Computational curation and analysis of publicly available protein sequence data from a single protein family
Bibliographic record
Abstract
The wealth of sequence data available on public databases is increasing at an exponential rate, and while tremendous efforts are being made to make access to these resources easier, these data can be challenging for researchers to reuse because submissions are made from numerous laboratories with different biological objectives, resulting in inconsistent naming conventions and sequence content. Researchers can manually inspect each sequence and curate a dataset by hand but automating some of these steps will reduce this burden. This paper is a step-by-step guide describing how to identify all proteins containing a specific domain with the Conserved Protein Domain Architecture Retrieval Tool, download all associated amino acid sequences from NCBI Entrez, tabulate, and clean the data. I will also describe how to extract the full taxonomic information and computationally predict some physicochemical properties of the proteins based on amino acid sequence. The resulting data are applicable to a wide range of bioinformatic analyses where publicly available data are utilized. • Step-by-step guide to gathering, cleaning, and parsing data from publicly available databases for computational analysis, plus supplementation of taxonomic data and physicochemical characteristics from sequence data. • This strategy allows for reuse of existing large-scale publicly available data for different downstream applications to answer novel biological questions.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.012 |
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".