Preprint Review Version 1 This version is not peer-reviewed

Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge - A Transcriptomics Perspective

Version 1 : Received: 3 July 2024 / Approved: 3 July 2024 / Online: 4 July 2024 (08:58:08 CEST)

How to cite: Aufiero, G.; Fruggiero, C.; D’Angelo, D.; D’Agostino, N. Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge - A Transcriptomics Perspective. Preprints 2024, 2024070415. https://doi.org/10.20944/preprints202407.0415.v1 Aufiero, G.; Fruggiero, C.; D’Angelo, D.; D’Agostino, N. Homoeologs in Allopolyploids: Navigating Redundancy as Both an Evolutionary Opportunity and a Technical Challenge - A Transcriptomics Perspective. Preprints 2024, 2024070415. https://doi.org/10.20944/preprints202407.0415.v1

Abstract

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides valuable insights into the fate of duplicated genes, evolutionary novelties, and environmental adaptations exhibited by allopolyploid plants. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid expression, with an emphasis on the critical steps of read mapping and normalization in differential gene expression analysis.

Keywords

Polyploids; homeologs; allopolyploidization; RNA-sequencing; gene expression; expression level dominance; homoeolog expression bias; additivity; bioinformatics

Subject

Biology and Life Sciences, Other

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