Abstract：
:A number of sequencing-based transcriptase drop-off assays have recently been developed to probe post-transcriptional dynamics of RNA-protein interaction, RNA structure, and RNA modification. Although these assays survey a diverse set of epitranscriptomic marks, we use the term toeprinting assays since they share methodological similarities. Their interpretation is predicated on addressing a similar computational challenge: how to learn isoform-specific chemical modification profiles in the face of complex read multi-mapping. We introduce PROBer, a statistical model and associated software, that addresses this challenge for the analysis of toeprinting assays. PROBer takes sequencing data as input and outputs estimated transcript abundances and isoform-specific modification profiles. Results on both simulated and biological data demonstrate that PROBer significantly outperforms individual methods tailored for specific toeprinting assays. Since the space of toeprinting assays is ever expanding and these assays are likely to be performed and analyzed together, we believe PROBer's unified data analysis solution will be valuable to the RNA community.
journal_name
Cell Systjournal_title
Cell systemsauthors
Li B,Tambe A,Aviran S,Pachter Ldoi
10.1016/j.cels.2017.04.007subject
Has Abstractpub_date
2017-05-24 00:00:00pages
568-574.e7issue
5eissn
2405-4712issn
2405-4720pii
S2405-4712(17)30139-4journal_volume
4pub_type
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