A Bayesian approach for estimating allele-specific expression from RNA-Seq data with diploid genomes
Bibliographic record
Abstract
BACKGROUND: RNA-sequencing (RNA-Seq) has become a popular tool for transcriptome profiling in mammals. However, accurate estimation of allele-specific expression (ASE) based on alignments of reads to the reference genome is challenging, because it contains only one allele on a mosaic haploid genome. Even with the information of diploid genome sequences, precise alignment of reads to the correct allele is difficult because of the high-similarity between the corresponding allele sequences. RESULTS: We propose a Bayesian approach to estimate ASE from RNA-Seq data with diploid genome sequences. In the statistical framework, the haploid choice is modeled as a hidden variable and estimated simultaneously with isoform expression levels by variational Bayesian inference. Through the simulation data analysis, we demonstrate the effectiveness of the proposed approach in terms of identifying ASE compared to the existing approach. We also show that our approach enables better quantification of isoform expression levels compared to the existing methods, TIGAR2, RSEM and Cufflinks. In the real data analysis of the human reference lymphoblastoid cell line GM12878, some autosomal genes were identified as ASE genes, and skewed paternal X-chromosome inactivation in GM12878 was identified. CONCLUSIONS: The proposed method, called ASE-TIGAR, enables accurate estimation of gene expression from RNA-Seq data in an allele-specific manner. Our results show the effectiveness of utilizing personal genomic information for accurate estimation of ASE. An implementation of our method is available at http://nagasakilab.csml.org/ase-tigar .
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".