Gigwa-Genotype investigator for genome-wide analyses

The groundnut improvement network for Africa (GINA) germplasm collection: a unique genetic resource for breeding and gene discovery

Cultivated peanut or groundnut (Arachis hypogaea L.) is a grain legume grown in many developing countries by smallholder farmers for food, feed, and/or income. The speciation of the cultivated species, that involved polyploidization followed by domestication, greatly reduced its variability at the DNA level. Mobilizing peanut diversity is a prerequisite for any breeding program for overcoming the main constraints that plague production and for increasing yield in farmer fields. In this study, the Groundnut Improvement Network for Africa assembled a collection of 1,049 peanut breeding lines, varieties, and landraces from 9 countries in Africa. The collection was genotyped with the Axiom_Arachis2 48K SNP array and 8,229 polymorphic single nucleotide polymorphism (SNP) markers were used to analyze the genetic structure of this collection and quantify the level of genetic diversity in each breeding program. A supervised model was developed using dapc to unambiguously assign 542, 35, and 172 genotypes to the Spanish, Valencia, and Virginia market types, respectively. Distance-based clustering of the collection showed a clear grouping structure according to subspecies and market types, with 73% of the genotypes classified as fastigiata and 27% as hypogaea subspecies. Using STRUCTURE, the global structuration was confirmed and showed that, at a minimum membership of 0.8, 76% of the varieties that were not assigned by dapc were actually admixed. This was particularly the case of most of the genotype of the Valencia subgroup that exhibited admixed genetic heritage. The results also showed that the geographic origin (i.e. East, Southern, and West Africa) did not strongly explain the genetic structure. The gene diversity managed by each breeding program, measured by the expected heterozygosity, ranged from 0.25 to 0.39, with the Niger breeding program having the lowest diversity mainly because only lines that belong to the fastigiata subspecies are used in this program. Finally, we developed a core collection composed of 300 accessions based on breeding traits and genetic diversity. This collection, which is composed of 205 genotypes of fastigiata subspecies (158 Spanish and 47 Valencia) and 95 genotypes of hypogaea subspecies (all Virginia), improves the genetic diversity of each individual breeding program and is, therefore, a unique resource for allele mining and breeding.

Managing High-Density Genotyping Data with Gigwa

Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.

Unravelling the complex story of intergenomic recombination in ABB allotriploid bananas

BACKGROUND AND AIMS

Bananas (Musa spp.) are a major staple food for hundreds of millions of people in developing countries. The cultivated varieties are seedless and parthenocarpic clones of which the ancestral origin remains to be clarified. The most important cultivars are triploids with an AAA, AAB or ABB genome constitution, with A and B genomes provided by M. acuminata and M. balbisiana, respectively. Previous studies suggested that inter-genome recombinations were relatively common in banana cultivars and that triploids were more likely to have passed through an intermediate hybrid. In this study, we investigated the chromosome structure within the ABB group, composed of starchy cooking bananas that play an important role in food security.

METHODS

Using SNP markers called from RADSeq data, we studied the chromosome structure of 36 ABB genotypes spanning defined taxonomic subgroups. To complement our understanding, we searched for similar events within nine AB hybrid genotypes.

KEY RESULTS

Recurrent homologous exchanges (HEs), i.e. chromatin exchanges between A and B subgenomes, were unravelled with at least nine founding events (HE patterns) at the origin of ABB bananas prior to clonal diversification. Two independent founding events were found for Pisang Awak genotypes. Two HE patterns, corresponding to genotypes Pelipita and Klue Teparod, show an over-representation of B genome contribution. Three HE patterns mainly found in Indian accessions shared some recombined regions and two additional patterns did not correspond to any known subgroups.

CONCLUSIONS

The discovery of the nine founding events allowed an investigation of the possible routes that led to the creation of the different subgroups, which resulted in new hypotheses. Based on our observations, we suggest different routes that gave rise to the current diversity in the ABB cultivars, routes involving primary AB hybrids, routes leading to shared HEs and routes leading to a B excess ratio. Genetic fluxes took place between M. acuminata and M. balbisiana, particularly in India, where these unbalanced AB hybrids and ABB allotriploids originated, and where cultivated M. balbisiana are abundant. The result of this study clarifies the classification of ABB cultivars, possibly leading to the revision of the classification of this subgroup.

Unravelling the complex story of intergenomic recombination in ABB allotriploid bananas

BACKGROUND AND AIMS

Bananas (Musa spp.) are a major staple food for hundreds of millions of people in developing countries. The cultivated varieties are seedless and parthenocarpic clones of which the ancestral origin remains to be clarified. The most important cultivars are triploids with an AAA, AAB or ABB genome constitution, with A and B genomes provided by M. acuminata and M. balbisiana, respectively. Previous studies suggested that inter-genome recombinations were relatively common in banana cultivars and that triploids were more likely to have passed through an intermediate hybrid. In this study, we investigated the chromosome structure within the ABB group, composed of starchy cooking bananas that play an important role in food security.

