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Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data

ABSTRACT: Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data

PROVIDER: PRJNA1092254 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28464922.fastq.gz	Fastqsanger.gz
	SRR28464923.fastq.gz	Fastqsanger.gz
	SRR28464924.fastq.gz	Fastqsanger.gz
	SRR28464925.fastq.gz	Fastqsanger.gz
	SRR28464926.fastq.gz	Fastqsanger.gz

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Evaluation of novel computational methods that identify RNA-binding protein footprints from structural data

Project description:RNA binding proteins (RBP) play diverse roles in mRNA processing and function. However, from over 1,000 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 binding is insufficient for enabling exhaustive prediction of binding sites transcriptome-wide. In that context, the rapidly expanding collection of transcriptomic datasets that map distinct, yet intertwined post-transcriptional marks, such as RNA structure and RBP binding, presents an opportunity to integratively analyze them in order to better characterize binding. A grand challenge faced by our community is that relatively little information on the structural context of RNA-protein interactions has been gleaned from integrating such datasets, partially due to lack of suitable methods. To engage scientists from diverse backgrounds in addressing this gap, the RNA Society organized the RBP Footprint Grand Challenge⸺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 several new in vivo binding datasets generated for validation. We hope this work will inspire additional innovation in computational methods, further utilization of available data resources, and future endeavors to engage the community in collaborating towards closing other critical data analysis gaps.

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