Brisk: Exact resource-efficient dictionary for <i>k</i> -mers
Bibliographic record
Abstract
ABSTRACT The rapid advancements in DNA sequencing technology have led to an unprecedented increase in the generation of genomic datasets, with modern sequencers now capable of producing up to ten terabases per run. However, the effective indexing and analysis of this vast amount of data pose significant challenges to the scientific community. K-mer indexing has proven crucial in managing extensive datasets across a wide range of applications, including alignment, compression, dataset comparison, error correction, assembly, and quantification. As a result, developing efficient and scalable k -mer indexing methods has become an increasingly important area of research. Despite the progress made, current state-of-the-art indexing structures are predominantly static, necessitating resource-intensive index reconstruction when integrating new data. Recently, the need for dynamic indexing structures has been recognized. However, many proposed solutions are only pseudo-dynamic, requiring substantial updates to justify the costs of adding new datasets. In practice, applications often rely on standard hash tables to associate data with their k -mers, leading to high k -mer encoding rates exceeding 64 bits per k -mer. In this work, we introduce Brisk, a drop-in replacement for most k -mer dictionary applications. This novel hashmap-like data structure provides high throughput while significantly reducing memory usage compared to existing dynamic associative indexes, particularly for large k -mer sizes. Brisk achieves this by leveraging hierarchical minimizer indexing and memory-efficient super- k -mer representation. We also introduce novel techniques for efficiently probing k -mers within a set of super- k -mers and managing duplicated minimizers. We believe that the methodologies developed in this work represent a significant advancement in the creation of efficient and scalable k -mer dictionaries, greatly facilitating their routine use in genomic data analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".