohala Coast, Hawaii, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1142/9789811286421_0039
