VDJML – tools for capturing the results of inferring immune receptor rearrangements
Bibliographic record
Abstract
Abstract Despite the widespread use of immune repertoire profiling, there is currently no standardized format for output files from VDJ analysis. Researchers utilize software such as IgBlast and IMGT/High V-Quest to perform VDJ analysis and infer germline rearrangements. Each of these software tools produces results in a different file format, and can identify the same result using different labels. These differences make it challenging for users to perform additional analysis using the output file from one software to the next. We have addressed this problem by developing a standardized file format for representing results. The purpose of VDJML is to provide a common standardized format for different VDJ analysis applications and to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format is accompanied by a suite of analysis tools, which are accessible via command line and written in C++ and python. The VDJML suite will allow users to streamline their VDJ analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://www.vdjserver.org/software . We welcome participation from others in developing the file format standard, as well as code contributions.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".