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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Mar 31, 2026

mirMachine: A One-Stop Shop for Plant miRNA Annotation

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing

Sheng Yang¹, Li Guo¹, Fang Shao¹

¹Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Road, Nanjing, Jiangsu 211166, China.

Computational and Mathematical Methods in Medicine

|October 29, 2015

Summary

This study compares feature selection algorithms for microRNA (miRNA) sequencing data, finding that combining edgeR and DESeq is effective for large sample sizes in disease association studies.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Sequencing is crucial for identifying microRNA (miRNA) and disease associations.
Challenges include low power and reproducibility due to negative binomial distribution and high dimensionality of sequencing data.
Evaluation of statistical learning methods for this data is infrequent.

Purpose of the Study:

To compare the performance of various feature selection (FS) algorithms for analyzing miRNA sequencing data.
To evaluate FS and classification algorithms using both simulated and real-world cancer genomics data.
To propose an optimized strategy for analyzing large-scale miRNA sequencing datasets.

Main Methods:

Compared seven FS algorithms (baySeq, DESeq, edgeR, rank sum test, lasso, PSO DT, RF) via simulation under varying conditions.
Evaluated RF, logistic regression, and support vector machine on real cancer genomics data.
Utilized the Apriori algorithm to identify frequently deregulated miRNAs from The Cancer Genomics Atlas datasets.

Main Results:

Simulation results informed the performance assessment of FS algorithms.
Real data analysis provided insights into the behavior of FS and classification methods.
Identified specific miRNAs (mir-133a, mir-133b, mir-183, mir-937, mir-96) as frequently deregulated.
A strategy combining edgeR and DESeq was proposed for large sample sizes, considering computational efficiency.

Conclusions:

The study provides a comparative analysis of feature selection methods for miRNA sequencing data.
The proposed strategy of combining edgeR and DESeq offers an efficient approach for large datasets.
Findings contribute to improving the reliability and reproducibility of miRNA-disease association studies.