miND (miRNA NGS Discovery pipeline): a small RNA-seq analysis pipeline and report generator for microRNA biomarker discovery studies

Abstract

In contrast to traditional methods like real-time polymerase chain reaction, next-generation sequencing (NGS), and especially small RNA-seq, enables the untargeted investigation of the whole small RNAome, including microRNAs (miRNAs) but also a multitude of other RNA species. With the promising application of small RNAs as biofluid-based biomarkers, small RNA-seq is the method of choice for an initial discovery study. However, the presentation of specific quality aspects of small RNA-seq data varies significantly between laboratories and is lacking a common (minimal) standard. The miRNA NGS Discovery pipeline (miND) aims to bridge the gap between wet lab scientist and bioinformatics with an easy to setup configuration sheet and an automatically generated comprehensive report that contains all essential qualitative and quantitative results that should be reported. Besides the standard steps like preprocessing, mapping, visualization, and quantification of reads, the pipeline also incorporates differential expression analysis when given the appropriate information regarding sample groups. Although miND has a focus on miRNAs, other RNA species like tRNAs, piRNA, snRNA, or snoRNA are included and mapping statistics are available for further analysis. miND has been developed and tested on a multitude of data sets with various RNA sources (tissue, plasma, extracellular vesicles, urine, etc.) and different species. miND is a Snakemake based pipeline and thus incorporates all advantages using a flexible workflow management system. Reference databases are downloaded, prepared and built with an included (but separate) workflow and thus can easily be updated to the most recent version but also stored for reproducibility. In conclusion, the miND pipeline aims to streamline the bioinformatics processing of small RNA-seq data by standardizing the processing from raw data to a final, comprehensive and reproducible report.

Small non-coding RNA landscape of extracellular vesicles from a post-traumatic model of equine osteoarthritis

“…Data were analysed using the miND pipeline ( 40 ). The overall quality of the next-generation sequencing data was evaluated automatically and manually with fastQC v0.11.9 ( 41 ) and multiQC v1.10 ( 42 ).…”

Investigation of MicroRNA Biomarkers in Equine Distal Interphalangeal Joint Osteoarthritis

“…However, the analysis revealed no outlying samples and largely overlapping miRNA profiles between the mild and severe OA groups ( Figure 1 ). Low-abundant miRNAs were filtered from the dataset prior to differential expression analysis using the independent filtering procedure implemented in DeSeq2 [ 30 ]. Three miRNAs; miR-25, miR-16 and miR-92a, were found to be significantly (FDR < 0.1) differentially expressed (DE) and downregulated in the severe OA synovial fluid samples compared to the mild synovial fluid samples, appreciated in the volcano plot in Figure 2 , which shows FDR (false discovery rate) and logFC (log fold change) for this comparison.…”

The plasma miRNome and venous thromboembolism in high‐grade glioma: miRNA Sequencing of a nested case–control cohort

“…NGS data processing and data analysis was performed using the miRNA NGS Discovery pipeline (miND) software, 19 which annotates microRNAs and other non‐coding RNAs from small RNA‐seq data, and can perform group‐ or pair‐wise statistical analyses based on EdgeR. p ‐Value from EdgeR based differential expression analysis were adjusted for multiple testing using Benjamini‐Hochberg false discovery rate.…”