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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Getting the most out of RNA-seq data analysis.

Tsung Fei Khang¹, Ching Yee Lau²

¹Institute of Mathematical Sciences, University of Malaya , Kuala Lumpur , Malaysia.

|November 6, 2015

Summary

For mild biological effects in RNA-seq, focus on validating gene candidates with at least three replicates using high positive predictive value (PPV) methods. Stronger effects allow for more reliable differential gene expression analysis even with fewer replicates.

Keywords:

Biological effect size Biological replicate size Differential gene expression analysis RNA-seq

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Area of Science:

Transcriptomics and Bioinformatics
Gene Expression Analysis
RNA Sequencing (RNA-seq)

Background:

Identifying differentially expressed genes across phenotypes is crucial for transcriptome projects.
RNA-seq analysis requires understanding variations from replicate size, effect size, and analysis methods.
Practical demonstration of these interactions in real RNA-seq data is of interest to biologists.

Purpose of the Study:

To investigate the interaction between replicate size, biological effect size, and differential gene expression calling methods.
To evaluate the performance of various methods under different experimental conditions using real RNA-seq data.
To provide practical guidance for biologists designing RNA-seq experiments.

Main Methods:

Utilized two large public RNA-seq datasets with strong and mild biological effect sizes.
Simulated various replicate size scenarios for each dataset.
Tested the performance of commonly used differential gene expression calling methods (e.g., NOISeq, GFOLD, DESeq2, edgeR).

Main Results:

For mild effect sizes, experimental validation is recommended; at least triplicates and high PPV methods (NOISeq, GFOLD) are necessary.
For strong effect sizes, unreplicated experiments with NOISeq, ASC, and GFOLD yielded 30-50% PPV.
Increasing replicates to three or more significantly improved PPV across methods, with GFOLD, DESeq2, NOISeq, and edgeR showing substantial gains.

Conclusions:

Weak biological effect sizes preclude systems-level analysis in unreplicated RNA-seq experiments.
NOISeq or GFOLD can identify potential gene candidates for validation with triplicates when effect sizes are weak.
For strong effect sizes, NOISeq and GFOLD are suitable for unreplicated validation; DESeq2 and edgeR are recommended for systems-level analysis with sufficient replicates.