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Record W4361856722 · doi:10.47371/s10267-010-0086-z

A note on the incidence of reverse complementary fungal ITS sequences in the public sequence databases and a software tool for their detection and reorientation

2011· article· en· W4361856722 on OpenAlexaff
R. Henrik Nilsson, Vilmar Veldre, Zheng Wang, Martin Eckart, Sara Branco, Martin Hartmann, Christopher Quince, Anna Godhe, Yann Bertrand, Johan F. Alfredsson, Karl‐Henrik Larsson, Urmas Kõljalg, Kessy Abarenkov

Bibliographic record

VenueMycoscience · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of British Columbia
FundersTartu Ülikool
KeywordsBiologyUnixSoftwareSequence (biology)GeneticsDNA sequencingDatabaseGeneComputational biologyBioinformaticsProgramming languageComputer science

Abstract

fetched live from OpenAlex

Reverse complementary DNA sequences—sequences that are inadvertently cast backward and in which all purines and pyrimidines are transposed—are not uncommon in sequence databases, where they may introduce noise into sequence-based research. We show that about 1% of the public fungal ITS sequences, the most commonly sequenced genetic marker in mycology, are reverse complementary, and we introduce an open source software solution to automate their detection and reorientation. The MacOSX/Linux/UNIX software operates on public or private datasets of any size, although some 50 base pairs of the 5.8 S gene of the ITS region are needed for the 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 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)0.031
metaresearch head score (Gemma)0.111
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.111
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0020.002
Scholarly communication0.0050.007
Open science0.0020.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.005

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 head0.070
GPT teacher head0.285
Teacher spread0.215 · 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
GenreMethods

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

Citations1
Published2011
Admission routes1
Has abstractyes

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