From motif to function: Inferring the functions of long zinc finger proteins through combinatorial selection
Bibliographic record
Abstract
ABSTRACT C2H2 zinc finger proteins (ZFPs) comprise of the largest group of DNA-binding proteins in human genome, and many of them contain long, tandem array of fingers, making the motif discovery, prediction of in vivo cis -regulatory elements (CREs), and understanding their functions particularly challenging. Previous work established that due to the dependent recognition between sub-motifs, the simple, additive recognition model impedes motif discovery and compromises our understanding about how ZFPs work. This work uses ZFP3, a 13-finger long ZFP with no known function, as case example to address the reverse question---given the full-length motif learned through in vitro experiments, like Spec-seq and HT-SELEX, how to reliably identify its in vivo cis-regulatory elements (CREs) and further predict this gene’s functions. Through sorting of all possible sites within the ChIP-seq peaks with similar predicted binding energy into groups and comparing the aggregate ChIP-seq signals between groups, it is evident that either its full-length or individual sub-motif alone fails to correctly identify all high-affinity specific sites without false-positives, thus it is necessary to revise current algorithm, and use both the core and upstream motifs as separate components to improve the prediction accuracy. Furthermore, significant number of regulatory elements of ZFP3 are found to be proximal to genes associated with microtubules organization and ciliogenesis, which coincides with the fact that ZFP3 is specifically upregulated in multiple ciliated cells. At last, local chromatin accessibility and active chromatin marks like H3K27ac are found to positively associate with the differential binding of ZFP3 between tested cell lines. Overall, this work establishes a novel “From motif to function” strategy for long ZFPs, and the data analysis workflows are implemented through R package TFCookbook for reuse onto other ZFPs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".