Sequenoscope: A Modular Tool for Nanopore Adaptive Sequencing Analytics and Beyond
Bibliographic record
Abstract
This article presents Sequenoscope: a bioinformatics pipeline for analysing Oxford Nanopore Technologies (ONT) adaptive sampling sequencing data. Sequenoscope features three main modules: filter_ONT for filtering raw reads and creating a FASTQ file with a subset of reads for further analyses, analyze for generating sequencing and read mapping statistics against the provided reference taxon sequences and plot for interactive data summarization, comparison, and visualization between two datasets/test conditions. Here, we demonstrate the ability of the pipeline to analyse ONT adaptive sampling sequence data and provide examples of the outputs users can expect using data we generated. Adaptive sampling was performed on two ZymoBIOMICS Microbial Community DNA Standards, log-distributed (Cat# D6311) and even-distributed (Cat# D6306) formulations, with targeted depletions of Listeria monocytogenes . By comparing the test and control experimental data in FASTQ files from the sequencing runs, Sequenoscope showed that depletion of L. monocytogenes was successful by providing users with parameters to compare such as taxon coverage, read length and types of pore-level decisions made during sequencing. Although Sequenoscope was designed for ONT adaptive sampling data analysis, it supports short-read data from other sequencing platforms such as Illumina, allowing for the direct comparison of any two experimental conditions or cross-platform benchmarking.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".