Visualisation and analysis of proteomic data from the procyclic form of <b> <i>Trypanosoma brucei</i> </b>
Bibliographic record
Abstract
We have undertaken a large scale study of the proteins expressed in the procyclic form of the parasite Trypanosoma brucei, which causes African sleeping sickness, using 2-DE and MS. The complete data set encompasses over 2000 identifications, of which 770 are distinct proteins. We have discovered that multiple protein isoforms appear to be common in T. brucei, as most proteins have been matched to more than one gel spot. We have developed visualisation software to investigate the differences between isoforms, based on the information from the results of database searches with MS data. We are able to highlight instances where PTMs are the most likely cause of variant forms. In other cases, spots that appear reproducibly across replicates contain fragments of proteins, arising either as experimental artefacts or as part of protein degradation. We are also able to classify clusters of gel spots into different groups based on the pattern of peptides that have been matched from MS data. The entire data set is stored within a relational database system that allows complex queries ( http://www.gla.ac.uk/functionalgenomics). Using specific proteins as examples, we demonstrate how the visualisation software and the database query facilities can be used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".