MétaCan
Menu
Back to cohort
Record W4389525101 · doi:10.1186/s13072-023-00524-4

Expanding the list of sequence-agnostic enzymes for chromatin conformation capture assays with S1 nuclease

2023· article· en· W4389525101 on OpenAlex
Gridina Maria, P Karpov Andrey, Shadskiy Artem, Torgunakov Nikita, Khrapov Evgeny, Ryzhkova Oxana, Filipenko Maxim, Fishman Veniamin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEpigenetics & Chromatin · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersMinistry of Science and Higher Education of the Russian FederationRussian Science Support Foundation
KeywordsNucleaseChromatinBiologyComputational biologySequence (biology)Human geneticsMicrococcal nucleaseEnzymeGeneticsDNABiochemistryGeneNucleosome

Abstract

fetched live from OpenAlex

This study presents a novel approach for mapping global chromatin interactions using S1 nuclease, a sequence-agnostic enzyme. We develop and outline a protocol that leverages S1 nuclease's ability to effectively introduce breaks into both open and closed chromatin regions, allowing for comprehensive profiling of chromatin properties. Our S1 Hi-C method enables the preparation of high-quality Hi-C libraries, marking a significant advancement over previously established DNase I Hi-C protocols. Moreover, S1 nuclease's capability to fragment chromatin to mono-nucleosomes suggests the potential for mapping the three-dimensional organization of the genome at high resolution. This methodology holds promise for an improved understanding of chromatin state-dependent activities and may facilitate the development of new genomic methods.

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.

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 categoriesnone
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 score0.044
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.257
Teacher spread0.242 · 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