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Record W2763738239 · doi:10.1038/s41598-017-13186-6

Genome-wide open chromatin regions and their effects on the regulation of silk protein genes in Bombyx mori

2017· article· en· W2763738239 on OpenAlexaff
Quan Zhang, Tingcai Cheng, Shengkai Jin, Youbing Guo, Yuqian Wu, Duolian Liu, Xiaomin Xu, Yueting Sun, Zhiqing Li, Huawei He, Qingyou Xia

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsMinistry of Agriculture
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsGeneChromatinBombyx moriTranscription factorBiologySILKBombyxGenomeDNAGeneticsComputational biologyCell biology

Abstract

fetched live from OpenAlex

Nucleosome-depleted open chromatin regions (OCRs) often harbor transcription factor (TF) binding sites that are associated with active DNA regulatory elements. To investigate the regulation of silk-protein genes, DNA molecules isolated from the silk glands of third-day fifth-instar silkworm larvae and embryo-derived (BmE) cells were subjected to formal dehyde-assisted isolation of regulatory elements (FAIRE) and high-throughput sequencing. In total, 68,000 OCRs were identified, and a number of TF-binding motifs were predicted. In particular, OCRs located near silk-protein genes contained potential binding sites for functional TFs. Moreover, many TFs were found to bind to clusters of OCRs upstream of silk-protein genes, and to regulate the expression of these genes. The expression of silk protein genes may be related not only to regulating TFs (such as fkh, Bmdimm, and Bmsage), but also to developmental and hormone-induced TFs (such as zen, eve, Br, and eip74ef). Elucidation of genome-wide OCRs and their regulatory motifs in silk protein genes will provide valuable data and clues for characterizing the mechanisms of transcriptional control of silk protein genes.

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)0.002
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 score0.124
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2017
Admission routes1
Has abstractyes

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