iMir

iMir performs automated analysis of small RNA-Seq data to identify and quantify microRNAs (miRNAs), isomiRs, and piwi-interacting RNAs (piRNAs), and to predict their mRNA targets for differential expression and regulatory studies using next-generation sequencing data.

Key Features:

• Automated Workflow: Integrates multiple analysis steps into a fully automated pipeline to process large small RNA-Seq datasets.
• Adapter Trimming and Quality Filtering: Includes modules for adapter removal and quality filtering to ensure high-quality reads for downstream analysis.
• Differential Expression Analysis: Detects and quantifies both known and novel non-coding RNAs, enabling differential expression comparisons across conditions.
• Biological Target Prediction: Incorporates methods for predicting putative mRNA targets of differentially expressed miRNAs.
• Statistical Rigor: Applies diverse statistical approaches tailored for deep-sequencing data to support robust analyses.
• Flexibility and Customization: Allows selection of preferred combinations of analytical steps to suit specific research requirements.

Scientific Applications:

• miRNA profiling in cancer cells: Applied to human breast cancer MCF-7 cells under different growth conditions to identify and quantify miRNAs.
• Novel miRNA and isomiR discovery: Enabled detection and differential expression analysis of approximately 450 miRNAs, including novel miRNAs and isomiRs.
• piRNA detection: Detected around 70 piwi-interacting RNAs (piRNAs) and identified piRNAs showing differential expression between proliferating and growth-arrested cells.
• mRNA target identification: Facilitated the identification of putative mRNA targets for differentially expressed miRNAs to infer regulatory relationships.

Methodology:

Automated integration of open-source modules performing adapter trimming, quality filtering, detection of known and novel non-coding RNAs (miRNAs, isomiRs, piRNAs), differential expression analysis, and biological target prediction using statistical approaches for small RNA-Seq data generated by next-generation sequencing technologies.