Abstract
Research in the past decade has demonstrated that a single reference genome is not representative of a species’ diversity. MaizeGDB introduces a pan-genomic approach to hosting genomic data, ...leveraging the large number of diverse maize genomes and their associated datasets to quickly and efficiently connect genomes, gene models, expression, epigenome, sequence variation, structural variation, transposable elements, and diversity data across genomes so that researchers can easily track the structural and functional differences of a locus and its orthologs across maize. We believe our framework is unique and provides a template for any genomic database poised to host large-scale pan-genomic data.
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Abstract
Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome ...assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.
Key message
High-density haplotype analysis revealed significant haplotype sharing between ex-PVPs registered from 1976 to 1992 and key maize founders, and uncovered similarities and differences in ...haplotype sharing patterns by company and heterotic group.
Proprietary inbreds developed by the private seed industry have been the major source for driving genetic gain in successful North American maize hybrids for decades. Much of the history of industry germplasm can be traced back to key founder lines, some of which were pivotal in the development of prominent heterotic groups. Previous studies have summarized pedigree-based relationships, genetic diversity and population structure among commercial inbreds with expired Plant Variety Protection (ex-PVP). However, less is known about the extent of haplotype sharing between historical founders and ex-PVPs. A better understanding of the relationships between founders and ex-PVPs provides insight into the haplotype and heterotic group structure among industry germplasm. We performed high-density haplotype analysis with 11.3 million SNPs on 212 maize inbreds, which included 157 ex-PVPs registered 1976–1992 and 55 public lines relevant to PVPs. Among these lines were 12 key founders identified in literature review: 207, A632, B14, B37, B73, LH123HT, LH82, Mo17, Oh43, OH7, PHG39 and Wf9. Our results revealed that, on average, 81.6% of an ex-PVP’s genome is shared with at least 1 of these 12 founder lines and more than half when limited to B73, Mo17 and 207. Quantifiable similarities and contrasts among heterotic groups and major US seed industry companies were also observed. The results from this study provide high-resolution haplotype data on ex-PVP germplasm, confirm founder relationship trends observed in previous studies, uncover region-specific haplotype structure differences and demonstrate how haplotype sharing analysis can be used as a tool to explore germplasm diversity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
With the advances in the high throughput next generation sequencing technologies, genome-wide association studies (GWAS) have identified a large set of variants associated with complex phenotypic ...traits at a very fine scale. Despite the progress in GWAS, identification of genotype-phenotype relationship remains challenging in maize due to its nature with dozens of variants controlling the same trait. As the causal variations results in the change in expression, gene expression analyses carry a pivotal role in unraveling the transcriptional regulatory mechanisms behind the phenotypes.
To address these challenges, we incorporated the gene expression and GWAS-driven traits to extend the knowledge of genotype-phenotype relationships and transcriptional regulatory mechanisms behind the phenotypes. We constructed a large collection of gene co-expression networks and identified more than 2 million co-expressing gene pairs in the GWAS-driven pan-network which contains all the gene-pairs in individual genomes of the nested association mapping (NAM) population. We defined four sub-categories for the pan-network: (1) core-network contains the highest represented ~ 1% of the gene-pairs, (2) near-core network contains the next highest represented 1-5% of the gene-pairs, (3) private-network contains ~ 50% of the gene pairs that are unique to individual genomes, and (4) the dispensable-network contains the remaining 50-95% of the gene-pairs in the maize pan-genome. Strikingly, the private-network contained almost all the genes in the pan-network but lacked half of the interactions. We performed gene ontology (GO) enrichment analysis for the pan-, core-, and private- networks and compared the contributions of variants overlapping with genes and promoters to the GWAS-driven pan-network.
Gene co-expression networks revealed meaningful information about groups of co-regulated genes that play a central role in regulatory processes. Pan-network approach enabled us to visualize the global view of the gene regulatory network for the studied system that could not be well inferred by the core-network alone.
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The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; ...however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.
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MaizeGDB is a highly curated, community-oriented database and informatics service to researchers focused on the crop plant and model organism Zea mays ssp. mays. Although some form of the maize ...community database has existed over the last 25 years, there have only been two major releases. In 1991, the original maize genetics database MaizeDB was created. In 2003, the combined contents of MaizeDB and the sequence data from ZmDB were made accessible as a single resource named MaizeGDB. Over the next decade, MaizeGDB became more sequence driven while still maintaining traditional maize genetics datasets. This enabled the project to meet the continued growing and evolving needs of the maize research community, yet the interface and underlying infrastructure remained unchanged. In 2015, the MaizeGDB team completed a multi-year effort to update the MaizeGDB resource by reorganizing existing data, upgrading hardware and infrastructure, creating new tools, incorporating new data types (including diversity data, expression data, gene models, and metabolic pathways), and developing and deploying a modern interface. In addition to coordinating a data resource, the MaizeGDB team coordinates activities and provides technical support to the maize research community. MaizeGDB is accessible online at http://www.maizegdb.org.
Genome assemblies are foundational for understanding the biology of a species. They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, ...genomic diversity and differences in gene expression across individuals and tissue types. Quality metrics for genome assemblies gauge both the completeness and contiguity of an assembly and help provide confidence in downstream biological insights. To compare quality across multiple assemblies, a set of common metrics are typically calculated and then compared to one or more gold standard reference genomes. While several tools exist for calculating individual metrics, applications providing comprehensive evaluations of multiple assembly features are, perhaps surprisingly, lacking. Here, we describe a new toolkit that integrates multiple metrics to characterize both assembly and gene annotation quality in a way that enables comparison across multiple assemblies and assembly types.
Our application, named GenomeQC, is an easy-to-use and interactive web framework that integrates various quantitative measures to characterize genome assemblies and annotations. GenomeQC provides researchers with a comprehensive summary of these statistics and allows for benchmarking against gold standard reference assemblies.
The GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license. All source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.
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Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain ...overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.
We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.
FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.
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We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of ...pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.
Environmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies ...provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress.
We mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments.
These results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB).
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