Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data
Bibliographic record
Abstract
RNA-binding proteins (RBP) play diverse roles in mRNA processing and function. However, from thousands of RBPs encoded in the human genome, a detailed molecular understanding of their interactions with RNA is available only for a small fraction. In most cases, our knowledge of the combination of RNA sequence and structure required for specific RBP binding is insufficient for accurately predicting binding sites transcriptome-wide. In this context, the rapidly expanding collection of transcriptomic data sets that map distinct, yet intertwined posttranscriptional marks, such as RNA structure and RBP binding, presents an opportunity for integrative analysis to better characterize RBP binding. A grand challenge faced by our community is that relatively little information on the secondary structure context within and near RBP-binding sites has been gleaned from integrating such data sets, partially due to lack of suitable computational methods. To engage scientists from diverse backgrounds in addressing this gap, the RNA Society organized the RBP Footprint Grand Challenge in 2021, an international community effort to develop new methods or leverage existing ones for predicting RBP-binding sites through analysis of a growing volume of sequence, structure, and binding data and to experimentally validate select predictions. Here, we report the initiative, analyses, and methods developed by the participants, validation results, and five new in vivo binding data sets generated for validation. We hope our work will inspire additional innovation in computational methods, further utilization of available data resources, and future endeavors to engage the community in collaborating toward closing other critical data-analysis gaps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".