Pattern Detection in Multiple Genome Sequences with Applications: The Case of All SARS-CoV-2 Complete Variants
Bibliographic record
Abstract
Abstract Pattern detection and string matching are fundamental problems in computer science and the accelerated expansion of bioinformatics and computational biology have made them a core topic for both disciplines. The SARS-CoV-2 pandemic has made such problems more demanding with hundreds or thousands of new genome variants discovered every week, because of constant mutations, and the need for fast and accurate analyses. Medicines and, mostly, vaccines must be altered to adapt and efficiently address mutations. The need of computational tools for genomic analysis, such as sequence alignment, is very important, although, in most cases the resources and computational power needed is vast. The presented data structures and algorithms, specifically built for text mining and pattern detection, can help to address efficiently several bioinformatics problems. With a single execution of advanced algorithms, with limited space and time complexity, it is possible to acquire knowledge on all repeated patterns that exist in multiple genome sequences and this information can be used for further meta analyses. The potentials of the presented solutions are demonstrated with the analysis of more than 55,000 SARS-CoV-2 genome sequences (collected on March 10, 2021) and the detection of all repeated patterns with length up to 60 nucleotides in these sequences, something practically impossible with other algorithms due to its complexity. These results can be used to help provide answers to questions such as all variants common patterns, sequence alignment, palindromes and tandem repeats detection, genome comparisons, etc.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".