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Analysis of Expressed Sequence Tags (ESTs) in the Ciliated Protozoan <i>Tetrahymena thermophila</i>

2002· article· en· W2178121338 on OpenAlexafffund
Jeffrey Fillingham, N. Doane Chilcoat, Aaron P. Turkewitz, Eduardo Orias, Michael Reith, Ronald E. Pearlman

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsInstitute for Marine BiosciencesYork University
FundersNational Center for Research ResourcesNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of HealthOphthalmic Research Institute of Australia
KeywordsTetrahymenaBiologyGeneGenBankExpressed sequence tagGeneticsComplementary DNAcDNA libraryGenome

Abstract

fetched live from OpenAlex

To assess the utility of expressed sequence tag (EST) sequencing as a method of gene discovery in the ciliated protozoan Tetrahymena thermophila, we have sequenced either the 5' or 3' ends of 157 clones chosen at random from two cDNA libraries constructed from the mRNA of vegetatively growing cultures. Of 116 total non-redundant clones, 8.6% represented genes previously cloned in Tetrahymena. Fifty-two percent had significant identity to genes from other organisms represented in GenBank, of which 92% matched human proteins. Intriguing matches include an opioid-regulated protein, a glutamate-binding protein for an NMDA-receptor, and a stem-cell maintenance protein. Eleven-percent of the non-Tetrahymena specific matches were to genes present in humans and other mammals but not found in other model unicellular eukaryotes, including the completely sequenced Saccharomyces cerevisiae. Our data reinforce the fact that Tetrahymena is an excellent unicellular model system for studying many aspects of animal biology and is poised to become an important model system for genome-scale gene discovery and functional analysis.

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)0.000
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 score0.308
Threshold uncertainty score0.376

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 science0.0010.000
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 head0.024
GPT teacher head0.236
Teacher spread0.212 · 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

Citations30
Published2002
Admission routes2
Has abstractyes

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