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Ranked by formulaVerdict: Notable

T2G: fast and accurate transcriptome to genome BAM liftover for short and long read RNA sequencing

Aligning RNA sequencing reads to the transcriptome rather than the genome recovers short and junction-spanning reads that genome alignment loses or misplaces, but it leaves the alignments in transcript coordinates…

By Rudler, Mahmud, Cleynen +1

Score████░░░░░░4.3

Key numbers

  • 8.5-fold compared with unspliced genome

Caveats

  • Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

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Abstract

Aligning RNA sequencing reads to the transcriptome rather than the genome recovers short and junction-spanning reads that genome alignment loses or misplaces, but it leaves the alignments in transcript coordinates, which genome browsers, variant callers and most annotation tools cannot use. Existing liftover tools translate between genome assemblies, and the few that reach transcript coordinates are tied to a single quantification model or assay type. Here we describe T2G, which converts complete BAM files from transcriptome to genome coordinates while keeping CIGAR strings, alignment tags and per-base RNA modification calls intact. Across nanopore direct RNA sequencing, ribosome footprinting and paired-end Illumina RNA sequencing, aligning to the transcriptome and lifting with T2G gave higher on-target specificity and more accurate splice junctions than direct genome alignment, removed the systematic assignment of reads to processed pseudogenes, and preserved isoform identity, revealing isoform-specific modification levels that genome alignment pools together. In a reanalysis of a published RNA-binding protein experiment, T2G reduced the number of reads attributed to pseudogenes 8.5-fold compared with unspliced genome alignment. T2G is available at https://github.com/Arnaroo/T2GD as a Python reference implementation and as t2gd, a dependency-free binary for Linux, macOS and Windows that also provides a gene-level quantifier and BAM utilities toolkit.

D. L. Rudler, S. Mahmud, A. Cleynen, N. E. Shirokikh

The editor's rubric

Heuristic review

Leverage3 / 5
A method or resource many groups across the field will adopt within a year.
24% of this desk’s rubric
Magnitude3 / 5
Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
16% of this desk’s rubric
Evidence3 / 5
Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
20% of this desk’s rubric
Novelty3 / 5
A genuinely new approach to an open problem.
20% of this desk’s rubric
Trajectory2 / 5
Some room to improve with obvious engineering.
10% of this desk’s rubric
Stakes2 / 5
Benefits a professional community (practitioners, clinicians, engineers).
10% of this desk’s rubric

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we report); gains (x-fold); novelty (alternative to status quo); verification (error bars, code released).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.3

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit
5.6 / 10
Weighted rubric, evidence-gated.
Adjusted merit
4.6 / 10
Shrunk toward the desk prior by editor confidence (38%).
Attention
0%
Citations, upvotes, points, mentions.
Freshness
88%
Half-life decay since publication.

No attention signals recorded yet.

The record

  • Reviewed by heuristic-v7 on Oct 22, 2026, 05:48 UTC. Paper type: method.
  • Categories: bioinformatics
  • BRIEF, No.3 in the Biology edition of October 22, 2026.