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Record W2035767666 · doi:10.1016/j.dib.2014.11.003

Raw data for the identification of SUMOylated proteins in S. cerevisiae subjected to two types of osmotic shock, using affinity purification coupled with mass spectrometry

2014· article· en· W2035767666 on OpenAlexaff
Tharan Srikumar, Megan Lewicki, Brian Raught

Bibliographic record

VenueData in Brief · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMass spectrometryProteomicsSaccharomyces cerevisiaeOsmotic shockChemistryTandem mass spectrometryIsobaric labelingHsp70ChromatographyBiochemistryBiophysicsComputational biologyBiologyHeat shock proteinYeastProtein mass spectrometryGene

Abstract

fetched live from OpenAlex

The small ubiquitin-related modifier (SUMO) "stress response" (SSR) is a poorly understood evolutionarily conserved phenomenon in which steady-state SUMO conjugate levels are dramatically increased in response to environmental stresses. Here we describe the data acquired using affinity-purification coupled with mass spectrometry to identify proteins that are SUMOylated in response to two different types of osmotic stress, 1 M sorbitol and 1 M KCl. The mass spectrometry dataset described here has been uploaded to the MassIVE repository with ID: MSV000078739, and consists of 32 raw MS files acquired in data-dependent mode on a Thermo Q-Exactive instrument. iProphet-processed MS/MS search results and associated SAINT scores are also included as a reference. These data are discussed and interpreted in "The S. cerevisiae SUMO stress response is a conjugation-deconjugation cycle that targets the transcription machinery", by Lewicki et al. in the Journal of Proteomics, 2014 [1].

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.008

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 head0.042
GPT teacher head0.301
Teacher spread0.258 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2014
Admission routes1
Has abstractyes

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