A Guide to Bioinformatics for Immunologists
Bibliographic record
Abstract
Bioinformatics includes a suite of methods, which are cheap, approachable, and many of which are easily accessible without any sort of specialized bioinformatic training. Yet, despite this, bioinformatic tools are under-utilized by immunologists. Herein, we review a representative set of publicly available, easy-to-use bioinformatic tools using our own research on an under-annotated human gene, SCARA3, as an example. SCARA3 shares an evolutionary relationship with the class A scavenger receptors, but preliminary research showed that it was divergent enough that its function remained unclear. In our quest for more information about this gene - did it share gene sequence similarities to other scavenger receptors? Did it contain conserved protein domains? Where was it expressed in the human body? - we discovered the power and informative potential of publicly available bioinformatic tools designed for the novice in mind, which allowed us to hypothesize on the regulation, structure, and function of this protein. We argue that these tools are largely applicable to many facets of immunology research.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.097 | 0.197 |
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".