Tile-X: A vertex reordering approach for scalable long read assembly
Bibliographic record
Abstract
Traditional long read assembly relies on computing overlaps between reads, followed by contig construction. Inherent to this process is the subproblem of ordering reads by their (unknown) genomic positions of origin-a challenge current assemblers do not explicitly address. Instead, read ordering becomes available only after assembly completes. We posit that computing a reliable read ordering beforehand, even if imperfect, can significantly reduce the computational burden of assembly, preserve quality, and enable scalable parallelization. We present Tile-X, a graph-theoretic approach that builds an overlap graph, then reorders the reads using vertex reordering techniques, and finally performs parallel partitioned assembly. We explore both standard reordering schemes (Tile-RCM, Tile-Metis, and Tile-Grappolo) and a custom heuristic (Tile-Far) that reduce redundancy by selecting a minimal informative subset of reads. On multiple real and simulated PacBio high-fidelity (HiFi) datasets, Tile-X improves NGA50 up to 2.1× while reducing runtime and memory usage by up to 3.5× and 3.3×, respectively.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".