- Technical Note
- Open Access
- Open Peer Review
Gigwa—Genotype investigator for genome-wide analyses
© The Author(s). 2016
- Received: 12 February 2016
- Accepted: 16 May 2016
- Published: 6 June 2016
The Erratum to this article has been published in GigaScience 2016 5:48
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.
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.
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.
- Genomic variations
- Web interface
With the advent of next-generation sequencing (NGS) technology, thousands of new genomes of both plant and animal organisms have recently become available. Whole exome and genome sequencing, genotyping-by-sequencing and restriction site-associated DNA sequencing (RADseq) are all becoming standard methods to detect single-nucleotide polymorphisms (SNPs) and insertions/deletions (indels), in order to identify causal mutations or study the associations between genetic variations and functional traits [1–4]. As a result, huge amounts of gene sequence variation data are accumulating in numerous species-oriented projects, such as 3000 rice genomes  or 1001 Arabidopsis genomes [6, 7]. In this context, the Variant Call Format (VCF)  has become a convenient and standard file format for storing variants identified by NGS approaches.
Filtering features. Such data can be processed by applications like VCFtools , GATK , PyVCF , VariantAnnotation  or WhopGenome  to query, filter and extract expertized datasets for day-to-day research. However, these tools are limited to command line or programmatic application programming interfaces (APIs) targeted at experienced users, and are not suitable for non-bioinformaticians.
Storage performance. Working with flat files is not an optimal solution in cases where scientists need to establish comparisons across projects and/or take metadata into account. The use of relational databases is still widely prevalent within the range of more integrated approaches. However, such solutions have limitations when managing big data . In computational environments with large amounts of heterogeneous data, the NoSQL database technology [14, 15] has emerged as an alternative to traditional relational database management systems. NoSQL refers to non-relational database management systems designed for large-scale data storage and massively parallel data processing. During the past 5 years, a number of bioinformatics projects have been developed based on NoSQL databases such as HBase [16, 17], Hadoop [18–20], Persevere , Cassandra  and CouchDB .
Sharing capabilities. This aspect is clearly best addressed by providing client/server-based applications, which enable multiple users to work on the same dataset without the need to replicate it for each user. There is, as yet, a considerable lack of web applications able to handle the potentially huge genotyping datasets that are emerging from mass genotyping projects, and which would enable biologists to easily access, query and analyze data online.
Graphical visualization. A number of solutions have been developed for the graphical visualization of genomic variation datasets. Some of these have been integrated into data portals associated with specific projects (e.g. OryzaGenome , SNP-Seek ) and are, therefore, only relevant to a particular community. Generic tools also exist (e.g. vcf.iobio [26, 27], JBrowse ) and may be built upon to create more versatile applications.
The Gigwa application, the name of which stands for ‘Genotype investigator for genome-wide analyses’, aims to take account of all of these aspects. It is a web-based, platform-independent solution that feeds a MongoDB  NoSQL database with VCF or HapMap files containing billions of genotypes, and provides a web interface to filter data in real time. In terms of visualization, the first version includes only an online density chart generator. However, Gigwa supplies the means to export filtered data in several popular formats, thus facilitating connectivity with many existing visualization engines.
‘Genotypes’ - this filter makes it possible to retrieve only variant positions that respect a specified genotyping pattern when considering selected individuals. If no individuals are selected, the application takes them all into account. A dozen predefined options (e.g. all same, at least one heterozygous) are available, covering those cases that are most frequently meaningful.
‘Minimum per-sample genotype quality’ and ‘Minimum per-sample read depth’ - these individual-based filters may be used, in the case of data from VCF files, to set thresholds on the quality (GQ) and depth (DP) fields assigned to genotypes . Individuals that do not meet these criteria are subsequently treated as missing data.
‘Authorized missing data ratio’ - this filter allows a maximum threshold of acceptable missing data among selected individuals to be defined. Its default value is 100 %, that is, accepting all data.
‘Minor allele frequency’ (MAF) - this filter retains only the variant positions for which the MAF calculated on selected individuals falls in the specified range (by default, 0–50 %). It is only applicable to bi-allelic markers.
‘Number of alleles’ - this filter allows specification of the number of known alleles the targeted variants are expected to have.
‘Position’ - this filter restricts the search to variants located in a given range of positions in relation to the reference.
SnpEff widgets - these allow additional filtering on variant effects and gene names for data originating from VCF files that have been annotated with SnpEff . The application automatically detects such additional data and is able to handle both types of annotation field, that is, ‘EFF’ (SnpEff versions prior to 4.1) and ‘ANN’ (SnpEff versions from 4.1 onwards).
additional variant-level attributes or annotations (global attributes related to the variant), on the left of the screen;
on the right of the screen, a box indicating each individual’s genotype, along with genotype-level attributes (e.g. depth, quality). A checkbox allows the display of genotypes for unselected individuals. Any GQ and DP values that are below specified thresholds, and have thus led to a genotype being considered as missing, are highlighted with a red background.
