NANOSLAST: Nanopore Signal Local Alignment and Segmentation Tool
Bibliographic record
Abstract
Nanopore sequencing provided by Oxford Nanopore Technologies enables real-time analysis of native DNA or RNA molecules based on measured electrical signals called squiggles.While basecalling algorithms translate squiggles into nucleotide sequences, much of the raw signal information remains unexplored.Although several tools exist for inspecting the squiggles and offer simple signal visualization, they do not provide integrated workflows that allow users to search for specific nucleotide sequences and directly extract or visualize the corresponding raw signal segments.To address this gap, we developed NANOSLAST -a Nanopore Signal Local Alignment and Search Tool.This Python-based tool links local sequence similarity searches (via BLAST) to raw nanopore signals using basecalled SAM files and signal data from FAST5 or POD5 formats.The tool can be used to extract signals corresponding to specific motifs or functional elements, compare raw signal characteristics between different flowcells or sequencers or extract signalsequence pairs suitable for training machine learning models focused on basecalling, methylation detection or other purposes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".