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Record W2067298056 · doi:10.1101/gr.133850.111

A vast collection of microbial genes that are toxic to bacteria

2012· article· en· W2067298056 on OpenAlexfundno aff
Aya Kimelman, Asaf Levy, Hila Sberro, Shahar Kidron, Azita Leavitt, Gil Amitai, Deborah R. Yoder-Himes, Omri Wurtzel, Yiwen Zhu, Edward M. Rubin, Rotem Sorek

Bibliographic record

VenueGenome Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Genetics and Biotechnology
Canadian institutionsnot available
FundersBasic Energy SciencesNational Institute of Allergy and Infectious DiseasesOffice of ScienceNational Institutes of HealthAzrieli FoundationIsrael Science FoundationU.S. Department of Energy
KeywordsBiologyCloning (programming)GeneGenomeEscherichia coliGeneticsComputational biologyMolecular cloningDNAGene expression

Abstract

fetched live from OpenAlex

In the process of clone-based genome sequencing, initial assemblies frequently contain cloning gaps that can be resolved using cloning-independent methods, but the reason for their occurrence is largely unknown. By analyzing 9,328,693 sequencing clones from 393 microbial genomes, we systematically mapped more than 15,000 genes residing in cloning gaps and experimentally showed that their expression products are toxic to the Escherichia coli host. A subset of these toxic sequences was further evaluated through a series of functional assays exploring the mechanisms of their toxicity. Among these genes, our assays revealed novel toxins and restriction enzymes, and new classes of small, non-coding toxic RNAs that reproducibly inhibit E. coli growth. Further analyses also revealed abundant, short, toxic DNA fragments that were predicted to suppress E. coli growth by interacting with the replication initiator DnaA. Our results show that cloning gaps, once considered the result of technical problems, actually serve as a rich source for the discovery of biotechnologically valuable functions, and suggest new modes of antimicrobial interventions.

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 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)0.001
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 score0.163
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.052
GPT teacher head0.317
Teacher spread0.265 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2012
Admission routes1
Has abstractyes

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