Identification of conserved regulatory elements by comparative genome analysis
Bibliographic record
Abstract
BACKGROUND: For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments. RESULTS: We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at http://www.phylofoot.org/. CONCLUSIONS: Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".