Next-generation sequencing applications for wheat crop improvement

2012 ◽  
Vol 99 (2) ◽  
pp. 365-371 ◽  
Author(s):  
Paul J. Berkman ◽  
Kaitao Lai ◽  
Michał T. Lorenc ◽  
David Edwards
Keyword(s):  
Next Generation Sequencing ◽  
Crop Improvement ◽  
Wheat Crop ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Next Generation Sequencing Based Forward Genetic Approaches for Identification and Mapping of Causal Mutations in Crop Plants: A Comprehensive Review

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1355
Author(s):  
Parmeshwar K. Sahu ◽  
Richa Sao ◽  
Suvendu Mondal ◽  
Gautam Vishwakarma ◽  
Sudhir Kumar Gupta ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Crop Improvement ◽  
Point Mutations ◽  
Crop Plants ◽  
Forward Genetics ◽  
Next Generation ◽  
Bioinformatics Tools ◽  
Short Period ◽  
Ngs Data ◽  
Generation Sequencing

The recent advancements in forward genetics have expanded the applications of mutation techniques in advanced genetics and genomics, ahead of direct use in breeding programs. The advent of next-generation sequencing (NGS) has enabled easy identification and mapping of causal mutations within a short period and at relatively low cost. Identifying the genetic mutations and genes that underlie phenotypic changes is essential for understanding a wide variety of biological functions. To accelerate the mutation mapping for crop improvement, several high-throughput and novel NGS based forward genetic approaches have been developed and applied in various crops. These techniques are highly efficient in crop plants, as it is relatively easy to grow and screen thousands of individuals. These approaches have improved the resolution in quantitative trait loci (QTL) position/point mutations and assisted in determining the functional causative variations in genes. To be successful in the interpretation of NGS data, bioinformatics computational methods are critical elements in delivering accurate assembly, alignment, and variant detection. Numerous bioinformatics tools/pipelines have been developed for such analysis. This article intends to review the recent advances in NGS based forward genetic approaches to identify and map the causal mutations in the crop genomes. The article also highlights the available bioinformatics tools/pipelines for reducing the complexity of NGS data and delivering the concluding outcomes.

scholarly journals Efficient curation of genebanks using next generation sequencing reveals substantial duplication of germplasm accessions

2018 ◽  
Author(s):  
Narinder Singh ◽  
Shuangye Wu ◽  
W. John Raupp ◽  
Sunish Sehgal ◽  
Sanu Arora ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Next Generation Sequencing ◽  
Crop Improvement ◽  
Aegilops Tauschii ◽  
Cost Effective ◽  
Ex Situ ◽  
Next Generation ◽  
Sequencing Platform ◽  
Efficient Management ◽  
Generation Sequencing

ABSTRACTGenebanks are valuable resources for crop improvement through the acquisition, ex-situ conservation and sharing of unique germplasm among plant breeders and geneticists. With over seven million existing accessions and increasing storage demands and costs, genebanks need efficient characterization and curation to make them more accessible and usable and to reduce operating costs, so that the crop improvement community can most effectively leverage this vast resource of untapped novel genetic diversity. However, the sharing and inconsistent documentation of germplasm often results in unintentionally duplicated collections with poor characterization and many identical accessions that can be hard or impossible to identify without passport information and unmatched accession identifiers. Here we demonstrate the use of genotypic information from these accessions using a cost-effective next generation sequencing platform to find and remove duplications. We identify and characterize over 50% duplicated accessions both within and across genebank collections of Aegilops tauschii, an important wild relative of wheat and source of genetic diversity for wheat improvement. We present a pipeline to identify and remove identical accessions within and among genebanks and curate globally unique accessions. We also show how this approach can also be applied to future collection efforts to avoid the accumulation of identical material. When coordinated across global genebanks, this approach will ultimately allow for cost effective and efficient management of germplasm and better stewarding of these valuable resources.

scholarly journals Benchmarking Variant Identification Tools for Plant Diversity Discovery

2019 ◽  
Author(s):  
Xing Wu ◽  
Christopher Heffelfinger ◽  
Hongyu Zhao ◽  
Stephen L. Dellaporta
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Crop Improvement ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Discovery ◽  
Variant Filtering ◽  
Generation Sequencing

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

scholarly journals Benchmarking variant identification tools for plant diversity discovery

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Xing Wu ◽  
Christopher Heffelfinger ◽  
Hongyu Zhao ◽  
Stephen L. Dellaporta
Keyword(s):  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Crop Improvement ◽  
Variant Calling ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Variant Discovery ◽  
Variant Filtering ◽  
Generation Sequencing

