scholarly journals The value of genotype-specific reference for transcriptome analyses

2021 ◽  
Author(s):  
Wenbin Guo ◽  
Max Coulter ◽  
Robbie Waugh ◽  
Runxuan Zhang
Keyword(s):  
Alternative Splicing ◽  
Reference Genome ◽  
Transcriptome Assembly ◽  
Specific Reference ◽  
Rna Seq ◽  
High Quality ◽  
Common Reference ◽  
Transcript Quantification ◽  
Gene Level ◽  
Reference Transcript

High quality transcriptome assembly using short reads from RNA-seq data still heavily relies upon reference-based approaches, of which the primary step is to align RNA-seq reads to a single reference genome of haploid sequence. However, it is increasingly apparent that while different genotypes within a species share core genes, they also contain variable numbers of specific genes that are only present a subset of individuals. Using a common reference may thus lead to a loss of genotype-specific information in the assembled transcript dataset and the generation of erroneous, incomplete or misleading transcriptomics analysis results. With the recent development of pan-genome information in many species, it is important that we understand the limitations of single genotype references for transcriptomics analysis. In this study, we quantitively evaluated the advantages of using genotype-specific reference genomes for transcriptome assembly and analysis using cultivated barley as a model. We mapped barley cultivar Barke RNA-seq reads to the Barke genome and to the cultivar Morex genome (common barley genome reference) to construct a genotype specific Reference Transcript Dataset (sRTD) and a common Reference Transcript Datasets (cRTD), respectively. We compared the two RTDs according to their transcript diversity, transcript sequence and structure similarity and the accuracy they provided for transcript quantification and differential expression analysis. Our evaluation shows that the sRTD has a significantly higher diversity of transcripts and alternative splicing events. Despite using a high-quality reference genome for assembly of the cRTD, we miss ca. 40% transcripts present in the sRTD and cRTD only has ca. 70% true assemblies. We found that the sRTD is more accurate for transcript quantification as well as differential expression and differential alternative splicing analysis. However, gene level quantification and comparative expression analysis are less affected by the source RTD, which indicates that analysing transcriptomic data at the gene level may be a reasonable compromise when a high-quality genotype-specific reference is not available.

scholarly journals BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

2019 ◽  
Author(s):  
Paulo Rapazote-Flores ◽  
Micha Bayer ◽  
Linda Milne ◽  
Claus-Dieter Mayer ◽  
John Fuller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Genome ◽  
Splice Junction ◽  
Rna Seq ◽  
Rt Pcr ◽  
High Quality ◽  
Transcript Quantification ◽  
Reference Transcript ◽  
Alternatively Spliced ◽  
Comprehensive Reference

AbstractBackgroundTime consuming computational assembly and quantification of gene expression and splicing analysis from RNA-seq data vary considerably. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants.ResultsA high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al., 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al., 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5’ and 3’ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2,791 differentially alternatively spliced genes and 2,768 transcripts with differential transcript usage.ConclusionA high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

scholarly journals BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Paulo Rapazote-Flores ◽  
Micha Bayer ◽  
Linda Milne ◽  
Claus-Dieter Mayer ◽  
John Fuller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Splice Junction ◽  
Rna Seq ◽  
Rt Pcr ◽  
High Quality ◽  
Transcript Quantification ◽  
Reference Transcript ◽  
Alternatively Spliced ◽  
Time Required ◽  
Comprehensive Reference

