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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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Updated: Jun 8, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification.

Tashifa Imtiaz¹, Jina Nanayakkara¹, Alexis Fang¹

¹Laboratory of Translational RNA Biology, Department of Pathology and Molecular Medicine, Queen's University, 88 Stuart St, Kingston, ON K7L 3N6, Canada.

|November 4, 2024

Summary

This summary is machine-generated.

This study introduces a machine learning method to classify samples using microRNA (miRNA) expression data. The approach effectively distinguishes between biological and clinical groups, overcoming challenges with high-dimensional sequencing data.

Keywords:

Bioinformatics Gene Expression RNAseq Sequence Analysis Sequencing

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Last Updated: Jun 8, 2025

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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Published on: September 28, 2017

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

RNA expression data, particularly microRNA (miRNA), offers potential for biological insights and clinical group discrimination.
High-dimensional and noisy sequencing data present significant challenges in identifying informative differences between sample groups.
Machine learning offers a promising avenue for analyzing complex biological datasets.

Purpose of the Study:

To develop and present a machine learning protocol for hierarchical sample discrimination and classification using high-dimensional miRNA expression data.
To provide a robust method for identifying subtle yet significant differences in miRNA profiles between sample cohorts.
To offer an open-source solution for researchers working with complex genomic data.

Main Methods:

The protocol involves several key steps: data preprocessing, unsupervised learning for pattern discovery, rigorous feature selection, and machine-learning-based hierarchical classification.
Utilized high-dimensional miRNA expression data as input for the classification models.
Developed and implemented a hierarchical classification strategy to refine sample discrimination.

Main Results:

The machine learning approach successfully achieved hierarchical discrimination and classification of samples based on miRNA expression data.
Demonstrated the ability to identify informative differences within complex, high-dimensional datasets.
The developed protocol provides a framework for robust sample classification in biological and clinical research.

Conclusions:

Machine learning provides an effective strategy for overcoming the challenges associated with classifying samples using high-dimensional miRNA expression data.
The developed protocol enables accurate biological insights and distinction between clinical groups.
The availability of open-source MATLAB code facilitates the application and further development of this methodology.