cis-Regulatory Element Prediction in Mammalian Genomes
Bibliographic record
Abstract
The identification of cis-regulatory elements and modules is an important step in understanding the regulation of genes. We have developed a pipeline capable of running multiple motif prediction methods on a whole genome scale. Using gene expression datasets to identify co-expressed genes and the Ensemhl Compara database orthologues, we assemble input sequence sets comprised of the upstream regions of a target gene, its orthologues and co-expressed genes on the premise that such genes will share promoters by evolution (orthologues) or share regulatory control mechanisms (co-expressed genes). Co-expressed genes are identified by an approach that combines Pearson distances from multiple gene expression datasets derived from multiple experimental approaches and calibrated against the GO database. Our pipeline runs a number of established motif detection algorithms with a range of parameter settings on the input dataset. We integrate the diverse result sets by scoring motifs with a method-independent function. For each target gene, we assign p-values to the motif score by running the discovery pipeline on multiple sets of input sequence containing the target gene, non-coexpressed genes and "Jake" orthologues generated by neutral numerical evolution. We have predicted 30,636 motif binding sites in human for 4,182 genes and an initial set of 472 motif binding sites in mouse for 92 genes with p<0.001. The positive predictive value against a library of biologically confirmed regulatory sites approaches 0.4 at the highest p-value threshold. Predicted regulatory elements and other resources from the project are available at www.cisred.org.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.004 |
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".