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Record W2949167437 · doi:10.3390/microorganisms7060171

Fugacium Spliced Leader Genes Identified from Stranded RNA-Seq Datasets

2019· article· en· W2949167437 on OpenAlexaff
Yue Song, Bahareh Zaheri, Min Liu, Sunil Kumar Sahu, Huan Liu, Wenbin Chen, Bo Song, David Morse

Bibliographic record

VenueMicroorganisms · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité de Montréal
FundersChina Postdoctoral Science FoundationGovernment of Jiangxi ProvinceNational Natural Science Foundation of China
KeywordsGeneIntronTrans-splicingBiologyRNA splicingGeneticsRNA-SeqAlternative splicingRNAComputational biologyTranscriptomeGene expressionExon

Abstract

fetched live from OpenAlex

Trans-splicing mechanisms have been documented in many lineages that are widely distributed phylogenetically, including dinoflagellates. The spliced leader (SL) sequence itself is conserved in dinoflagellates, although its gene sequences and arrangements have diversified within or across different species. In this study, we present 18 Fugacium kawagutii SL genes identified from stranded RNA-seq reads. These genes typically have a single SL but can contain several partial SLs with lengths ranging from 103 to 292 bp. Unexpectedly, we find the SL gene transcripts contain sequences upstream of the canonical SL, suggesting that generation of mature transcripts will require additional modifications following trans-splicing. We have also identified 13 SL-like genes whose expression levels and length are comparable to Dino-SL genes. Lastly, introns in these genes were identified and a new site for Sm-protein binding was proposed. Overall, this study provides a strategy for fast identification of SL genes and identifies new sequences of F. kawagutii SL genes to supplement our understanding of trans-splicing.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.085
Threshold uncertainty score0.977

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)0.0830.024

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 head0.009
GPT teacher head0.212
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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