E-MEM: efficient computation of maximal exact matches for very large genomes
Bibliographic record
Abstract
MOTIVATION: Alignment of similar whole genomes is often performed using anchors given by the maximal exact matches (MEMs) between their sequences. In spite of significant amount of research on this problem, the computation of MEMs for large genomes remains a challenging problem. The leading current algorithms employ full text indexes, the sparse suffix array giving the best results. Still, their memory requirements are high, the parallelization is not very efficient, and they cannot handle very large genomes. RESULTS: We present a new algorithm, efficient computation of MEMs (E-MEM) that does not use full text indexes. Our algorithm uses much less space and is highly amenable to parallelization. It can compute all MEMs of minimum length 100 between the whole human and mouse genomes on a 12 core machine in 10 min and 2 GB of memory; the required memory can be as low as 600 MB. It can run efficiently genomes of any size. Extensive testing and comparison with currently best algorithms is provided. AVAILABILITY AND IMPLEMENTATION: The source code of E-MEM is freely available at: http://www.csd.uwo.ca/∼ilie/E-MEM/ CONTACT: ilie@csd.uwo.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".