Assembly Arena: Benchmarking RNA isoform reconstruction algorithms for nanopore sequencing
Bibliographic record
Abstract
Abstract Resolving the transcriptomes of higher eukaryotes is more tangible with the advent of long read sequencing, which greatly facilitates the identification of new transcripts and their splicing isoforms. However, the computational analysis of long read RNA sequencing data remains challenging as it is difficult to disentangle technical artifacts from bona fide biological information. To address this, we evaluated the performance of multiple leading transcriptome assembly algorithms on their ability to accurately reconstruct RNA transcript isoforms. We specifically focused on deep nanopore sequencing of synthetic RNA spike-in controls (Sequins™ and SIRVs) across different chemistries, including cDNA and direct RNA protocols. Our systematic comparative benchmarking exposes the strengths and limitations of the different surveyed strategies. We also highlight conceptual and technical challenges with the annotation of transcriptomes and the formalization of assembly quality metrics. Our results complement similar recent endeavors, helping forge a path towards a gold standard analytical pipeline for long read transcriptome assembly.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".