METHODS

Using SNP markers called from RADSeq data, we studied the chromosome structure of 36 ABB genotypes spanning defined taxonomic subgroups. To complement our understanding, we searched for similar events within nine AB hybrid genotypes.

KEY RESULTS

Recurrent homologous exchanges (HEs), i.e. chromatin exchanges between A and B subgenomes, were unravelled with at least nine founding events (HE patterns) at the origin of ABB bananas prior to clonal diversification. Two independent founding events were found for Pisang Awak genotypes. Two HE patterns, corresponding to genotypes Pelipita and Klue Teparod, show an over-representation of B genome contribution. Three HE patterns mainly found in Indian accessions shared some recombined regions and two additional patterns did not correspond to any known subgroups.

CONCLUSIONS

The discovery of the nine founding events allowed an investigation of the possible routes that led to the creation of the different subgroups, which resulted in new hypotheses. Based on our observations, we suggest different routes that gave rise to the current diversity in the ABB cultivars, routes involving primary AB hybrids, routes leading to shared HEs and routes leading to a B excess ratio. Genetic fluxes took place between M. acuminata and M. balbisiana, particularly in India, where these unbalanced AB hybrids and ABB allotriploids originated, and where cultivated M. balbisiana are abundant. The result of this study clarifies the classification of ABB cultivars, possibly leading to the revision of the classification of this subgroup.

High density genotype storage for plant breeding in the Chado schema of Breedbase

Modern breeding programs routinely use genome-wide information for selecting individuals to advance. The large volumes of genotypic information required present a challenge for data storage and query efficiency. Major use cases require genotyping data to be linked with trait phenotyping data. In contrast to phenotyping data that are often stored in relational database schemas, next-generation genotyping data are traditionally stored in non-relational storage systems due to their extremely large scope. This study presents a novel data model implemented in Breedbase (https://breedbase.org/) for uniting relational phenotyping data and non-relational genotyping data within the open-source PostgreSQL database engine. Breedbase is an open-source, web-database designed to manage all of a breeder's informatics needs: management of field experiments, phenotypic and genotypic data collection and storage, and statistical analyses. The genotyping data is stored in a PostgreSQL data-type known as binary JavaScript Object Notation (JSONb), where the JSON structures closely follow the Variant Call Format (VCF) data model. The Breedbase genotyping data model can handle different ploidy levels, structural variants, and any genotype encoded in VCF. JSONb is both compressed and indexed, resulting in a space and time efficient system. Furthermore, file caching maximizes data retrieval performance. Integration of all breeding data within the Chado database schema retains referential integrity that may be lost when genotyping and phenotyping data are stored in separate systems. Benchmarking demonstrates that the system is fast enough for computation of a genomic relationship matrix (GRM) and genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays, 314 triploid Musa acuminata, and 924 diploid Manihot esculenta samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively.

BrAPI-an application programming interface for plant breeding applications

Motivation

Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge.

Results

To facilitate interoperability among breeding applications, we present the public plant Breeding Application Programming Interface (BrAPI). BrAPI is a standardized web service API specification. The development of BrAPI is a collaborative, community-based initiative involving a growing global community of over a hundred participants representing several dozen institutions and companies. Development of such a standard is recognized as critical to a number of important large breeding system initiatives as a foundational technology. The focus of the first version of the API is on providing services for connecting systems and retrieving basic breeding data including germplasm, study, observation, and marker data. A number of BrAPI-enabled applications, termed BrAPPs, have been written, that take advantage of the emerging support of BrAPI by many databases.

Availability and implementation

More information on BrAPI, including links to the specification, test suites, BrAPPs, and sample implementations is available at https://brapi.org/. The BrAPI specification and the developer tools are provided as free and open source.

Benchmarking database systems for Genomic Selection implementation

MOTIVATION

With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems.

RESULTS

We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix.

AVAILABILITY

http://gobiin1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse.

Gigwa v2-Extended and improved genotype investigator

Background

The study of genetic variations is the basis of many research domains in biology. From genome structure to population dynamics, many applications involve the use of genetic variants. The advent of next-generation sequencing technologies led to such a flood of data that the daily work of scientists is often more focused on data management than data analysis. This mass of genotyping data poses several computational challenges in terms of storage, search, sharing, analysis, and visualization. While existing tools try to solve these challenges, few of them offer a comprehensive and scalable solution.