Data export and visualization
The Gigwa application offers seven standardized formats (VCF, Eigenstrat, GFF3, BED, HapMap, DARwin and PLINK) in which to export filtered results in compressed files. Export is individual-based. Thus, if the data selection includes several samples that belong to the same individual, only one genotype per variant is exported. If these genotypes are inconsistent, the one most frequently found is selected. If there is no most frequently found genotype, one is picked at random.
Where data that originated from VCF files is being re-exported in the same format, the application takes phased genotypes into account: a procedure was implemented to maintain phasing information (i.e. haplotype estimation) in the database and recalculate it at export time, even if intermediate positions had been filtered out.
Exports can be directed either to the client computer or to a temporary URL on a web server, thus making the dataset instantly shareable, for example, with Galaxy . Such links remain active for a week. In addition, when applied to the VCF format, this ‘export to URL’ feature provides the means for users to view selected variants in their genomic context in a running instance of the Integrative Genomics Viewer (IGV) .
The current selection may also be directed to an online, interactive, density chart viewer. The variant distribution of each sequence may then be observed, with the ability to filter on variation type, and these figures can also be exported in various file formats (i.e. PNG, JPEG, PDF and SVG).
Third-party software involved
The data model for storing genotyping information, defined using Spring Data documents, is shared with the WIDDE application  and allows a single database to hold genotypes from multiple runs of multiple projects. This model is marker-oriented and mainly relies on two basic document types: VariantData, which embeds variant-level information (e.g. position, marker type); VariantRunData, which contains genotyping data along with possible metadata.
A collection named taggedVariants is not tied to a model object because its documents only contain variant IDs. However, it serves an important purpose by providing dividers (‘landmarks’) that partition the entire collection of variants into evenly sized chunks. These chunks are then used when querying directly on the VariantRunData collection (i.e. without a preliminary filter on variant features) to split the query into several sub-queries, which confers several advantages (see Querying strategy below).
Less significant model objects include GenotypingProject, which keeps track of elements used to rapidly build the interface (e.g. distinct lists of sequence names and variant types involved in the project), and DBVCFHeader, which simply stores the contents of headers for runs imported in VCF format.
When the Search button is clicked, the values selected in the search-interface widgets are passed to the server application. They may then be used to count and/or browse matching variants.
The first time a given combination of filters is invoked, the count procedure is launched to establish the number of variants that match the combination. This result is then cached in a dedicated collection so that whenever a user subsequently repeats the same search, the result will be available instantly.
Once the count result has been displayed to the user, if the ‘Enable browsing and exporting’ box is checked, a second request is sent to the server, invoking the find procedure that eventually provides paginated, detailed variant information in the form of a comprehensive table.
a simple, preliminary query of variant features (variant type, sequence, start position), which is applied to indexed fields and therefore executes quickly;
the main aggregation query, which is split into several partial queries aimed at running in simultaneous threads on evenly sized variant chunks of the VariantRunData collection. This technique not only improves performance, but also allows Gigwa to provide a progress indicator and the facility to terminate a run before it has finished. The method used for dividing the main query depends on whether or not a preliminary filter was executed beforehand. If it was, the application holds a subset of variant IDs as a consequence, which it uses to split the data using MongoDB’s $in operator in each sub-query. Otherwise, the contents of the taggedVariants collection are used, in conjunction with the $lte (less than or equal) and $gt (greater than) operators, to define the limits of each sub-query’s chunk.
Summary of features
Support for large genotyping files with up to several million variants
Responsive queries even in the case of a local deployment
Intuitive graphical user interface allowing the definition of precise queries in a few clicks
Filtering on functional annotations
Ability to abort running queries
Display of query progress
Support of multiple data sources for a single instance
A multi-user mode which enables both public and private access to databases to be defined
Support for incremental data loading
Support for seven different export formats
Easy connection with IGV for integration within a consistent genomic context
No loss of phasing information when provided (VCF format only)
Support for haploid, diploid and polyploid data
Online variant density viewing.
The Gigwa application therefore represents a very efficient, versatile and user-friendly tool for users with standard levels of expertise in web navigation to explore large amounts of genotyping data, identify variants of interest and export subsets of data in a convenient format for further analysis. We believe that its large panel of undoubtedly useful features will make Gigwa an essential tool in the increasingly complex field of genomics.
