Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes
Bibliographic record
Abstract
Abstract Motivation: The sheer scale of the metagenomic and metatranscriptomic datasets that are now available warrants the development of automated protocols for organizing, annotating and comparing the samples in terms of their metabolic profiles. We describe a user-friendly java program FROMP (Fragment Recruitment on Metabolic Pathways) for mapping and visualizing enzyme annotations onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways or custom-made pathways and comparing the samples in terms of their Pathway Completeness Scores, their relative Activity Scores or enzyme enrichment odds ratios. This program along with our fully configurable PERL-based annotation organization pipeline Meta2Pro (METAbolic PROfiling of META-omic data) offers a quick and accurate standalone solution for metabolic profiling of environmental samples or cultures from different treatments. Apart from pictorial comparisons, FROMP can also generate score matrices for multiple meta-omics samples, which can be used directly by other statistical programs. Availability: The source code and documentation for FROMP can be downloaded from https://sites.google.com/site/dhwanidesai/home/software along with the Meta2Pro collection of PERL scripts. Supplementary data are available at https://sites.google.com/site/dhwanidesai/home/fromp_suppl. Contact: Dhwani.Desai@Dal.Ca Supplementary information: Supplementary data are available at Bioinformatics online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".