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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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A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks.

Hatice Büşra Lüleci¹, Dilara Uzuner¹, Müberra Fatma Cesur¹

¹Department of Bioengineering, Gebze Technical University, Kocaeli, 41400, Turkey.

NPJ Systems Biology and Applications

|October 25, 2024

Summary

Choosing the right RNA-seq normalization method is crucial for creating accurate condition-specific metabolic models using algorithms like iMAT and INIT. Between-sample normalization methods (RLE, TMM, GeTMM) offer better predictive accuracy for disease-associated genes.

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Area of Science:

Computational biology
Systems biology
Bioinformatics

Background:

Genome-scale metabolic models (GEMs) require transcriptomic data for condition-specific personalization.
Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are key algorithms for this process.
RNA-sequencing (RNA-seq) data normalization impacts model accuracy.

Purpose of the Study:

To benchmark RNA-seq normalization methods for iMAT and INIT.
To evaluate the impact of covariates (age, gender) on personalized metabolic model generation.
To compare between-sample (TMM, GeTMM, RLE) and within-sample (TPM, FPKM) normalization strategies.

Main Methods:

Applied five normalization methods (TPM, FPKM, TMM, GeTMM, RLE) and their covariate-adjusted versions to RNA-seq data.
Utilized iMAT and INIT algorithms to generate personalized metabolic models from normalized data.
Validated model performance using RNA-seq data from Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients.

Main Results:

Between-sample normalization methods (RLE, TMM, GeTMM) produced models with lower reaction variability compared to within-sample methods.
RLE, TMM, and GeTMM achieved higher accuracy in identifying disease-associated genes (~0.80 for AD, ~0.67 for LUAD).
Covariate adjustment generally improved prediction accuracies across all normalization methods.

Conclusions:

Between-sample normalization methods are preferred for generating condition-specific GEMs with iMAT and INIT, reducing false positives.
Normalization choice significantly impacts the accuracy of personalized metabolic models.
Covariate adjustment is a valuable step for enhancing the predictive power of transcriptomic data in metabolic modeling.