Abstract Background The time required to analyse RNA-seq data varies considerably, due to discrete steps for computational assembly, quantification of gene expression and splicing analysis. Recent fast non-alignment tools such as Kallisto and Salmon overcome these problems, but these tools require a high quality, comprehensive reference transcripts dataset (RTD), which are rarely available in plants. Results A high-quality, non-redundant barley gene RTD and database (Barley Reference Transcripts – BaRTv1.0) has been generated. BaRTv1.0, was constructed from a range of tissues, cultivars and abiotic treatments and transcripts assembled and aligned to the barley cv. Morex reference genome (Mascher et al. Nature; 544: 427–433, 2017). Full-length cDNAs from the barley variety Haruna nijo (Matsumoto et al. Plant Physiol; 156: 20–28, 2011) determined transcript coverage, and high-resolution RT-PCR validated alternatively spliced (AS) transcripts of 86 genes in five different organs and tissue. These methods were used as benchmarks to select an optimal barley RTD. BaRTv1.0-Quantification of Alternatively Spliced Isoforms (QUASI) was also made to overcome inaccurate quantification due to variation in 5′ and 3′ UTR ends of transcripts. BaRTv1.0-QUASI was used for accurate transcript quantification of RNA-seq data of five barley organs/tissues. This analysis identified 20,972 significant differentially expressed genes, 2791 differentially alternatively spliced genes and 2768 transcripts with differential transcript usage. Conclusion A high confidence barley reference transcript dataset consisting of 60,444 genes with 177,240 transcripts has been generated. Compared to current barley transcripts, BaRTv1.0 transcripts are generally longer, have less fragmentation and improved gene models that are well supported by splice junction reads. Precise transcript quantification using BaRTv1.0 allows routine analysis of gene expression and AS.

scholarly journals Siberian sturgeon multi-tissue reference transcriptome database

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Christophe Klopp ◽  
Cédric Cabau ◽  
Gonzalo Greif ◽  
André Lasalle ◽  
Santiago Di Landro ◽  
...  
Keyword(s):  
Reference Genome ◽  
Transcriptome Assembly ◽  
Fish Farming ◽  
Siberian Sturgeon ◽  
Supplementary Information ◽  
Rna Seq ◽  
High Quality ◽  
Reference Transcriptome ◽  
Functional Studies ◽  
Transcriptome Database

Abstract Motivation: Siberian sturgeon is a long lived and late maturing fish farmed for caviar production in 50 countries. Functional genomics enable to find genes of interest for fish farming. In the absence of a reference genome, a reference transcriptome is very useful for sequencing based functional studies. Results: We present here a high-quality transcriptome assembly database built using RNA-seq reads coming from brain, pituitary, gonadal, liver, stomach, kidney, anterior kidney, heart, embryonic and pre-larval tissues. It will facilitate crucial research on topics such as puberty, reproduction, growth, food intake and immunology. This database represents a major contribution to the publicly available sturgeon transcriptome reference datasets. Availability: The database is publicly available at http://siberiansturgeontissuedb.sigenae.org Supplementary information:  Supplementary data are available at Database online.

scholarly journals AtRTD2: A Reference Transcript Dataset for accurate quantification of alternative splicing and expression changes in Arabidopsis thaliana RNA-seq data

2016 ◽  
Author(s):  
Runxuan Zhang ◽  
Cristiane P. G. Calixto ◽  
Yamile Marquez ◽  
Peter Venhuizen ◽  
Nikoleta A. Tzioutziou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Alternative Splicing ◽  
Rna Seq ◽  
Protein Coding ◽  
Transcript Isoforms ◽  
Transcript Quantification ◽  
Protein Coding Genes ◽  
Genome Wide ◽  
Reference Transcript

AbstractBackgroundAlternative splicing is the major post-transcriptional mechanism by which gene expression is regulated and affects a wide range of processes and responses in most eukaryotic organisms. RNA-sequencing (RNA-seq) can generate genome-wide quantification of individual transcript isoforms to identify changes in expression and alternative splicing. RNA-seq is an essential modern tool but its ability to accurately quantify transcript isoforms depends on the diversity, completeness and quality of the transcript information.ResultsWe have developed a new Reference Transcript Dataset for Arabidopsis (AtRTD2) for RNA-seq analysis containing over 82k non-redundant transcripts, whereby 74,194 transcripts originate from 27,667 protein-coding genes. A total of 13,524 protein-coding genes have at least one alternatively spliced transcript in AtRTD2 such that about 60% of the 22,453 protein-coding, intron-containing genes in Arabidopsis undergo alternative splicing. More than 600 putative U12 introns were identified in more than 2,000 transcripts. AtRTD2 was generated from transcript assemblies of ca. 8.5 billion pairs of reads from 285 RNA-seq data sets obtained from 129 RNA-seq libraries and merged along with the previous version, AtRTD, and Araport11 transcript assemblies. AtRTD2 increases the diversity of transcripts and through application of stringent filters represents the most extensive and accurate transcript collection for Arabidopsis to date. We have demonstrated a generally good correlation of alternative splicing ratios from RNA-seq data analysed by Salmon and experimental data from high resolution RT-PCR. However, we have observed inaccurate quantification of transcript isoforms for genes with multiple transcripts which have variation in the lengths of their UTRs. This variation is not effectively corrected in RNA-seq analysis programmes and will therefore impact RNA-seq analyses generally. To address this, we have tested different genome-wide modifications of AtRTD2 to improve transcript quantification and alternative splicing analysis. As a result, we release AtRTD2-QUASI specifically for use in Quantification of Alternatively Spliced Isoforms and demonstrate that it out-performs other available transcriptomes for RNA-seq analysis.ConclusionsWe have generated a new transcriptome resource for RNA-seq analyses in Arabidopsis (AtRTD2) designed to address quantification of different isoforms and alternative splicing in gene expression studies. Experimental validation of alternative splicing changes identified inaccuracies in transcript quantification due to UTR length variation. To solve this problem, we also release a modified reference transcriptome, AtRTD2-QUASI for quantification of transcript isoforms, which shows high correlation with experimental data.

