Improving Equivalency in Metagenomics: A Harmonized Process to Extract Fecal DNA
Notice bibliographique
Résumé
In the field of clinical chemistry, there is a major push to harmonize and standardize laboratory processes and materials to improve the equivalency of reported test results and reduce the risk of postanalytical errors. The impetus of harmonized results is expanding to the field of molecular pathology and is seen in the development of standardized reference materials for the measurement of pathogens in clinical samples, and the harmonization of PCR and fluorescence in situ hybridization methods for noninvasive prenatal diagnosis and cancer screening, respectively. Molecular methods are used to study microbial communities in the human body to assess contributions of microbiota to human health. Microbiota in the gastrointestinal tract comprise one of the best studied ecosystems owing to their large volume, high diversity, and relevance to several pathologies (e.g., diabetes, inflammatory bowel disease, and colorectal cancer). Until now, most studies in gut microbiota have used unique methodologies resulting in demographically distinct cohorts. This has limited the potential for interstudy comparisons and metaanalyses, as it is difficult to disentangle biological from methodological variation. To address the technical variation in metagenomics and more confidently assess contributions of microbiota to human health, a recent article by Costea et al.(1) suggests that a harmonized protocol outlined by the authors be used to extract DNA from feces. The authors tested 21 DNA extraction protocols on the same fecal samples and quantified the differences observed in the microbial community compositions. Procedural variables of library preparation and sample storage were contrasted with biological variations observed within the same specimen or within an individual over time. Extraction protocols were then ranked by the resulting DNA quantity and quality, estimates of recovered community diversity, and the ratio between gram-positive and gram-negative bacteria. Procedure reproducibility was tested within and across laboratories, and the accuracy of the top-performing DNA extraction methods was assessed using a mock community of bacterial species whose exact relative abundance was known. If only the ranks of the bacterial species of interest are required, most of the available protocols tested gave highly comparable results. However, for many applications, species-specific abundance information is required, and this information needs to be commensurable between methods. Using this metric, many of the protocols tested were not equivalent and instead introduced large batch effects. Of the species particularly affected by the extraction protocol, the majority were gram-positive, an unsurprising finding given the higher mechanical strength of gram-positive bacterial cell walls. In the selection of the final protocol, reproducibility, recovery of bacterial diversity, and automation were factors. In addition, the selected winning protocol accurately extracted DNA from the spiked bacteria in the test stool samples. As the literature regarding the harmonization of molecular methods in research or clinical laboratories expands, this article makes an important contribution. Variations in DNA extraction protocols can have large effects on the observed microbial composition of stool samples, and a harmonized method will improve the comparability of human gut microbiome studies and facilitate metaanalysis. Furthermore, because of its performance characteristics, the selected protocol should serve as a benchmark for new methods.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,077 | 0,109 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,010 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,004 | 0,011 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».