Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.
The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most ana...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-06-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005361&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849722182720028672 |
|---|---|
| author | Jens Christian Claussen Jurgita Skiecevičienė Jun Wang Philipp Rausch Tom H Karlsen Wolfgang Lieb John F Baines Andre Franke Marc-Thorsten Hütt |
| author_facet | Jens Christian Claussen Jurgita Skiecevičienė Jun Wang Philipp Rausch Tom H Karlsen Wolfgang Lieb John F Baines Andre Franke Marc-Thorsten Hütt |
| author_sort | Jens Christian Claussen |
| collection | DOAJ |
| description | The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions. |
| format | Article |
| id | doaj-art-7fd1664e0aec4f5f920cbad35e524f95 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2017-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-7fd1664e0aec4f5f920cbad35e524f952025-08-20T03:11:25ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-06-01136e100536110.1371/journal.pcbi.1005361Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome.Jens Christian ClaussenJurgita SkiecevičienėJun WangPhilipp RauschTom H KarlsenWolfgang LiebJohn F BainesAndre FrankeMarc-Thorsten HüttThe analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005361&type=printable |
| spellingShingle | Jens Christian Claussen Jurgita Skiecevičienė Jun Wang Philipp Rausch Tom H Karlsen Wolfgang Lieb John F Baines Andre Franke Marc-Thorsten Hütt Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. PLoS Computational Biology |
| title | Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. |
| title_full | Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. |
| title_fullStr | Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. |
| title_full_unstemmed | Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. |
| title_short | Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome. |
| title_sort | boolean analysis reveals systematic interactions among low abundance species in the human gut microbiome |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005361&type=printable |
| work_keys_str_mv | AT jenschristianclaussen booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT jurgitaskieceviciene booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT junwang booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT philipprausch booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT tomhkarlsen booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT wolfganglieb booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT johnfbaines booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT andrefranke booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome AT marcthorstenhutt booleananalysisrevealssystematicinteractionsamonglowabundancespeciesinthehumangutmicrobiome |