IMPALA: A Comprehensive Pipeline for Detecting and Elucidating Mechanisms of Allele Specific Expression in Cancer
Bibliographic record
Abstract
Abstract Summary Allele-specific expression (ASE), where transcripts from one allele are more abundant than transcripts from the other, can arise from various genetic mechanisms and has implications for gene regulation and disease. We present IMPALA (Integrated Mapping and Profiling of Allelically-expressed Loci with Annotations), a versioned and containerized pipeline for detecting ASE in samples including cancer genomes. IMPALA leverages RNA sequencing data and, optionally, phased variant, copy number variant (CNV), allelic methylation, and mutation data to identify ASE genes and uncover underlying regulatory mechanisms. IMPALA incorporates the MBASED framework for ASE detection, and outputs a comprehensive summary table and informative figures to visualize the genomic distribution of ASE genes and their correlation with potential regulatory causes. We applied IMPALA to a cancer sample and identified thousands of genes with ASE and highlighted potential somatic events that may have influenced ASE of these genes. ASE data can be used to detect the downstream consequences of genomic alterations, which facilitates the identification of dysregulated cancer-related genes. IMPALA thus provides researchers with a powerful tool for both ASE analysis and for investigating genetic factors correlated with ASE. Availability and implementation IMPALA is licensed under GNU General Public License v3.0 and freely available at https://github.com/bcgsc/IMPALA and https://doi.org/10.5281/zenodo.8019168 with documentation and tutorial. Contact sjones@bcgsc.ca Supplemental information Supplemental materials are available at Bioinformatics online. Issue section: Gene expression
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.025 |
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".