In order to assess Gigwa’s performance, we conducted benchmarks against comparable applications.
two quad-core CPUs (Core-i7 L5520 at 2.26 GHz);
36 GB RAM (DDR3 at 1333 MHz);
250-GB SATA2 hard drive.
As our base dataset, we chose to use the CoreSNP dataset from the 3000 Rice Genomes Project [5, 38], which at the time of download (v2.1) contained genotypes for 3000 individuals on 365,710 SNPs. This dataset was first converted to a 4.09-GB VCF file using VCFtools, from which three progressively smaller datasets were then generated by successively dividing the number of variants by ten, i.e. resulting in datasets of 36,571, 3658 and 366 SNPs, respectively.
VCFtools (v0.1.13) ;
A MySQL (v5.6.28)  implementation of a standard relational database model with indexes on appropriate fields. Corresponding queries were implemented as stored procedures, and both these and the database schema are provided as supplementary material within the supporting data.
In addition, the opportunity was taken to evaluate the relative performance of the currently available storage solutions offered by MongoDB v3.0.6, i.e. the newly introduced WiredTiger (WT) storage engine, configured with three different compression levels (none, snappy and zlib), and the original MMapv1 storage engine.
Therefore, each of the benchmarking plots generated contains six series: VCFtools, MySQL, Gigwa-MMapv1, Gigwa-WT-none, Gigwa-WT-snappy and Gigwa-WT-zlib. MongoDB queries were launched via the Gigwa interface because, internally, the application splits them into a number of partial, concurrent queries.
Location-based query: a query counting variants located in a defined region of a chromosome (chromosome 3, 1 Mbp to 5 Mbp).
Genotype-based query: a query counting variants exhibiting a given MAF range (10 to 30 %) on the first 2000 individuals (out of 3000).
All benchmarks were executed three times, except for the MAF queries in Gigwa where the different MongoDB configurations gave response times showing high degrees of heterogeneity. In order to establish more distinction between them, these benchmarks were therefore executed 12 times. In all cases, average response times were calculated and then reported through graphical plots. The caching system implemented in Gigwa was disabled for the duration of the benchmarking.
Thus, Gigwa configured with WiredTiger-snappy (or WiredTiger-zlib in the case of constraints on disk space) appears to be an excellent compromise, being the only solution that responds in a reasonable time to both kinds of query. Furthermore, although it was beyond the scope of this benchmark, we should mention that the greatly reduced storage space required by both WiredTiger-snappy and WiredTiger-zlib, when compared with that required by MMapv1, provides an additional justification for choosing WiredTiger in most cases.
We developed Gigwa to manage large genomic variation data derived from NGS analyses or high-throughput genotyping. The application aims to provide a user-friendly web interface that makes real-time filtering of such data, based on variant features and individuals’ genotypes, widely accessible. Gigwa can be deployed either in single-user mode or in multi-user mode, with credentials and permissions allowing fine-grained control of access to connected databases.
We ran benchmarks on two kinds of queries - variant-oriented and genotype-oriented - to compare Gigwa’s performance with that of both VCFtools and a standard MySQL model. Each of these latter tools performed best in one benchmark but by far the worst in the other. Gigwa, when configured with the WiredTiger storage engine and either the snappy or zlib compression level, appeared as an excellent compromise, performing almost as well as the best solution in both benchmarks.
Future versions of Gigwa will include a RESTful API to allow external applications to interact with Gigwa and query data in a standardized manner, as well as additional visualization tools and a Docker  package aimed at distributing the tool as a solution capable of functioning in platform-as-a-service (PaaS)  mode. Further benchmarks will be conducted to evaluate the application’s performance in a distributed environment using MongoDB’s sharding functionality.
Project name: Gigwa
Project home page: http://www.southgreen.fr/content/gigwa
Operating system(s): Platform-independent
Programming language: Java & MongoDB
Requirements: Java 7 or higher, Tomcat 7 or higher, MongoDB 3 or higher
License: GNU GPLv3
Restrictions to use for non-academics: None
This project was funded by UMR DIADE and Agropolis Fondation under the reference ID ARCAD 0900-001. The authors thank the South Green Platform team for technical support, Sébastien Ravel for testing the annotation filters, Francois Sabot and Mathieu Rouard for meaningful advice, and Aravind Venktesan and Jean-Marc Mienville for careful reading that helped improve the manuscript.
Availability of supporting data
Gigwa’s source code is available in South Green’s public GitHub repository . Supplementary data, benchmarking material and installation archives can be found in the GigaScience GigaDB repository .
MR provided the original idea. GSempéré designed the application logic. GSempéré and FP implemented the software. AD and GSarah generated the benchmark datasets. PL and GSempéré conducted the benchmarks. AD and MR performed beta-testing. PL, GSempéré and AD drafted the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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