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Record W1965315835 · doi:10.1186/1471-2164-12-431

Functional categorization of unique expressed sequence tags obtained from the yeast-like growth phase of the elm pathogen Ophiostoma novo-ulmi

2011· article· en· W1965315835 on OpenAlexafffund
William E. Hintz, Michael Pinchback, Paul Bastide, Steven Burgess, Volker Jacobi, Richard C. Hamelin, Colette Breuil, Louis Bernier

Bibliographic record

VenueBMC Genomics · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of British ColumbiaCentre Intégré de Santé et de Services Sociaux des LaurentidesUniversité LavalCentre de Géomatique du QuébecUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsBiologyDutch elm diseaseOphiostomaGeneticsExpressed sequence tagVirulenceGenBankComputational biologyGenomeKEGGPopulationDNA microarrayTranscriptomeGeneFungusGene expressionBotany

Abstract

fetched live from OpenAlex

BACKGROUND: The highly aggressive pathogenic fungus Ophiostoma novo-ulmi continues to be a serious threat to the American elm (Ulmus americana) in North America. Extensive studies have been conducted in North America to understand the mechanisms of virulence of this introduced pathogen and its evolving population structure, with a view to identifying potential strategies for the control of Dutch elm disease. As part of a larger study to examine the genomes of economically important Ophiostoma spp. and the genetic basis of virulence, we have constructed an expressed sequence tag (EST) library using total RNA extracted from the yeast-like growth phase of O. novo-ulmi (isolate H327). RESULTS: A total of 4,386 readable EST sequences were annotated by determining their closest matches to known or theoretical sequences in public databases by BLASTX analysis. Searches matched 2,093 sequences to entries found in Genbank, including 1,761 matches with known proteins and 332 matches with unknown (hypothetical/predicted) proteins. Known proteins included a collection of 880 unique transcripts which were categorized to obtain a functional profile of the transcriptome and to evaluate physiological function. These assignments yielded 20 primary functional categories (FunCat), the largest including Metabolism (FunCat 01, 20.28% of total), Sub-cellular localization (70, 10.23%), Protein synthesis (12, 10.14%), Transcription (11, 8.27%), Biogenesis of cellular components (42, 8.15%), Cellular transport, facilitation and routes (20, 6.08%), Classification unresolved (98, 5.80%), Cell rescue, defence and virulence (32, 5.31%) and the unclassified category, or known sequences of unknown metabolic function (99, 7.5%). A list of specific transcripts of interest was compiled to initiate an evaluation of their impact upon strain virulence in subsequent studies. CONCLUSIONS: This is the first large-scale study of the O. novo-ulmi transcriptome. The expression profile obtained from the yeast-like growth phase of this species will facilitate a multigenic approach to gene expression studies to assess their role in the determination of pathogenicity for this species. The identification and evaluation of gene targets in such studies will be a prerequisite to the development of biological control strategies for this pathogen.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.213
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2011
Admission routes2
Has abstractyes

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