An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs
Bibliographic record
Abstract
Here, we describe MetaErg, a standalone and fully automated metagenome and metaproteome annotation pipeline. Annotation of metagenomes is challenging. First, metagenomes contain sequence data of many organisms from all domains of life. Second, many of these are from understudied lineages, encoding genes with low similarity to experimentally validated reference genes. Third, assembly and binning are not perfect, sometimes resulting in artifactual hybrid contigs or genomes. To address these challenges, MetaErg provides graphical summaries of annotation outcomes, both for the complete metagenome and for individual metagenome-assembled genomes (MAGs). It performs a comprehensive annotation of each gene, including taxonomic classification, enabling functional inferences despite low similarity to reference genes, as well as detection of potential assembly or binning artifacts. When provided with metaproteome information, it visualizes gene and pathway activity using sequencing coverage and proteomic spectral counts, respectively. For visualization, MetaErg provides an HTML interface, bringing all annotation results together, and producing sortable and searchable tables, collapsible trees, and other graphic representations enabling intuitive navigation of complex data. MetaErg, implemented in Perl, HTML, and JavaScript, is a fully open source application, distributed under Academic Free License at https://github.com/xiaoli-dong/metaerg. MetaErg is also available as a docker image at https://hub.docker.com/r/xiaolidong/docker-metaerg.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.025 |
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".