A novel pairwise sequence alignment algorithm for similarity search in massive datasets
Bibliographic record
Abstract
Advances in sequencing technologies have resulted in the production of a huge volume of data. Since the pairwise sequence alignment plays an essential role in comparing sequencing data, various algorithms have been developed. Among the previously suggested algorithms, the basic local alignment search tool (BLAST) is currently employed in a wide range of biological applications, largely due to its low time and memory complexity. However, not only BLAST but also other improved sequence alignment algorithms may fail to produce accurate results, therefore, more efficient algorithms can be highly advantageous. In the present study, we introduce a novel algorithm for sequence alignment (NASA) consisting of preprocessing and aligning steps. In the preprocessing step, the positions of residues are determined within a provided nucleotide or peptide sequence, resulting in seeking only informative regions. In the aligning step, based on a constant number of comparisons, the sequence similarity score is calculated between two sequences in a linear time and memory orders. To evaluate NASA, a large volume of sequencing data was analyzed and the outcomes were compared with other algorithms. The results showed that NASA outperforms other basic algorithms in terms of the elapsed time, required memory, system resource utilization, and alignment score precision. Collectively, NASA might be a promising method for retrieving similar sequences from large datasets.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".