CoRAL: Predicting Non-coding RNAs from Small RNA-sequencing Data

CoRAL: Predicting Non-coding RNAs from Small RNA-sequencing Data

h/t: @genetics_blog

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities.

These guys deserve a kill ENCODE T-shirt from Dan Graur, but otherwise the paper is useful.

We first designed algorithms to generate several types of features from smRNA-seq data based on read length distribution, strand specificity and the secondary structure of the transcript for transcribed genomic regions. We then applied a multi-class classification algorithm with feature selection and cross-validation schemes included to train classifiers among a collection of known RNA functional classes including lincRNAs, miRNAs, small cytoplasmic RNA (scRNAs), C/D box snoRNAs, snRNAs and transposon-derived RNAs. For each RNA class, we identified the most informative features that might be associated with the molecular mechanisms and metabolic processes of the functional classes. Trained models, informative features and annotation results have been validated using (i) external datasets, (ii) SAVoR, a visualization tool for RNA structures (14), and (iii) curation of the primary literature.

The analysis workflow for differentiating between six different classes of ncRNAs in smRNA-seq data sets.

Written by M. //