Small RNA-seq of undiseased human brain

ABSTRACT: The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to c

ORGANISM(S): Homo sapiens

SUBMITTER: Paul Ryvkin 

PROVIDER: E-GEOD-43335 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress