CGtag: complete genomics toolkit and annotation in a cloud-based Galaxy
- Saskia Hiltemann†1, 2Email author,
- Hailiang Mei†3,
- Mattias de Hollander†4,
- Ivo Palli†1,
- Peter van der Spek†1,
- Guido Jenster†2 and
- Andrew Stubbs†1
© Hiltemann et al.; licensee BioMed Central Ltd. 2014
Received: 18 September 2013
Accepted: 18 January 2014
Published: 24 January 2014
Complete Genomics provides an open-source suite of command-line tools for the analysis of their CG-formatted mapped sequencing files. Determination of; for example, the functional impact of detected variants, requires annotation with various databases that often require command-line and/or programming experience; thus, limiting their use to the average research scientist. We have therefore implemented this CG toolkit, together with a number of annotation, visualisation and file manipulation tools in Galaxy called CGtag (Complete Genomics Toolkit and Annotation in a Cloud-based Galaxy).
In order to provide research scientists with web-based, simple and accurate analytical and visualisation applications for the selection of candidate mutations from Complete Genomics data, we have implemented the open-source Complete Genomics tool set, CGATools, in Galaxy. In addition we implemented some of the most popular command-line annotation and visualisation tools to allow research scientists to select candidate pathological mutations (SNV, and indels). Furthermore, we have developed a cloud-based public Galaxy instance to host the CGtag toolkit and other associated modules.
CGtag provides a user-friendly interface to all research scientists wishing to select candidate variants from CG or other next-generation sequencing platforms’ data. By using a cloud-based infrastructure, we can also assure sufficient and on-demand computation and storage resources to handle the analysis tasks. The tools are freely available for use from an NBIC/CTMM-TraIT (The Netherlands Bioinformatics Center/Center for Translational Molecular Medicine) cloud-based Galaxy instance, or can be installed to a local (production) Galaxy via the NBIC Galaxy tool shed.
KeywordsComplete genomics Next generation sequencing Genetic variation Pathogenic gene selection
Complete Genomics (CG) supplies results for whole-genome next-generation sequencing (NGS) data mapped to a user-defined genome  and additional open-source tools  for further characterisation of the sequenced genomes. Whilst these tools are open-source and available for download and use on the command-line, they are not amenable for scientists to use from their desktop, and require scripting skills to link these tools together with other applications to successfully prioritise candidate pathogenic genes based on these NGS results. To address this issue, we implemented the Complete Genomics Analysis Toolkit (CGATools), including several functional annotation and visualisation tools in a cloud-enabled instance of Galaxy. Galaxy offers a web-based graphical user interface to command-line tools, and allows for the graphical construction of complex workflows; Galaxy will automatically keep track of the analysis history, and allows for easy sharing and publishing of data and/or workflows with other users [3–5]. Furthermore, Galaxy is an extensible platform, nearly any software tool may be integrated into Galaxy, and there is an active community of users and developers ensuring the latest tools are made available for use in Galaxy through the Galaxy tool shed.
This implementation of the CGATools in a Galaxy environment simplifies the analysis of genomes via the Galaxy GUI and the cloud resource ensures that sufficient computing power is available for the analysis. The inherent functionality in Galaxy of CGtag enables the creation of customisable user-defined workflows by the scientist and not only by the bioinformatician.
For large datasets, transfer to Galaxy via SFTP is available and recommended, but is still limited by the upload speed of the user’s internet connection, and can be a bottleneck in the analysis of large datasets.
Overview of CGTag tools available in NBIC/CTMM-TraIT Galaxy and the NBIC tool shed
Lists the non-redundant set of small variations found in an arbitrary number of genomes.
Determine which variants are found in which genomes given the results of ListVariants.
Compares two variant files to determine where and how the genomes differ.
Copies the input varfile or masterVar file, applying filters.
Reports difference between junction calls of CG junction files.
Groups and annotate related junctions.
Compares genotype calls to CG variant files.
Merge two tab-delimited files based on equal field or overlapping regions.
Retrieve sequences from a CRR file for a given range of a chromosome.
Converts CG variant and/or junction files to VCF.
Converts a varfile to a on-line-per-locus format.
Converts fasta sequences into a single reference crr file.
Converts crr file to fasta sequence.
CG community tool. Converts output of the TestVariants tool to VCF.
Functional annotation of genetic variants from high-throughput sequencing data.
Functional impact of protein mutations.
CONsensus DELeteriousness score of missense SNVs.
CG Circos plots
Create CG-style tumour, normal and somatic plots.
Create circos plot from CG and SNParray data.
Generic genomic data plotter
Plot any type of numerical genomic data using GNUplot.
Filter tab-delimited files based on column contents.
Add/remove chr prefix
adds or removes chr prefix from chromosome column.
Extract and/or rearrange columns in tab-delimited file.
Sort chromosomal position
Sort a tab-delimited file by chromosomal position.
Remove header from files.
Concatenate 2 files (e.g. for restoring header).
Functional annotation tools
To provide users with enhanced filtering capabilities, we have integrated several command-line annotation tools in this NBIC/CTMM-TraIT Galaxy instance. ANNOVAR  is a command-line tool used to functionally annotate genetic variants. We provide a Galaxy tool wrapper for ANNOVAR. This tool will take a list of variants as input and provide gene and amino acid change annotation, SIFT scores, PolyPhen scores, LRT scores, MutationTaster scores, PhyloP conservation scores, GERP++ conservation scores, DGV variant annotation, dbSNP identifiers, 1000 Genomes Project allele frequencies, NHLBI-ESP 6500 exome project allele frequencies, and other information. We have implemented this tool to accept VCF (v4) files, Complete Genomics varfiles or CG-derived tab-separated files using the CG 0-based half-open coordinate system, or lastly, the standard ANNOVAR input format consisting of tab-separated lists of variants using the 1-based coordinate system. This tool will output the original file columns, followed by additional ANNOVAR columns. The ANNOVAR code itself is not included in the tool shed repository, but instructions on how to obtain a license and the subsequent manual installation of the tool are included in the readme of the Galaxy tool shed repository. We obtained permission to offer ANNOVAR on our public Galaxy server, so the tool can be previewed there. To supplement ANNOVAR, Condel (CONsensus DELeteriousness)  has been included to calculate the deleterious score associated of missense SNVs and the impact of non-synonymous SNVs on protein function. Condel integrates the outputs of two tools: SIFT and Polyphen2, to calculate a weighted average of the scores (WAS) of these tools. Condel can optionally incorporate the output of a third tool, MutationAssessor, which is also included in this Galaxy instance. Mutation Assessor  is a web-based tool providing predictions of the functional impact of amino-acid substitutions in proteins, such as mutations discovered in cancer or missense polymorphisms. The MutationAssessor database is accessed through a REST API. In order not to overload the server, queries are limited to 3 per second, so when dealing with a long list of variants, some pre-filtering is recommended. The functional annotation provided by ANNOVAR, including the addition of multiple versions of dbSNP, the variants provided by Complete Genomics Public data from unrelated individuals only  and 31 genomes from Huvariome , are available in this Galaxy instance. Huvariome provides the user with additional whole genome variant calls for those regions which are difficult to sequence and can retrieve the weighted allele frequency for each base in the human genome .
In addition to these tools within Galaxy, structural variation files processed using CGtag may be exported to our previously described fusion gene prioritisation tool, iFUSE  to identify candidate fusion genes and display their representative DNA, RNA and protein sequence.
Our suite of tools also includes several auxiliary tools supplied by CG but not available from the Galaxy tool shed which offer the user several file format conversion tools (Table 1) that enable users to connect the output from the CGATools analysis to other analytical or annotation workflows by means of standard file formats (e.g., FASTA, VCF). In addition a number of file formatting tools are also included, such as removing of headers from files (required by some tools), adding removing of a chr prefix to a column of a file (i.e., chrX vs. X), concatenation of files, and extracting and rearranging of columns, to help facilitate the flow of data from one tool to the next.
NBIC Galaxy is hosted at a high performance computing (HPC) cloud system operated by SURFsara . This HPC cloud consists of 19 fast servers with 608 CPUs and almost 5TB of memory. The NBIC Galaxy that operates in this HPC cloud is implemented using the Cloudman framework  and its adapted version supports the OpenNebula Cloud environment. The advantage of using the Cloudman framework to build NBIC Galaxy is mainly two-fold, firstly Cloudman provides a set of complete scripts to automatically install tools and datasets on a virtual machine image. The installed tools include the Galaxy system itself and all its dependencies. These dependencies include webserver (nginx), database (postgres), cluster job scheduler (SGE), and common NGS tools, such as bowtie, BWA, samtools, and so forth. The installed datasets include most of the common reference genomes (hg18, hg19, mm9, etc) and their tool-specific index files. Thus, the end product of running Cloudman installation script is a fully functional NBIC Galaxy system operating in the HPC Cloud.
The second contribution of Cloudman to our NBIC Galaxy system is its ability to set up a flexible virtual cluster and ability to provide auto-scaling support. The previous NBIC Galaxy was hosted on a dedicate physical server with rather limit resources (4 CPU, 32G memory). Due to this resource limitation, our NBIC Galaxy was never promoted to be a real data analysis server to handle the production level of NGS datasets. On the other hand, because of the sporadic nature of user access, the server was mostly on idle during its 2-year lifespan. Moving to Cloud resolved both issues. The current NBIC Galaxy operates on top of a virtual cluster. This virtual cluster contains one head node and a number of worker nodes. These nodes are all virtual machines that are built using the machine image generated by the Cloudman script. During minimal usage, the cluster will only contain one head node. Once a significant load occurs due to training courses or production level data analysis, the virtual cluster can automatically scale itself upwards. More worker nodes will be added dynamically to this virtual cluster to boost the capacity of NBIC Galaxy. Once the load decreases, the virtual cluster can scale down again to operate with only a limited number of nodes.
The use of shared resources does have drawback as well. We have experienced a more obvious I/O bottleneck in the cloud-based NBIC Galaxy compared to the previous system that ran in a physical machine. In the HPC Cloud, storage is provided through a network file system (NFS) instead of a local hard disk. When more concurrent Cloud users are using the Cloud resource, we observe the extra job time caused by I/O delays. However, we argue that this issue is far outweighed by the benefit of having a dynamic virtual cluster support to the NBIC Galaxy.
Availability and requirements
Project Name: CGtag: Complete Genomics Toolkit and Annotation in a Cloud-based Galaxy Project home page: http://galaxy.ctmm-trait.nl Operating system: Linux (Galaxy and CGtag)Programming language: Python (Galaxy and CGtag), R (CGtag), Bash (CGTag) Other requirements: Circos , GNUplot , Complete Genomics open source Toolkit  and dependencies therein); see documentation for a comprehensive list of optional dependencies, based on workflow requirements.License: GPL v3Restrictions to use by non-academics: ANNOVAR license must be obtained before it can be used.Galaxy resources: published page: http://galaxy.ctmm-trait.nl/u/saskia-hiltemann/p/cgtag Links to tool shed repositories:annovar: http://toolshed.nbic.nl/view/saskia-hiltemann/annovar cgatools: http://toolshed.nbic.nl/view/saskia-hiltemann/cgatools\_v17 circos plotters: http://toolshed.nbic.nl/view/saskia-hiltemann/cg\_circos_plots condel: http://toolshed.nbic.nl/view/saskia-hiltemann/condel file manipulation tools: http://toolshed.nbic.nl/view/saskia-hiltemann/file_manipulation generic genomic data plotter: http://toolshed.nbic.nl/view/saskia-hiltemann/genomic_data_plotter mutation assessor: http://toolshed.nbic.nl/view/saskia-hiltemann/mutation\_assessor NOTE: these tools can be installed to both Cloudman Galaxy instances or non-Cloudman Galaxy instances alike (via the tool shed or manually from the command line).
Availability and supporting data
Complete genomics analysis tools
Complete genomics toolkit and annotation in a cloud-based galaxy
The Netherlands Bioinformatics Center
Network file system
Next generation sequencing
Single nucleotide variation
This study was performed within the framework of the Center for Translational Molecular Medicine (CTMM). TraIT project (grant 05T-401).
This work was sponsored by the BiG Grid project for the use of the computing and storage facilities, with financial support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research, NWO).
Note from the Editors
This paper is part of the GigaScience Galaxy series. We will be hosting some of the computational resources of these papers on our GigaGalaxy server (http://galaxy.cbiit.cuhk.edu.hk).
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