scholarly journals Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Erik Alexandersson ◽  
Sandeep Kushwaha ◽  
Aastha Subedi ◽  
Deborah Weighill ◽  
Sharlee Climer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
De Novo ◽  
Field Performance ◽  
Late Blight Resistance ◽  
Pathogen Resistance ◽  
Rna Seq ◽  
Climate Conditions ◽  
Negatively Associated ◽  
Dickeya Solani

Abstract Background Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93–1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding. Results A total of 34 progeny lines from SW93–1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate. Conclusion In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

scholarly journals Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

2020 ◽  
Author(s):  
Erik Alexandersson ◽  
Sandeep Kushwaha ◽  
Aastha Subedi ◽  
Deborah Weighill ◽  
Sharlee Climer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
De Novo ◽  
Field Performance ◽  
Late Blight Resistance ◽  
Pathogen Resistance ◽  
Rna Seq ◽  
Climate Conditions ◽  
Negatively Associated ◽  
Dickeya Solani

Abstract Background Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding. Results A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate. Conclusion In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

scholarly journals Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

2019 ◽  
Author(s):  
Erik Alexandersson ◽  
Sandeep Kushwaha ◽  
Aastha Subedi ◽  
Deborah Weighill ◽  
Sharlee Climer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
De Novo ◽  
Field Performance ◽  
Late Blight Resistance ◽  
Pathogen Resistance ◽  
Rna Seq ◽  
Climate Conditions ◽  
Negatively Associated ◽  
Dickeya Solani

Abstract Background Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding. Results A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate. Conclusion In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

scholarly journals Linking crop traits to transcriptome differences in a progeny population of tetraploid potato

2019 ◽  
Author(s):  
Erik Alexandersson ◽  
Sandeep Kushwaha ◽  
Aastha Subedi ◽  
Deborah Weighill ◽  
Sharlee Climer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
De Novo ◽  
Field Performance ◽  
Late Blight Resistance ◽  
Pathogen Resistance ◽  
Rna Seq ◽  
Climate Conditions ◽  
Negatively Associated ◽  
Dickeya Solani

Abstract Background Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding. Results A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate. Conclusion In our study, we identify 100’s of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

Effect of SMARCB1 deficiency in renal medullary carcinoma (RMC) on genes associated with nucleosome assembly and telomere organization.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 614-614 ◽  
Author(s):  
Pavlos Msaouel ◽  
Gabriel G. Malouf ◽  
Xiaoping Su ◽  
Hui Yao ◽  
Durga N Tripathi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Sickle Cell Trait ◽  
Regulation Of Gene Expression ◽  
Kidney Tissue ◽  
Rna Seq ◽  
Nucleosome Assembly ◽  
P Values ◽  
Go Analysis

614 Background: RMC is a highly aggressive tumor with close to universal fatality despite therapy. It is almost exclusively found in young African-Americans with sickle cell trait, and is characterized by complete loss of expression of SMARCB1, a major chromatin remodeler involved in regulation of gene expression. We investigated the effects of SMARCB1 loss on mutation frequency, gene expression, and cell growth in RMC. Methods: Whole exome sequencing (WES) and RNA sequencing (RNA-seq) were performed in RMC tissues from 15 and 11 patients respectively, each with matched adjacent normal kidney tissue controls. In vitro experiments were performed in a cell line (RMC2C) we established from a patient with RMC. SMARCB1 was conditionally re-expressed using a tetracycline-inducible lentivector. Gene ontology (GO) analysis was performed using DAVID. Results: WES showed that RMC harbors a low number (median of < 25/tumor sample) of non-synonymous exomic single nucleotide variants (SNVs) or small indels. GO analysis revealed that the most significant pathways upregulated in RMC compared with normal tissue were those associated with nucleosome assembly and telomere organization (p values < 0.0001). Re-expression of SMARCB1 at near-endogenous levels suppressed the growth rate of RMC2C cells. Subsequent silencing of SMARCB1 expression restored the growth rate of these cells. RNA-seq of RMC2C cells expressing SMARCB1 demonstrated that the most significant downregulated pathways compared with SMARCB1-negative RMC2C cells were those associated with nucleosome assembly and telomere organization (p values < 0.0001). Conclusions: RMC harbors a remarkably simple genome, as evidenced by our WES analysis. Therefore, consistently detected alterations, such as SMARCB1 loss, are likely to serve as drivers for this disease. Indeed, in vitro restoration of SMARCB1 expression suppressed the growth of RMC cells and repressed genes associated with nucleosome assembly and telomere organization, identifying for the first time a causal link between loss of SMARCB1 and dysregulation of these genes. These results provide the basis for future therapeutic strategies targeting SMARCB1 loss in RMC.

