Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.)

Onions (Allium cepa L.) are one of the most consumed vegetable crops worldwide and are damaged by several fungal diseases in the field or during storage. Gray mold disease caused by the necrotrophic pathogens Botrytis cinerea and Botrytis squamosa is a disease that reduces the productivity and storage life in onions. However, it is difficult to control gray mold disease in onions by using physical and chemical methods. Breeding resistant onions against gray mold disease can reduce the damage caused by pathogens, reduce the labor required for control, and reduce environmental pollution caused by fungicides. However, onions have a large genome size (16Gb), making them difficult to analyze, and have a biennial cycle, resulting in a very long breeding period. Therefore, in this study, markers were developed to shorten the onion breeding period. First, random amplified polymorphic DNA (RAPD) was performed to confirm the genetic relationship between the gray mold disease-resistant and -susceptible lines through a dendrogram. In addition, the sequence characterized amplified region (SCAR)-OPAN1 marker to select resistant lines was developed using a polymorphic RAPD fragment. Second, the RNA-seq of the gray mold-resistant and -susceptible onion lines were analyzed using NGS technology. Using the RNA-seq results and DEG and GO analyses were performed, and the variants, such as SNPs and indels, were analyzed to develop a selectable marker for the resistant line. This study developed the SNP-3 HRM marker for selecting gray mold disease-resistant lines by using the SNPs present in the aldo-keto reductase (AKR) gene with high expression levels in these lines. The SCAR-OPAN1 and SNP-3 HRM markers developed in this study could be used to select gray mold disease-resistant onions in breeding programs to reduce the damage caused by gray mold disease.

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Identifying stably expressed genes from multiple RNA-Seq data sets

PeerJ ◽

10.7717/peerj.2791 ◽

2016 ◽

Vol 4 ◽

pp. e2791 ◽

Cited By ~ 6

Author(s):

Bin Zhuo ◽

Sarah Emerson ◽

Jeff H. Chang ◽

Yanming Di

Keyword(s):

Differential Expression ◽

Reference Gene ◽

Biological Samples ◽

Differential Expression Analysis ◽

Variance Component Analysis ◽

Linear Mixed Effect Model ◽

Rna Seq ◽

Mixed Effect ◽

Gene Set ◽

Common Reference

We examined RNA-Seq data on 211 biological samples from 24 different Arabidopsis experiments carried out by different labs. We grouped the samples according to tissue types, and in each of the groups, we identified genes that are stably expressed across biological samples, treatment conditions, and experiments. We fit a Poisson log-linear mixed-effect model to the read counts for each gene and decomposed the total variance into between-sample, between-treatment and between-experiment variance components. Identifying stably expressed genes is useful for count normalization and differential expression analysis. The variance component analysis that we explore here is a first step towards understanding the sources and nature of the RNA-Seq count variation. When using a numerical measure to identify stably expressed genes, the outcome depends on multiple factors: the background sample set and the reference gene set used for count normalization, the technology used for measuring gene expression, and the specific numerical stability measure used. Since differential expression (DE) is measured by relative frequencies, we argue that DE is a relative concept. We advocate using an explicit reference gene set for count normalization to improve interpretability of DE results, and recommend using a common reference gene set when analyzing multiple RNA-Seq experiments to avoid potential inconsistent conclusions.

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