The value of genotype-specific reference for transcriptome analyses in barley

Guo, Wenbin; Coulter, Max; Waugh, Robbie; Zhang, Runxuan

doi:10.26508/lsa.202101255

Cited by 5 publications

(3 citation statements)

References 77 publications

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“…This can be overcome by short-read sequencing data with an advantage of read depth, which permits the generation of high-confidence SJs ( Au et al, 2012 ; Kovaka et al, 2019 ; Coulter et al, 2022 ; Zhang et al, 2022 ). A recent study identified some flaws while creating RTD with a common reference genome sequence for the different genotype transcriptome sequencing data within a species ( Guo et al, 2022 ). Rice has different genotypes with high-value traits and IndicaRTD can be improved by using the genotype-specific reference genome ( Das et al, 2013 ; Roy et al, 2015 ; Shrestha et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

Srikakulam

Sridevi²,

Pandi³

2022

Front. Genet.

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The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.

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Section: Discussionmentioning

confidence: 99%

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

Srikakulam

Sridevi²,

Pandi³

2022

Front. Genet.

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“…However, there are limitations with Illumina short reads. The transcript assemblies from short reads are often inaccurate and produce large numbers of mis-assembled transcripts and missing real transcripts [40,41]. Also, research has shown that it is difficult to reconstruct splice isoforms and quantify differential expression of isoforms using short reads [42,43], which is necessary to determine the nature of the encoded protein and in assessing a splice variant's role [32,43].…”

Section: Bioinformatic Tools Software and Computational Methods To Qu...mentioning

confidence: 99%

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs

Dwivedi,

Quiroz,

Reddy

et al. 2023

IJMS

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Alternative splicing (AS) is a gene regulatory mechanism modulating gene expression in multiple ways. AS is prevalent in all eukaryotes including plants. AS generates two or more mRNAs from the precursor mRNA (pre-mRNA) to regulate transcriptome complexity and proteome diversity. Advances in next-generation sequencing, omics technology, bioinformatics tools, and computational methods provide new opportunities to quantify and visualize AS-based quantitative trait variation associated with plant growth, development, reproduction, and stress tolerance. Domestication, polyploidization, and environmental perturbation may evolve novel splicing variants associated with agronomically beneficial traits. To date, pre-mRNAs from many genes are spliced into multiple transcripts that cause phenotypic variation for complex traits, both in model plant Arabidopsis and field crops. Cataloguing and exploiting such variation may provide new paths to enhance climate resilience, resource-use efficiency, productivity, and nutritional quality of staple food crops. This review provides insights into AS variation alongside a gene expression analysis to select for novel phenotypic diversity for use in breeding programs. AS contributes to heterosis, enhances plant symbiosis (mycorrhiza and rhizobium), and provides a mechanistic link between the core clock genes and diverse environmental clues.

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“…We identified 'tissue' as the main driver of transcript diversity (Figure 1b). To avoid genotypic bias introduced by using a single reference for mapping RNAseq reads 12 , we produced genotype specific reference transcript datasets (GsRTDs) for each of the twenty individuals. GsRTDs assemble a broader range of transcripts than a single reference, producing more comprehensive and accurate transcriptomes that improve transcript quantification and expression analyses [13][14][15][16] .…”

Section: A Barley Pan-transcriptomementioning

confidence: 99%

A barley pan-transcriptome reveals layers of genotype-dependent transcriptional complexity

Waugh,

Guo,

Schreiber

et al. 2024

Preprint

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A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species, revealing an assortment of functions that drive biological outcomes. We developed a barley pan-transcriptome using twenty inbred genotypes representing domesticated* barley diversity by generating and analysing extensive short- and long-read RNA sequencing datasets from multiple tissues. To overcome single reference bias and facilitate downstream analyses we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a single pan-RTD. Categorising transcripts based upon presence or absence across genotypes defined them as core (expressed in all), shell (absent in one or more) or cloud (expressed in only one). Focusing on the core we observed significant transcript abundance variation among tissues and between genotypes. We show that drivers of transcript abundance variation in this category include RNA processing, gene copy number, large structural rearrangements and degree of conservation of promotor motifs. We reveal conserved patterns of co-expression module-tissue correlations encompassing distinct biological functions, as well as frequent functional diversification. We complement the pan-transcriptome by integrating extensive and diverse replicated public RNA-seq datasets from the reference cultivar (cv.) Morex into a comprehensive gene-expression atlas

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The value of genotype-specific reference for transcriptome analyses in barley

Cited by 5 publications

References 77 publications

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

High-quality reference transcriptome construction improves RNA-seq quantification in Oryza sativa indica

Alternative Splicing Variation: Accessing and Exploiting in Crop Improvement Programs

A barley pan-transcriptome reveals layers of genotype-dependent transcriptional complexity

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