Hardware acceleration of the novel two dimensional Burrows‐Wheeler Aligner algorithm with maximal exact matches seed extension kernel
Bibliographic record
Abstract
Abstract Next‐generation sequencing techniques have dramatically increased the amount of genomic data being sequenced, which calls for the acceleration of the alignment algorithms. This article proposes an FPGA‐based accelerated implementation of the seed extension kernel of the Burrows–Wheeler alignment genomic mapping algorithm. The well‐known Smith–Waterman algorithm is used during the seed extension to find the optimum alignment between the sequences. The state‐of‐the‐art architectures in the literature use one‐dimensional (1‐D) systolic arrays to fill a similarity matrix, based on the best score out of all match combinations, mismatches and gaps are computed. The cells on the same anti‐diagonal are computed in parallel in these architectures. We propose a novel 2‐dimensional architecture in which all the cells on the same rows and the same columns are computed in parallel and, thereby, significantly accelerated the process. The similarity matrix cells are computed in two phases: (1) the calculation phase and (2) error compensation phase. The calculation phase roughly approximate the cell values and the approximation error is fixed up during the error compensation phase. Our simulation results show that the proposed architecture can be up to 718x and 1.7x faster than the software execution and the 1‐D systolic arrays, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".