PlantSecKB: the Plant Secretome and Subcellular Proteome KnowledgeBase
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Normal 0 MicrosoftInternetExplorer4 Normal 0 MicrosoftInternetExplorer4 Prediction and curation of protein subcellular locations is essential for protein functional annotation. We developed the Plant Secretome and Subcellular Proteome KnowledgeBase (PlantSecKB) for the plant research community to access and curate plant protein subcellular locations, with a focus on secreted proteins. The database is constructed with all the available plant protein data retrieved from the UniProtKB database and plant protein sequences predicted from EST data assembled by the PlantGDB project. The database contains information collected from three sources: (1) subcellular locations that were curated or computationally predicted in the UniProtKB; (2) subcellular locations and features predicted by eight computational tools; (3) secreted proteins that were curated from recent literature. The categories of subcellular locations include secretome, mitochondria, chloroplast, cytosol, cytoskeleton, endoplasmic reticulum, Golgi apparatus, lysosome, peroxisome, nucleus, vacuole, and plasma membrane. The data can be searched by using UniProt accession number or ID, GenBank GI or RefSeq accession number, gene name, and keywords. Species specific secretome and subcellular proteomes can be searched and downloaded into a FASTA file. BLAST is available to allow users to search the database based on protein sequences. Community curation for subcellular locations of plant proteins is also supported. A primary analysis revealed that monocots and dicots had a similar proportion of secretomes, and monocots had a significantly higher proportion of proteins distributed to mitochondria (both membrane and non-membrane) and chloroplast membrane, while dicots had significantly more proteins distributed to cytosol and nucleus. This database aims to facilitate plant protein research and is available at http://proteomics.ysu.edu/secretomes/plant.php. /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman;} /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman;}
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it