CRIS: complete reconstruction of immunoglobulin <i>V-D-J</i> sequences from RNA-seq data
Bibliographic record
Abstract
Abstract Motivation B cells display remarkable diversity in producing B-cell receptors through recombination of immunoglobulin (Ig) V-D-J genes. Somatic hypermutation (SHM) of immunoglobulin heavy chain variable (IGHV) genes are used as a prognostic marker in B-cell malignancies. Clinically, IGHV mutation status is determined by targeted Sanger sequencing which is a resource-intensive and low-throughput procedure. Here, we describe a bioinformatic pipeline, CRIS (Complete Reconstruction of Immunoglobulin IGHV-D-J Sequences) that uses RNA sequencing (RNA-seq) datasets to reconstruct IGHV-D-J sequences and determine IGHV SHM status. Results CRIS extracts RNA-seq reads aligned to Ig gene loci, performs assembly of Ig transcripts and aligns the resulting contigs to reference Ig sequences to enumerate and classify SHMs in the IGHV gene sequence. CRIS improves on existing tools that infer the B-cell receptor repertoire from RNA-seq data using a portion IGHV gene segment by de novo assembly. We show that the SHM status identified by CRIS using the entire IGHV gene segment is highly concordant with clinical classification in three independent chronic lymphocytic leukemia patient cohorts. Availability and implementation The CRIS pipeline is available under the MIT License from https://github.com/Rashedul/CRIS. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".