Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines
| hal.structure.identifier | Laboratoire de Biologie Intégrative des Modèles Marins [LBI2M] | |
| dc.contributor.author | KARIMI, Elham | |
| hal.structure.identifier | Laboratoire de Biologie Intégrative des Modèles Marins [LBI2M] | |
| hal.structure.identifier | Station biologique de Roscoff = Roscoff Marine Station [SBR] | |
| dc.contributor.author | GESLAIN, Enora | |
| hal.structure.identifier | Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss] | |
| dc.contributor.author | BELCOUR, Arnaud | |
| hal.structure.identifier | Quadram Institute | |
| hal.structure.identifier | Pleiade, from patterns to models in computational biodiversity and biotechnology [PLEIADE] | |
| dc.contributor.author | FRIOUX, Clémence | |
| hal.structure.identifier | Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss] | |
| dc.contributor.author | AITE, Méziane | |
| hal.structure.identifier | Dynamics, Logics and Inference for biological Systems and Sequences [Dyliss] | |
| dc.contributor.author | SIEGEL, Anne | |
| hal.structure.identifier | Station biologique de Roscoff = Roscoff Marine Station [SBR] | |
| hal.structure.identifier | Fédération de recherche de Roscoff [FR2424] | |
| dc.contributor.author | CORRE, Erwan | |
| hal.structure.identifier | Laboratoire de Biologie Intégrative des Modèles Marins [LBI2M] | |
| dc.contributor.author | DITTAMI, Simon | |
| dc.date.issued | 2021-05-06 | |
| dc.identifier.issn | 2167-8359 | |
| dc.description.abstractEn | Animals, plants, and algae rely on symbiotic microorganisms for their development and functioning. Genome sequencing and genomic analyses of these microorganisms provide opportunities to construct metabolic networks and to analyze the metabolism of the symbiotic communities they constitute. Genome-scale metabolic network reconstructions rest on information gained from genome annotation. As there are multiple annotation pipelines available, the question arises to what extent differences in annotation pipelines impact outcomes of these analyses. Here, we compare five commonly used pipelines (Prokka, MaGe, IMG, DFAST, RAST) from predicted annotation features (coding sequences, Enzyme Commission numbers, hypothetical proteins) to the metabolic network-based analysis of symbiotic communities (biochemical reactions, producible compounds, and selection of minimal complementary bacterial communities). While Prokka and IMG produced the most extensive networks, RAST and DFAST networks produced the fewest false positives and the most connected networks with the fewest dead-end metabolites. Our results underline differences between the outputs of the tested pipelines at all examined levels, with small differences in the draft metabolic networks resulting in the selection of different microbial consortia to expand the metabolic capabilities of the algal host. However, the consortia generated yielded similar predicted producible compounds and could therefore be considered functionally interchangeable. This contrast between selected communities and community functions depending on the annotation pipeline needs to be taken into consideration when interpreting the results of metabolic complementarity analyses. In the future, experimental validation of bioinformatic predictions will likely be crucial to both evaluate and refine the pipelines and needs to be coupled with increased efforts to expand and improve annotations in reference databases. | |
| dc.description.sponsorship | Biotechnologies pour la valorisation des macroalgues - ANR-10-BTBR-0004 | |
| dc.language.iso | en | |
| dc.publisher | PeerJ | |
| dc.subject.en | Bioinformatics | |
| dc.subject.en | Computational Biology | |
| dc.subject.en | Genomics | |
| dc.subject.en | Microbiology | |
| dc.subject.en | Computational Gene prediction | |
| dc.subject.en | Functional annotation | |
| dc.subject.en | Genome-scale metabolic networks | |
| dc.subject.en | Metabolic complementary analyses | |
| dc.subject.en | Metabolic exchanges | |
| dc.subject.en | Holobionts | |
| dc.title.en | Robustness analysis of metabolic predictions in algal microbial communities based on different annotation pipelines | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.7717/peerj.11344 | |
| dc.subject.hal | Informatique [cs]/Bio-informatique [q-bio.QM] | |
| dc.subject.hal | Sciences du Vivant [q-bio]/Bio-Informatique, Biologie Systémique [q-bio.QM] | |
| bordeaux.journal | PeerJ | |
| bordeaux.page | 1-24 | |
| bordeaux.volume | 9 | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-03223662 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03223662v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=PeerJ&rft.date=2021-05-06&rft.volume=9&rft.spage=1-24&rft.epage=1-24&rft.eissn=2167-8359&rft.issn=2167-8359&rft.au=KARIMI,%20Elham&GESLAIN,%20Enora&BELCOUR,%20Arnaud&FRIOUX,%20Cl%C3%A9mence&AITE,%20M%C3%A9ziane&rft.genre=article |
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