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Prober: A general toolkit for analyzing sequencing-based 'toeprinting' assays

By Bo Li, Akshay Tambe, Sharon Aviran, Lior Pachter

Posted 10 Jul 2016
bioRxiv DOI: 10.1101/063107 (published DOI: 10.1016/j.cels.2017.04.007)

A number of high-throughput transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and post-transcriptional modifications. Although these assays survey a diverse set of 'epitranscriptomic' marks, they share methodological similarities and as such their interpretation is predicated on addressing similar computational challenges. Among these, a key question is how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. In this paper, we propose PROBer, the first rigorous statistical model to handle these challenges for a general set of sequencing-based 'toeprinting' assays.

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