The Challenge and Potential of Metagenomics in the Clinic
Bibliographic record
Abstract
The bacteria, fungi, and viruses that live on and in us have a tremendous impact on our day-to-day health and are often linked to many diseases, including autoimmune disorders and infections. Diagnosing and treating these disorders relies on accurate identification and characterization of the microbial community. Current sequencing technologies allow the sequencing of the entire nucleic acid complement of a sample providing an accurate snapshot of the community members present in addition to the full genetic potential of that microbial community. There are a number of clinical applications that stand to benefit from these data sets, such as the rapid identification of pathogens present in a sample. Other applications include the identification of antibiotic-resistance genes, diagnosis and treatment of gastrointestinal disorders, and many other diseases associated with bacterial, viral, and fungal microbiomes. Metagenomics also allows the physician to probe more complex phenotypes such as microbial dysbiosis with intestinal disorders and disruptions of the skin microbiome that may be associated with skin disorders. Many of these disorders are not associated with a single pathogen but emerge as a result of complex ecological interactions within microbiota. Currently, we understand very little about these complex phenotypes, yet clearly they are important and in some cases, as with fecal microbiota transplants in Clostridium difficile infections, treating the microbiome of the patient is effective. Here, we give an overview of metagenomics and discuss a number of areas where metagenomics is applicable in the clinic, and progress being made in these areas. This includes (1) the identification of unknown pathogens, and those pathogens particularly hard to culture, (2) utilizing functional information and gene content to understand complex infections such as Clostridium difficile, and (3) predicting antimicrobial resistance of the community using genetic determinants of resistance identified from the sequencing data. All of these applications rely on sophisticated computational tools, and we also discuss the importance of skilled bioinformatic support for the implementation and use of metagenomics in the clinic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".