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Updated: May 5, 2026

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

Scalable Approach to Evaluate Plant microRNA Trimming and Tailing from Small RNA-Seq Data.

Ileana Tossolini^1,2, Pablo Andrés Manavella^1,3,4, Agustín Lucas Arce^5,6

¹Instituto de Agrobiotecnología del Litoral (CONICET-UNL), 3000 Santa Fe, Argentina.

Methods in Molecular Biology (Clifton, N.J.)

|May 16, 2025

Summary

This summary is machine-generated.

This study presents a bioinformatics pipeline to analyze microRNA (miRNA) trimming and tailing from sequencing data. The method provides a scalable approach for evaluating these crucial miRNA modifications in plant research.

Keywords:

miRNA, Trimming, Tailing, Plants, miRNA turnover, Bioinformatics, sRNA-Seq

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Last Updated: May 5, 2026

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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Published on: September 16, 2019

Area of Science:

Molecular Biology
Bioinformatics
Plant Science

Background:

MicroRNAs (miRNAs) are key regulators of gene expression, controlling processes post-transcriptionally.
miRNA stability is influenced by modifications like 2'-O-methylation, trimming, and tailing.
Understanding miRNA turnover is vital for deciphering gene regulation.

Purpose of the Study:

To develop and describe a bioinformatics pipeline for analyzing miRNA trimming and tailing.
To provide a method for evaluating miRNA modifications from small RNA sequencing (sRNA-Seq) data.
To enable researchers to process raw sequencing data into meaningful results.

Main Methods:

The pipeline processes raw sRNA-Seq reads, including 3' adaptor removal and quality filtering.
Reads are aligned to a masked reference genome to identify and quantify miRNA modifications.
An index for trimming and tailing is calculated, with graphical and statistical evaluation.

Main Results:

The developed pipeline effectively assesses the degree of miRNA trimming and tailing.
It allows for the analysis of a large number of samples from public repositories.
The method generates graphical and statistical representations of miRNA modification data.

Conclusions:

This scalable bioinformatics pipeline facilitates the analysis of miRNA modifications from sRNA-Seq data.
Researchers can utilize this method to gain insights into miRNA turnover and regulation.
The pipeline transforms raw sequencing data into interpretable results for plant biology studies.