CRIS: complete reconstruction of immunoglobulin <i>V-D-J</i> sequences from RNA-seq data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it