NormQ: RNASeq normalization based on RT-qPCR derived size factors

The merit of RNASeq data relies heavily on correct normalization. However, most methods assume that the majority of transcripts show no differential expression between conditions. This assumption may not always be correct, especially when one condition results in overexpression. We present a new met...

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Detalles Bibliográficos
Autores principales: Naraine, Ravindra, Abaffy, Pavel, Sidova, Monika, Tomankova, Silvie, Pocherniaieva, Kseniia, Smolik, Ondrej, Kubista, Mikael, Psenicka, Martin, Sindelka, Radek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264052/
https://www.ncbi.nlm.nih.gov/pubmed/32514328
http://dx.doi.org/10.1016/j.csbj.2020.05.010
Descripción
Sumario:The merit of RNASeq data relies heavily on correct normalization. However, most methods assume that the majority of transcripts show no differential expression between conditions. This assumption may not always be correct, especially when one condition results in overexpression. We present a new method (NormQ) to normalize the RNASeq library size, using the relative proportion observed from RT-qPCR of selected marker genes. The method was compared against the popular median-of-ratios method, using simulated and real-datasets. NormQ produced more matches to differentially expressed genes in the simulated dataset and more distribution profile matches for both simulated and real datasets.

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