Format

Send to

Choose Destination
BMC Bioinformatics. 2019 Mar 14;20(Suppl 2):96. doi: 10.1186/s12859-019-2623-x.

MNEMONIC: MetageNomic Experiment Mining to create an OTU Network of Inhabitant Correlations.

Author information

1
Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA. jdwren@gmail.com.
2
Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA.
3
Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
4
Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
5
Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
6
Arthritis and Clinical Immunology Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104-5005, USA. jonathan-wren@omrf.org.
7
Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. jonathan-wren@omrf.org.
8
Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. jonathan-wren@omrf.org.
9
Department of Geriatric Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA. jonathan-wren@omrf.org.

Abstract

BACKGROUND:

The number of publicly available metagenomic experiments in various environments has been rapidly growing, empowering the potential to identify similar shifts in species abundance between different experiments. This could be a potentially powerful way to interpret new experiments, by identifying common themes and causes behind changes in species abundance.

RESULTS:

We propose a novel framework for comparing microbial shifts between conditions. Using data from one of the largest human metagenome projects to date, the American Gut Project (AGP), we obtain differential abundance vectors for microbes using experimental condition information provided with the AGP metadata, such as patient age, dietary habits, or health status. We show it can be used to identify similar and opposing shifts in microbial species, and infer putative interactions between microbes. Our results show that groups of shifts with similar effects on microbiome can be identified and that similar dietary interventions display similar microbial abundance shifts.

CONCLUSIONS:

Without comparison to prior data, it is difficult for experimentalists to know if their observed changes in species abundance have been observed by others, both in their conditions and in others they would never consider comparable. Yet, this can be a very important contextual factor in interpreting the significance of a shift. We've proposed and tested an algorithmic solution to this problem, which also allows for comparing the metagenomic signature shifts between conditions in the existing body of data.

KEYWORDS:

Case-control shift; Differential abundance; Human microbiome; Meta-analysis

PMID:
30871469
PMCID:
PMC6419333
DOI:
10.1186/s12859-019-2623-x
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center