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RNA-seq

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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.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Related Experiment Video

Updated: Apr 7, 2026

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions.

Mostafa Rezapour¹, Stephanie V Trefry^2,3, Lorreta A Opoku³

¹Wake Forest Institute for Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.

Computational and Structural Biotechnology Journal

|April 6, 2026

Summary

This summary is machine-generated.

Feature selection is crucial for artificial intelligence (AI) models analyzing high-dimensional bulk RNA sequencing (RNA-seq) data. A statistically guided method, GLMQL-MAS, effectively reduces gene numbers for robust and reproducible transcriptomic modeling.

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Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Bulk RNA sequencing (RNA-seq) generates high-dimensional data, posing challenges for artificial intelligence (AI) model development due to more features than samples.
This imbalance increases the risk of overfitting and poor generalization in AI models, necessitating effective feature selection strategies.

Purpose of the Study:

To review the constraints posed by high dimensionality and limited sample size in AI-based bulk RNA-seq data analysis.
To survey feature selection strategies that prevent information leakage and improve model generalization.

Main Methods:

Focus on statistically guided, training-only feature selection frameworks.
Detailed examination of generalized linear models with quasi-likelihood F tests and magnitude-altitude scoring (GLMQL-MAS).

Main Results:

GLMQL-MAS yields compact and interpretable gene sets from bulk RNA-seq data.
These gene sets enhance robustness, reproducibility, and cross-dataset generalization in AI models.

Conclusions:

Effective feature selection is essential for reliable AI-based analysis of high-dimensional transcriptomic data.
GLMQL-MAS offers a robust approach for gene selection in bulk RNA-seq studies, particularly in viral infection research.