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Updated: Oct 3, 2025

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Published on: August 21, 2019

PRECISION.seq: An R Package for Benchmarking Depth Normalization in microRNA Sequencing.

Jian Zou¹, Yannick Düren², Li-Xuan Qin³

¹Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States.

Frontiers in Genetics

|February 14, 2022

Summary

This summary is machine-generated.

PRECISION.seq is a new R package for evaluating microRNA sequencing (miRNA-seq) data normalization. It offers tools and datasets for objective performance assessment of normalization methods, aiding researchers in selecting the best approaches.

Keywords:

benchmarking microRNA normalization sequencing software

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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate normalization is crucial for microRNA sequencing (miRNA-seq) data analysis.
Existing methods for assessing normalization performance lack comprehensive evaluation tools.
Differential expression analysis in miRNA-seq is sensitive to normalization choices.

Purpose of the Study:

To introduce PRECISION.seq, a novel R package for assessing depth normalization performance in miRNA-seq data.
To provide a standardized framework for objective and systematic evaluation of normalization methods.
To enable comparison of user-selected normalization methods against established benchmarks.

Main Methods:

Development of an R package, PRECISION.seq.
Inclusion of paired miRNA-seq datasets from tumor samples.
Generation of simulated datasets with varying differential expression patterns.
Provision of numerical and graphical tools for performance assessment.

Main Results:

PRECISION.seq facilitates the evaluation of normalization method performance.
The package allows comparison against nine pre-included normalization methods.
It utilizes realistically distributed and robustly benchmarked data.
Enables evaluation across diverse differential expression patterns.

Conclusions:

PRECISION.seq is the first tool of its kind for evaluating miRNA-seq normalization.
It promotes objective and systematic assessment of normalization strategies.
Aids researchers in selecting optimal normalization methods for miRNA-seq data analysis.