Site identification in high-throughput RNA-protein interaction data

Philip J. Uren, Emad Bahrami-Samani, Suzanne C. Burns, Mei Qiao, Fedor V. Karginov, Emily Hodges, Gregory J. Hannon, Jeremy R. Sanford, Luiz O.F. Penalva, Andrew D. Smith

Research output: Contribution to journalArticlepeer-review

148 Scopus citations

Abstract

Motivation: Post-transcriptional and co-transcriptional regulation is a crucial link between genotype and phenotype. The central players are the RNA-binding proteins, and experimental technologies [such as cross-linking with immunoprecipitation-(CLIP-) and RIP-seq] for probing their activities have advanced rapidly over the course of the past decade. Statistically robust, flexible computational methods for binding site identification from high-throughput immunoprecipitation assays are largely lacking however.Results: We introduce a method for site identification which provides four key advantages over previous methods: (i) it can be applied on all variations of CLIP and RIP-seq technologies, (ii) it accurately models the underlying read-count distributions, (iii) it allows external covariates, such as transcript abundance (which we demonstrate is highly correlated with read count) to inform the site identification process and (iv) it allows for direct comparison of site usage across cell types or conditions.

Original languageEnglish (US)
Pages (from-to)3013-3020
Number of pages8
JournalBioinformatics
Volume28
Issue number23
DOIs
StatePublished - Dec 2012

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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