scholarly journals The Vigna unguiculata Gene Expression Atlas (Vu GEA ) from de novo assembly and quantification of RNA ‐seq data provides insights into seed maturation mechanisms

2016 ◽  
Vol 88 (2) ◽  
pp. 318-327 ◽  
Author(s):  
Shaolun Yao ◽  
Chuan Jiang ◽  
Ziyue Huang ◽  
Ivone Torres‐Jerez ◽  
Junil Chang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Vigna Unguiculata ◽  
De Novo Assembly ◽  
De Novo ◽  
Seed Maturation ◽  
Rna Seq ◽  
Gene Expression Atlas ◽  
Expression Atlas

scholarly journals gEAR: gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

2020 ◽  
Author(s):  
Joshua Orvis ◽  
Brian Gottfried ◽  
Jayaram Kancherla ◽  
Ricky S. Adkins ◽  
Yang Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
De Novo ◽  
Marker Gene ◽  
Rna Seq ◽  
Data Types ◽  
Multiple Species ◽  
User Friendly ◽  
The Brain

ABSTRACTThe gEAR portal (gene Expression Analysis Resource, umgear.org) is an open access community-driven tool for multi-omic and multi-species data visualization, analysis and sharing. The gEAR supports visualization of multiple RNA-seq data types (bulk, sorted, single cell/nucleus) and epigenomics data, from multiple species, time points and tissues in a single-page, user-friendly browsable format. An integrated scRNA-seq workbench provides access to raw data of scRNA-seq datasets for de novo analysis, as well as marker-gene and cluster comparisons of pre-assigned clusters. Users can upload, view, analyze and privately share their own data in the context of previously published datasets. Short, permanent URLs can be generated for dissemination of individual or collections of datasets in published manuscripts. While the gEAR is currently curated for auditory research with over 90 high-value datasets organized in thematic profiles, the gEAR also supports the BRAIN initiative (via nemoanalytics.org) and is easily adaptable for other research domains.

scholarly journals Development of a relevant strategy using de novo transcriptome assembly method for transcriptome comparisons between Muscovy and common duck species and their reciprocal inter-specific Mule and Hinny hybrids fed ad libitum and overfed

2020 ◽  
Author(s):  
Xi Liu ◽  
Frédéric Hérault ◽  
Christian Diot ◽  
Erwan Corre
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Genetic Type ◽  
Rna Seq ◽  
Genetic Types ◽  
Assembly Method ◽  
Muscovy Ducks ◽  
Ad Libitum ◽  
Different Response

Abstract Background: Common Pekin and Muscovy ducks and their intergeneric hinny and mule hybrids have different abilities for fatty liver production. RNA-Seq analyses from the liver of these different genetic types fed ad libitum or overfed would help to identify genes with different response to overfeeding between them. However RNA-seq analyses from different species and comparison is challenging. The goal of this study was develop a relevant strategy for transcriptome analysis and comparison between different species.Results: Transcriptomes were first assembled with a reference-based approach. Important mapping biases were observed when heterologous mapping were conducted on common duck reference genome, suggesting that this reference-based strategy was not suited to compare the four different genetic types. De novo transcriptome assemblies were then performed using Trinity and Oases. Assemblies of transcriptomes were not relevant when more than a single genetic type was considered. Finally, single genetic type transcriptomes were assembled with DRAP in a mega-transcriptome. No bias was observed when reads from the different genetic types were mapped on this mega-transcriptome and differences in gene expression between the four genetic types could be identified.Conclusions: Analyses using both reference-based and de novo transcriptome assemblies point out a good performance of the de novo approach for the analysis of gene expression in different species. It also allowed the identification of differences in responses to overfeeding between Pekin and Muscovy ducks and hinny and mule hybrids.

