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Record W2342023238 · doi:10.1101/044545

NanoSim: nanopore sequence read simulator based on statistical characterization

2016· preprint· en· W2342023238 on OpenAlexafffund
Chen Yang, Justin Chu, René L. Warren, İnanç Birol

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2016
Typepreprint
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaBC Cancer Agency
FundersNational Institutes of HealthGenome British ColumbiaGenome Canada
KeywordsNanoporeSequence (biology)Characterization (materials science)Computer scienceTime sequenceNanopore sequencingSimulationAlgorithmNanotechnologyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Abstract Motivation: In 2014, Oxford Nanopore Technologies (ONT) announced a new sequencing platform called MinION. The particular features of MinION reads – longer read lengths and single-molecule sequencing in particular – show potential for genome characterization. As of yet, the pre-commercial technology is exclusively available through early-access, and only a few datasets are publically available for testing. Further, no software exists that simulates MinION platform reads with genuine ONT characteristics. Results: In this article, we introduce NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data, and allows for adjustments upon improvement of nanopore sequencing technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.215
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2016
Admission routes2
Has abstractyes

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