MétaCan
Menu
Back to cohort
Record W4310266409 · doi:10.1038/s43705-022-00204-6

Unexpected absence of ribosomal protein genes from metagenome-assembled genomes

2022· article· en· W4310266409 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueISME Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyMinistry of Education, Culture, Sports, Science and TechnologyInstitute of Medical Science, University of TokyoJapan Society for the Promotion of ScienceInstitute of GeneticsUniversity of Tokyo
KeywordsMetagenomicsBiologyRibosomal RNAGenomeGeneRibosomal proteinGeneticsComputational biologyGenomicsEvolutionary biologyRibosomePhylogeneticsRNA

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.262
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it