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Record W3047015776 · doi:10.1016/j.xpro.2020.100079

Using Chromatin Accessibility to Delineate Therapeutic Subtypes in Pancreatic Cancer Patient-Derived Cell Lines

2020· article· en· W3047015776 on OpenAlexfundno aff
Holly Brunton, Ian Garner, Ulla‐Maja Bailey, Rosanna Upstill‐Goddard, Peter J. Bailey

Bibliographic record

VenueSTAR Protocols · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
FundersNational Cancer InstituteMedical Research CouncilNational Institutes of HealthHowat FoundationPancreatic Cancer Research FundMinistero della SaluteCancer AustraliaNational Health and Medical Research CouncilCancer Research UKOntario Institute for Cancer ResearchWellcome TrustPancreatic Cancer UKWellcomeAssociazione Italiana per la Ricerca sul CancroEngineering and Physical Sciences Research CouncilAmerican Association for Cancer Research
KeywordsChromatinComputational biologyPancreatic cancerPancreatic ductal adenocarcinomaTranscription factorBiologyCancer researchCancerGeneticsGene

Abstract

fetched live from OpenAlex

Disrupted chromatin regulatory processes contribute to the development of cancer, in particular pancreatic ductal adenocarcinoma. The assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) is typically used to study chromatin organization. Here, we present a revised ATAC-seq protocol to study chromatin accessibility in adherent patient-derived cell lines. We provide details on how to calculate the library molarity using Agilent's Bioanalyzer and an analysis pipeline for peak calling and transcription factor mapping. For complete details on the use and execution of this protocol, please refer to Brunton et al. (2020).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.051
GPT teacher head0.344
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations12
Published2020
Admission routes1
Has abstractyes

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