Determining the Sub-Cellular Localization of Proteins within Caenorhabditis elegans Body Wall Muscle
Bibliographic record
Abstract
Determining the sub-cellular localization of a protein within a cell is often an essential step towards understanding its function. In Caenorhabditis elegans, the relatively large size of the body wall muscle cells and the exquisite organization of their sarcomeres offer an opportunity to identify the precise position of proteins within cell substructures. Our goal in this study is to generate a comprehensive "localizome" for C. elegans body wall muscle by GFP-tagging proteins expressed in muscle and determining their location within the cell. For this project, we focused on proteins that we know are expressed in muscle and are orthologs or at least homologs of human proteins. To date we have analyzed the expression of about 227 GFP-tagged proteins that show localized expression in the body wall muscle of this nematode (e.g. dense bodies, M-lines, myofilaments, mitochondria, cell membrane, nucleus or nucleolus). For most proteins analyzed in this study no prior data on sub-cellular localization was available. In addition to discrete sub-cellular localization we observe overlapping patterns of localization including the presence of a protein in the dense body and the nucleus, or the dense body and the M-lines. In total we discern more than 14 sub-cellular localization patterns within nematode body wall muscle. The localization of this large set of proteins within a muscle cell will serve as an invaluable resource in our investigation of muscle sarcomere assembly and function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".