Results

Gigwa v2 is an easy-to-use, species-agnostic web application for managing and exploring high-density genotyping data. It can handle multiple databases and may be installed on a local computer or deployed as an online data portal. It supports various standard import and export formats, provides advanced filtering options, and offers means to visualize density charts or push selected data into various stand-alone or online tools. It implements 2 standard RESTful application programming interfaces, GA4GH, which is health-oriented, and BrAPI, which is breeding-oriented, thus offering wide possibilities of interaction with third-party applications. The project home page provides a list of live instances allowing users to test the system on public data (or reasonably sized user-provided data).

Conclusions

This new version of Gigwa provides a more intuitive and more powerful way to explore large amounts of genotyping data by offering a scalable solution to search for genotype patterns, functional annotations, or more complex filtering. Furthermore, its user-friendliness and interoperability make it widely accessible to the life science community.

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

Rice Galaxy: an open resource for plant science

BACKGROUND

Rice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high-density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties, and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high-density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci discovery and molecular marker development. Comparative sequence analyses across quantitative trait loci regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.

FINDINGS

The Rice Galaxy resource has shared datasets that include high-density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from 9 published rice genomes. The Rice Galaxy web server and deployment installer includes tools for designing single-nucleotide polymorphism assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.

CONCLUSIONS

Rice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

The Rise and Fall of African Rice Cultivation Revealed by Analysis of 246 New Genomes

African rice (Oryza glaberrima) was domesticated independently from Asian rice. The geographical origin of its domestication remains elusive. Using 246 new whole-genome sequences, we inferred the cradle of its domestication to be in the Inner Niger Delta. Domestication was preceded by a sharp decline of most wild populations that started more than 10,000 years ago. The wild population collapse occurred during the drying of the Sahara. This finding supports the hypothesis that depletion of wild resources in the Sahara triggered African rice domestication. African rice cultivation strongly expanded 2,000 years ago. During the last 5 centuries, a sharp decline of its cultivation coincided with the introduction of Asian rice in Africa. A gene, PROG1, associated with an erect plant architecture phenotype, showed convergent selection in two rice cultivated species, Oryza glaberrima from Africa and Oryza sativa from Asia. In contrast, a shattering gene, SH5, showed selection signature during African rice domestication, but not during Asian rice domestication. Overall, our genomic data revealed a complex history of African rice domestication influenced by important climatic changes in the Saharan area, by the expansion of African agricultural society, and by recent replacement by another domesticated species.

MGIS: managing banana (Musa spp.) genetic resources information and high-throughput genotyping data

Unraveling the genetic diversity held in genebanks on a large scale is underway, due to advances in Next-generation sequence (NGS) based technologies that produce high-density genetic markers for a large number of samples at low cost. Genebank users should be in a position to identify and select germplasm from the global genepool based on a combination of passport, genotypic and phenotypic data. To facilitate this, a new generation of information systems is being designed to efficiently handle data and link it with other external resources such as genome or breeding databases. The Musa Germplasm Information System (MGIS), the database for global ex situ-held banana genetic resources, has been developed to address those needs in a user-friendly way. In developing MGIS, we selected a generic database schema (Chado), the robust content management system Drupal for the user interface, and Tripal, a set of Drupal modules which links the Chado schema to Drupal. MGIS allows germplasm collection examination, accession browsing, advanced search functions, and germplasm orders. Additionally, we developed unique graphical interfaces to compare accessions and to explore them based on their taxonomic information. Accession-based data has been enriched with publications, genotyping studies and associated genotyping datasets reporting on germplasm use. Finally, an interoperability layer has been implemented to facilitate the link with complementary databases like the Banana Genome Hub and the MusaBase breeding database.

Database URL:https://www.crop-diversity.org/mgis/

Gigwa-Genotype investigator for genome-wide analyses

BACKGROUND

Exploring the structure of genomes and analyzing their evolution is essential to understanding the ecological adaptation of organisms. However, with the large amounts of data being produced by next-generation sequencing, computational challenges arise in terms of storage, search, sharing, analysis and visualization. This is particularly true with regards to studies of genomic variation, which are currently lacking scalable and user-friendly data exploration solutions.

DESCRIPTION

Here we present Gigwa, a web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data by filtering it not only on the basis of variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability properties. Gigwa can handle multiple databases and may be deployed in either single- or multi-user mode. In addition, it provides a wide range of popular export formats.

CONCLUSIONS

The Gigwa application is suitable for managing large amounts of genomic variation data. Its user-friendly web interface makes such processing widely accessible. It can either be simply deployed on a workstation or be used to provide a shared data portal for a given community of researchers.