scholarly journals ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events

2018 ◽  
Author(s):  
Luca Denti ◽  
Raffaella Rizzi ◽  
Stefano Beretta ◽  
Gianluca Della Vedova ◽  
Marco Previtali ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Experimental Analysis ◽  
Reference Genome ◽  
Gene Annotation ◽  
Source Code ◽  
Rna Seq ◽  
Computationally Expensive ◽  
Transcriptome Analyses ◽  
Alternative Splicing Events ◽  
Complicated Task

AbstractBackground: While the reconstruction of transcripts from a sample of RNA-Seq data is a computationally expensive and complicated task, the detection of splicing events from RNA-Seq data and a gene annotation is computationally feasible. The latter task, which is adequate for many transcriptome analyses, is usually achieved by aligning the reads to a reference genome, followed by comparing the alignments with a gene annotation, often implicitly represented by a graph: the splicing graph.Results: We present ASGAL (Alternative Splicing Graph ALigner): a tool for mapping RNA-Seq data to the splicing graph, with the main goal of detecting novel alternative splicing events. ASGAL receives in input the annotated transcripts of a gene and an RNA-Seq sample, and it computes (1) the spliced alignments of each read, and (2) a list of novel events with respect to the gene annotation.Conclusions: An experimental analysis shows that, by aligning reads directly to the splicing graph, ASGAL better predicts alternative splicing events when compared to tools requiring spliced alignments of the RNA-Seq data to a reference genome. To the best of our knowledge, ASGAL is the first tool that detects novel alternative splicing events by directly aligning reads to a splicing graph.Availability: Source code, documentation, and data are available for download at http://asgal.algolab.eu.

scholarly journals PacBio Iso-Seq Improves the Rainbow Trout Genome Annotation and Identifies Alternative Splicing Associated With Economically Important Phenotypes

2021 ◽  
Vol 12 ◽  
Author(s):  
Ali Ali ◽  
Gary H. Thorgaard ◽  
Mohamed Salem
Keyword(s):  
Rainbow Trout ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Genome Annotation ◽  
Reference Genome ◽  
Cold Water ◽  
Intron Retention ◽  
Transcriptome Assembly ◽  
Model Organism ◽  
Fish Growth

Rainbow trout is an important model organism that has received concerted international efforts to study the transcriptome. For this purpose, short-read sequencing has been primarily used over the past decade. However, these sequences are too short of resolving the transcriptome complexity. This study reported a first full-length transcriptome assembly of the rainbow trout using single-molecule long-read isoform sequencing (Iso-Seq). Extensive computational approaches were used to refine and validate the reconstructed transcriptome. The study identified 10,640 high-confidence transcripts not previously annotated, in addition to 1,479 isoforms not mapped to the current Swanson reference genome. Most of the identified lncRNAs were non-coding variants of coding transcripts. The majority of genes had multiple transcript isoforms (average ∼3 isoforms/locus). Intron retention (IR) and exon skipping (ES) accounted for 56% of alternative splicing (AS) events. Iso-Seq improved the reference genome annotation, which allowed identification of characteristic AS associated with fish growth, muscle accretion, disease resistance, stress response, and fish migration. For instance, an ES in GVIN1 gene existed in fish susceptible to bacterial cold-water disease (BCWD). Besides, under five stress conditions, there was a commonly regulated exon in prolyl 4-hydroxylase subunit alpha-2 (P4HA2) gene. The reconstructed gene models and their posttranscriptional processing in rainbow trout provide invaluable resources that could be further used for future genetics and genomics studies. Additionally, the study identified characteristic transcription events associated with economically important phenotypes, which could be applied in selective breeding.