Abstract Background The ability to accurately and comprehensively identify genomic variations is critical for plant studies utilizing high-throughput sequencing. Most bioinformatics tools for processing next-generation sequencing data were originally developed and tested in human studies, raising questions as to their efficacy for plant research. A detailed evaluation of the entire variant calling pipeline, including alignment, variant calling, variant filtering, and imputation was performed on different programs using both simulated and real plant genomic datasets. Results A comparison of SOAP2, Bowtie2, and BWA-MEM found that BWA-MEM was consistently able to align the most reads with high accuracy, whereas Bowtie2 had the highest overall accuracy. Comparative results of GATK HaplotypCaller versus SAMtools mpileup indicated that the choice of variant caller affected precision and recall differentially depending on the levels of diversity, sequence coverage and genome complexity. A cross-reference experiment of S. lycopersicum and S. pennellii reference genomes revealed the inadequacy of single reference genome for variant discovery that includes distantly-related plant individuals. Machine-learning-based variant filtering strategy outperformed the traditional hard-cutoff strategy resulting in higher number of true positive variants and fewer false positive variants. A 2-step imputation method, which utilized a set of high-confidence SNPs as the reference panel, showed up to 60% higher accuracy than direct LD-based imputation. Conclusions Programs in the variant discovery pipeline have different performance on plant genomic dataset. Choice of the programs is subjected to the goal of the study and available resources. This study serves as an important guiding information for plant biologists utilizing next-generation sequencing data for diversity characterization and crop improvement.

Insights into the evo-devo of plant reproduction using next-generation sequencing approaches

2020 ◽  
Author(s):  
Jill C Preston
Keyword(s):  
Next Generation Sequencing ◽  
Crop Improvement ◽  
Population Level ◽  
Evolutionary Developmental Biology ◽  
Model Organisms ◽  
Plant Responses ◽  
Next Generation ◽  
Evo Devo ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract The development of plant model organisms has traditionally been analyzed using resource-heavy, tailored applications that are not easily transferable to distantly related non-model taxa. Thus, our understanding of plant development has been limited to a subset of traits, and evolutionary studies conducted most effectively either across very wide [e.g. Arabidopsis thaliana and Oryza sativa (rice)] or narrow (i.e. population level) phylogenetic distances. As plant biologists seek to capitalize on natural diversity for crop improvement, enhance ecosystem functioning, and better understand plant responses to climate change, high-throughput and broadly applicable forms of existing molecular biology assays are becoming an invaluable resource. Next-generation sequencing (NGS) is increasingly becoming a powerful tool in evolutionary developmental biology (evo-devo) studies, particularly through its application to understanding trait evolution at different levels of gene regulation. Here, I review some of the most common and emerging NGS-based methods, using exemplar studies in reproductive plant evo-devo to illustrate their potential.

scholarly journals Next Generation Sequencing Data Analysis and its Applications in Agriculture

2021 ◽  
Author(s):  
Shbana Begum ◽  
Rahul Banerjee
Keyword(s):  
Data Analysis ◽  
Next Generation Sequencing ◽  
Low Cost ◽  
Crop Improvement ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Genomic Studies ◽  
Generation Sequencing ◽  
Sequencing Data Analysis

Next Generation sequencing (NGS) technologies are revolutionizing the acquisition of genomic data at relatively low cost. NGS technologies are rapidly changing approaches to complex genomic studies and generating a vast amount of data. New and more powerful bioinformatics proposals and tools are needed to handle such large biological collections of such huge amounts of data provided by NGS data analysis. In addition, specialized software tools and advanced computational resources are required for data integration. In this article, we are describing here the main computational approach for next generation sequencing data analysis and its role in agriculture for crop improvement.

scholarly journals Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

2022 ◽  
Vol 23 (2) ◽  
pp. 651
Author(s):  
Muhammad Furqan Ashraf ◽  
Dan Hou ◽  
Quaid Hussain ◽  
Muhammad Imran ◽  
Jialong Pei ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
De Novo ◽  
Crop Improvement ◽  
Molecular Techniques ◽  
Natural Ecosystem ◽  
Next Generation ◽  
Wild Type ◽  
Plant Metabolites ◽  
Genetic Potential ◽  
Generation Sequencing

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists’ emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants’ genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

Sign in / Sign up

Export Citation Format

Share Document