Unexpected absence of ribosomal protein genes from metagenome-assembled genomes
Bibliographic record
Abstract
Metagenome-assembled genomes (MAGs) have revealed the hidden diversity and functions of uncultivated microbes, but their reconstruction from metagenomes remains a computationally difficult task. Repetitive or exogenous sequences, such as ribosomal RNA and horizontally transferred genes, are frequently absent from MAGs because of misassembly and binning errors. Here, we report that ribosomal protein genes are also often absent from MAGs, although they are neither repetitive nor exogenous. Comprehensive analyses of more than 190,000 MAGs revealed that these genes could be missing in more than 20-40% of near-complete (i.e., with completeness of 90% or higher) MAGs. While some uncultivated environmental microbes intrinsically lack some ribosomal protein genes, we found that this unexpected absence is largely due to special evolutionary patterns of codon usage bias in ribosomal protein genes and algorithmic characteristics of metagenomic binning, which is dependent on tetranucleotide frequencies of contigs. This problem reflects the microbial life-history strategy. Fast-growing microbes tend to have this difficulty, likely because of strong evolutionary pressures on ribosomal protein genes toward the efficient assembly of ribosomes. Our observations caution those who study genomics and phylogeny of uncultivated microbes, the diversity and evolution of microbial genes in the central dogma, and bioinformatics in metagenomics.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".