Sebnif: An Integrated Bioinformatics Pipeline for the Identification of Novel Large Intergenic Noncoding RNAs (lincRNAs) - Application in Human Skeletal Muscle Cells
Year: 2014
Type: article
Abstract: Ab initio assembly of transcriptome sequencing data has been widely used to identify large intergenic non-coding RNAs (lincRNAs), a novel class of gene regulators involved in many biological processes. To differentiate real lincRNA transcripts from thousands of assembly artifacts, a series of filtering steps such as filters of transcript length, expression level and coding potential, need to be applied. However, an easy-to-use and publicly available bioinformatics pipeline that integrates these filters is not yet available. Hence, we implemented sebnif, an integrative bioinformatics pipeline to facilitate the discovery of bona fide novel lincRNAs that are suitable for further functional characterization. Specifically, sebnif is the only pipeline that implements an algorithm for identifying high-quality single-exonic lincRNAs that were often omitted in many studies. To demonstrate the usage of sebnif, we applied it on a real biological RNA-seq dataset from Human Skeletal Muscle Cells (HSkMC) and built a novel lincRNA catalog containing 917 highly reliable lincRNAs. Sebnif is available at http://sunlab.lihs.cuhk.edu.hk/sebnif/.
Source: PLoS ONE
Authors: Kun Sun, Yu Zhao, Huating Wang, Hao Sun
Institutions: Chinese University of Hong Kong, Prince of Wales Hospital
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funder: Chinese University of Hong Kong
OpenAlex ID: W1987323258