Several online and local tools have been developed to analyze microRNA-sequencing (miRNA-Seq) data, but usually they are limited by many factors including: inaccurate processing, lack of optimal parameterization, outdated references plus annotations, restrictions in uploading large datasets, and shortage of biological inferences. In this work, we have developed a fully customized bioinformatics analysis pipeline (Color and Base-Space miRNA-Seq – CBS-miRSeq) for the seamless processing of short-reads miRNA-Seq data. The pipeline has been designed using Bash, Perl, and R scripts. CBS-miRSeq includes modules for read pre- and post-processing (quality assessment, filtering, adapter trimming and mapping) and different types of downstream analyses (identification of miRNA variants (isomiRs), novel miRNA prediction, miRNA:mRNA interaction target prediction, robust differential miRNA analysis, and target gene functional analysis). In this manuscript, we show that re-analysis of two published datasets using the CBS-miRSeq pipeline leads to better performance and efficiency in terms of their pipelines set and biomarker discovery between two biological conditions.
- Bioinformatics pipeline
- Gene expression profiling
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics