Lorena Pantano1, Xavier Estivill, Eulalia Martí. 1. Genetic Causes of Disease, Genes and Disease Programme, Centre for Genomic Regulation (CRG) and UPF, Barcelona, Spain.
Abstract
MOTIVATION: Recent progress in high-throughput sequencing technologies has largely contributed to reveal a highly complex landscape of small non-coding RNAs (sRNAs), including novel non-canonical sRNAs derived from long non-coding RNA, repeated elements, transcription start sites and splicing site regions among others. The published frameworks for sRNA data analysis are focused on miRNA detection and prediction, ignoring further information in the dataset. As a consequence, tools for the identification and classification of the sRNAs not belonging to miRNA family are currently lacking. RESULTS: Here, we present, SeqCluster, an extension of the currently available SeqBuster tool to identify and analyze at different levels the sRNAs not annotated or predicted as miRNAs. This new module deals with sequences mapping onto multiple locations and permits a highly versatile and user-friendly interaction with the data in order to easily classify sRNA sequences with a putative functional importance. We were able to detect all known classes of sRNAs described to date using SeqCluster with different sRNA datasets.
MOTIVATION: Recent progress in high-throughput sequencing technologies has largely contributed to reveal a highly complex landscape of small non-coding RNAs (sRNAs), including novel non-canonical sRNAs derived from long non-coding RNA, repeated elements, transcription start sites and splicing site regions among others. The published frameworks for sRNA data analysis are focused on miRNA detection and prediction, ignoring further information in the dataset. As a consequence, tools for the identification and classification of the sRNAs not belonging to miRNA family are currently lacking. RESULTS: Here, we present, SeqCluster, an extension of the currently available SeqBuster tool to identify and analyze at different levels the sRNAs not annotated or predicted as miRNAs. This new module deals with sequences mapping onto multiple locations and permits a highly versatile and user-friendly interaction with the data in order to easily classify sRNA sequences with a putative functional importance. We were able to detect all known classes of sRNAs described to date using SeqCluster with different sRNA datasets.
Authors: Federica Bernardini; Roberto Galizi; Mariana Wunderlich; Chrysanthi Taxiarchi; Nace Kranjc; Kyros Kyrou; Andrew Hammond; Tony Nolan; Mara N K Lawniczak; Philippos Aris Papathanos; Andrea Crisanti; Nikolai Windbichler Journal: Genetics Date: 2017-08-31 Impact factor: 4.562