scholarly journals Exact transcript quantification over splice graphs

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Cong Ma ◽  
Hongyu Zheng ◽  
Carl Kingsford
Keyword(s):  
Transcriptome Assembly ◽  
Generation Model ◽  
Rna Seq ◽  
New Approach ◽  
Transcript Quantification ◽  
Splice Graph ◽  
Quantification Model ◽  
Splice Junctions ◽  
Model Graph ◽  
Expression Quantification

Abstract Background The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447–55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed. Results We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quantifier with exactly the set of all compatible transcripts. The key to our method is constructing an extension of the splice graph based on Aho-Corasick automata. The proof of equivalence is based on a novel reparameterization of the read generation model of a state-of-art transcript quantification method. Conclusion We propose a new approach for graph quantification, which is useful for modeling scenarios where reference transcriptome is incomplete or not available and can be further used in transcriptome assembly or alternative splicing analysis.

scholarly journals High-Quality Reference Genome Sequence for the Oomycete Vegetable Pathogen Phytophthora capsici Strain LT1534

2021 ◽  
Vol 10 (21) ◽  
Author(s):  
Jason E. Stajich ◽  
Andrea L. Vu ◽  
Howard S. Judelson ◽  
Gregory M. Vogel ◽  
Michael A. Gore ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Genome Assembly ◽  
Transcriptome Sequencing ◽  
Reference Genome ◽  
Phytophthora Capsici ◽  
Rna Seq ◽  
High Quality ◽  
Content Type ◽  
Wide Range ◽  
Illumina Data

The oomycete Phytophthora capsici is a destructive pathogen of a wide range of vegetable hosts, especially peppers and cucurbits. A 94.17-Mb genome assembly was constructed using PacBio and Illumina data and annotated with support from transcriptome sequencing (RNA-Seq) reads.

scholarly journals Transcriptomic basis for salt tolerance and disease resistance of silverleaf sunflower revealed by Iso-seq and RNA-seq

2019 ◽  
Author(s):  
Jing Bing ◽  
Yunhe Ling ◽  
Peipei An ◽  
Enshi Xiao ◽  
Chunlian Li ◽  
...  
Keyword(s):  
Disease Resistance ◽  
Salt Tolerance ◽  
Reference Genome ◽  
Transcriptome Assembly ◽  
Future Research ◽  
Rna Seq ◽  
Gene Expressions ◽  
Breeding Programs ◽  
Genomic Variance ◽  
Cultivated Species

Abstract Background Silverleaf sunflower, Helianthus argophyllus , is one of the most important wild species that have been usually used for the improvement of cultivated sunflower. Although a reference genome is now available for the cultivated species, H. annuus , its effect in helping understanding the mechanisms underlying the traits of H. argophyllus is limited by the substantial genomic variance between these two species.Results In this study, we generated a high-quality reference transcriptome of H. argophyllus using Iso-seq strategy. This assembly contains 50,153 unique genes covering more than 91% of the whole genes. Among them, we find 205 genes that are absent in the cultivated species and 475 fusion genes containing components of coding or non-coding sequences from the genome of H. annuus . It is interesting that in line with the strong disease resistance observed for H. argophyllus , these H. argophyllus -specific genes are predominantly related to functions of resistance. We have also profiled the gene expressions in leaf and root under normal or salt stressed conditions and, as a result, find distinct transcriptomic responses to salt stress in leaf and root. Particularly, genes involved in several critical processes including the synthesis and metabolism of glutamate and carbohydrate transport are reversely regulated in leaf and root.Conclusions Overall, this study provided insights into the genomic mechanisms underlying the disease resistance and salt tolerance of silverleaf sunflower and the transcriptome assembly and the genes identified in this study can serve as a complement data resources for future research and breeding programs of sunflowers.

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