Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data.
Orel Mizrahi, Meredith Corley, Ori Feldman, Thorben Fröhlking, Lei Sun, Alison Ziesel, Maciej Antczak, Mattia Bernetti, Shaimae I Elhajjajy, Wenze Huang, Grady G Nguyen, Samuel S Park, Raul I Perez Martell, Luke Trinity, Kui Xu, Tomasz Zok, Giovanni Bussi, Hosna Jabbari, Yaron Orenstein, Sharon Aviran, Michelle M Meyer, Gene W Yeo
RNA (New York, N.Y.) · 2025 · Vol. 31 (8) · pp. 1103-1124
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.
Linked datasets 1
Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data
Homo sapiens
40 data files
| File | Type | Size |
|---|---|---|
| hnRNPA2B1_HepG2_IN1.fastq.gz | RNA-Seq | 1.1 GB |
| hnRNPA2B1_HepG2_IN2.fastq.gz | RNA-Seq | 1.3 GB |
| hnRNPA2B1_HepG2_IP1.fastq.gz | RNA-Seq | 1.4 GB |
| hnRNPA2B1_HepG2_IP2.fastq.gz | RNA-Seq | 1.5 GB |
| HNRNPC_K562_In1.fastq.gz | RNA-Seq | 880.9 MB |
| HNRNPC_K562_In2.fastq.gz | RNA-Seq | 890.3 MB |
| HNRNPC_K562_IP1.fastq.gz | RNA-Seq | 829.7 MB |
| HNRNPC_K562_IP2.fastq.gz | RNA-Seq | 795.7 MB |
| PRPF17_Hek293T_In1.fastq.gz | RNA-Seq | 5.1 GB |
| PRPF17_Hek293T_In2.fastq.gz | RNA-Seq | 602.2 MB |
| PRPF17_Hek293T_IP1.fastq.gz | RNA-Seq | 682.7 MB |
| PRPF17_Hek293T_IP2.fastq.gz | RNA-Seq | 567.9 MB |
| PRPF17_HeLa_In1.fastq.gz | RNA-Seq | 466.0 MB |
| PRPF17_HeLa_In2.fastq.gz | RNA-Seq | 524.0 MB |
| PRPF17_HeLa_IP1.fastq.gz | RNA-Seq | 991.2 MB |
| PRPF17_HeLa_IP2.fastq.gz | RNA-Seq | 1.0 GB |
| SND1_k562_IN1.fastq.gz | RNA-Seq | 952.9 MB |
| SND1_k562_IN2.fastq.gz | RNA-Seq | 1.4 GB |
| SND1_k562_IP1.fastq.gz | RNA-Seq | 1.3 GB |
| SND1_k562_IP2.fastq.gz | RNA-Seq | 1.2 GB |
| SRR28464922_GSM8171533 | RNA-Seq | 1.2 GB |
| SRR28464923_GSM8171532 | RNA-Seq | 1.3 GB |
| SRR28464924_GSM8171531 | RNA-Seq | 1.4 GB |
| SRR28464925_GSM8171530 | RNA-Seq | 952.0 MB |
| SRR28464926_GSM8171529 | RNA-Seq | 1.0 GB |
| SRR28464927_GSM8171528 | RNA-Seq | 991.0 MB |
| SRR28464928_GSM8171527 | RNA-Seq | 523.0 MB |
| SRR28464929_GSM8171526 | RNA-Seq | 466.0 MB |
| SRR28464930_GSM8171525 | RNA-Seq | 567.0 MB |
| SRR28464931_GSM8171524 | RNA-Seq | 682.0 MB |
| SRR28464932_GSM8171523 | RNA-Seq | 602.0 MB |
| SRR28464933_GSM8171522 | RNA-Seq | 5.1 GB |
| SRR28464938_GSM8171517 | RNA-Seq | 1.5 GB |
| SRR28464939_GSM8171516 | RNA-Seq | 1.4 GB |
| SRR28464940_GSM8171515 | RNA-Seq | 1.3 GB |
| SRR28464941_GSM8171514 | RNA-Seq | 1.1 GB |
| SRR32612463_GSM8171518 | RNA-Seq | 880.0 MB |
| SRR32612464_GSM8171519 | RNA-Seq | 890.0 MB |
| SRR32612465_GSM8171520 | RNA-Seq | 829.0 MB |
| SRR32612466_GSM8171521 | RNA-Seq | 795.0 MB |
Potentially related datasets 10
These accessions were text-mined from the PMC full text. They may be referenced for comparison, cited from other studies, or otherwise mentioned without being primary data for this paper.
| Accession | Source | Title |
|---|---|---|
| GSE145805 | GSE · GEO GEO ↗ | RNA secondary structome of different cell lines |
| GSE227455 | GSE · GEO GEO ↗ | RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data |
| GSE262542 | GSE · GEO GEO ↗ | Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data |
| GSE86464 | GSE · GEO GEO ↗ | HNRNPA2B1 regulates alternative RNA processing in the nervous system and accumulates in granules in ALS IPSC-derived motor neurons |
| GSM2400181 | GSM · GEO | No title in the index |
| GSM3734788 | GSM · GEO | No title in the index |
| ENCSR249ROI | ENCSR · ENCODE | eCLIP experiment on K562 against HNRNPC |
| ENCSR128VXC | ENCSR · ENCODE | eCLIP experiment on K562 against SND1 |
| ENCSR815VVI | ENCSR · ENCODE | eCLIP experiment on HepG2 against CDC40 |
| ENCFF859XKH | ENCFF · ENCODE | No title in the index |
Analysis pipelines 3
| Pipeline | Assay | Steps | Dataset |
|---|---|---|---|
| GSE145805 SHAPE-seq Data Processing geo_data_processing | SHAPE-seq | 7 | GSE145805 |
| GSE227455 eCLIP Data Processing geo_data_processing | eCLIP | 7 | GSE227455 |
| GSE262542 eCLIP Data Processing geo_data_processing | eCLIP | 4 | GSE262542 |
Funding 1 grant
| Grant | Funder |
|---|---|
| R21 GM148835 | NIGMS NIH HHS (United States) |