Blockclust: efficient clustering and classification of non-coding rnas from short Read RNA-seq profiles

Abstract

Sequence and secondary structure analysis can be used to assign putative functions to non-coding RNAs. However sequence information is changed by post-transcriptional modifications and secondary structure is only a proxy for the true 3D conformation of the RNA polymer. In order to tackle these issues we can extract a different type of description using the pattern of processing that can be observed through the traces left in small RNA-seq reads data. To obtain an efficient and scalable procedure, we propose to encode expression profiles in discrete structures, and process them using fast graph-kernel techniques.