scholarly journals Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer

2016 ◽  
Author(s):  
Shirley Pepke ◽  
Greg Ver Steeg
Keyword(s):  
Gene Expression ◽  
Drug Targets ◽  
De Novo ◽  
Epithelial Mesenchymal Transition ◽  
Learning Algorithm ◽  
Gene Clusters ◽  
Rna Seq ◽  
Latent Factors ◽  
Mesenchymal Transition ◽  
Therapeutic Implications

BackgroundDe novoinference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations (co-expression) that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem.MethodsIn this work we adapt a recently developed machine learning algorithm, CorEx, that efficiently optimizes over multivariate mutual information for sensitive detection of complex gene relationships. The algorithm can be iteratively applied to generate a hierarchy of latent factors. Patients are stratified relative to each factor and combinatoric survival analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies.ResultsAnalysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a potentially druggable microRNA. Further, combinations of factors lead to a synergistic survival advantage in some cases.ConclusionsIn contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are both found to have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments.

scholarly journals Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment

2021 ◽  
Author(s):  
Anish M.S. Shrestha ◽  
Joyce Emlyn B. Guiao ◽  
Kyle Christian R. Santiago
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Differential Expression Analysis ◽  
Homology Search ◽  
Model Organisms ◽  
Rna Seq ◽  
Protein Database

AbstractRNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. Conventional differential expression analysis for organisms without reference sequences requires performing computationally expensive and error-prone de-novo transcriptome assembly, followed by homology search against a high-confidence protein database for functional annotation. We propose a shortcut, where we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the protein database. Through experiments on simulated and real data, we show drastic reductions in run-time and memory usage, with no loss in accuracy. A Snakemake implementation of our workflow is available at:https://bitbucket.org/project_samar/samar

scholarly journals Development of a relevant strategy using de novo transcriptome assembly method for transcriptome comparisons between Muscovy and common duck species and their reciprocal inter-specific Mule and Hinny hybrids fed ad libitum and overfed

2020 ◽  
Author(s):  
Xi Liu ◽  
Frédéric Hérault ◽  
Christian Diot ◽  
Erwan Corre
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Genetic Type ◽  
Rna Seq ◽  
Genetic Types ◽  
Assembly Method ◽  
Muscovy Ducks ◽  
Ad Libitum ◽  
Different Response

Abstract Background: Common Pekin and Muscovy ducks and their intergeneric hinny and mule hybrids have different abilities for fatty liver production. RNA-Seq analyses from the liver of these different genetic types fed ad libitum or overfed would help to identify genes with different response to overfeeding between them. However RNA-seq analyses from different species and comparison is challenging. The goal of this study was develop a relevant strategy for transcriptome analysis and comparison between different species.Results: Transcriptomes were first assembled with a reference-based approach. Important mapping biases were observed when heterologous mapping were conducted on common duck reference genome, suggesting that this reference-based strategy was not suited to compare the four different genetic types. De novo transcriptome assemblies were then performed using Trinity and Oases. Assemblies of transcriptomes were not relevant when more than a single genetic type was considered. Finally, single genetic type transcriptomes were assembled with DRAP in a mega-transcriptome. No bias was observed when reads from the different genetic types were mapped on this mega-transcriptome and differences in gene expression between the four genetic types could be identified.Conclusions: Analyses using both reference-based and de novo transcriptome assemblies point out a good performance of the de novo approach for the analysis of gene expression in different species. It also allowed the identification of differences in responses to overfeeding between Pekin and Muscovy ducks and hinny and mule hybrids.

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