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Software Tool Article

MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data

[version 1; peer review: 2 approved with reservations, 1 not approved]
* Equal contributors
PUBLISHED 02 Apr 2015
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This article is included in the Galaxy gateway.

Abstract

The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies have proven to be promising approaches for efficient and unbiased detection of pathogens in complex biological samples, providing access to comprehensive analyses. As NGS approaches typically yield millions of putatively representative reads per sample, efficient data management and visualization resources have become mandatory. Most usually, those resources are implemented through a dedicated Laboratory Information Management System (LIMS), solely to provide perspective regarding the available information.
We developed an easily deployable web-interface, facilitating management and bioinformatics analysis of metagenomics data-samples. It was engineered to run associated and dedicated Galaxy workflows for the detection and eventually classification of pathogens.
The web application allows easy interaction with existing Galaxy metagenomic workflows, facilitates the organization, exploration and aggregation of the most relevant sample-specific sequences among millions of genomic sequences, allowing them to determine their relative abundance, and associate them to the most closely related organism or pathogen.
The user-friendly Django-Based interface, associates the users’ input data and its metadata through a bio-IT provided set of resources (a Galaxy instance, and both sufficient storage and grid computing power). Galaxy is used to handle and analyze the user’s input data from loading, indexing, mapping, assembly and DB-searches. Interaction between our application and Galaxy is ensured by the BioBlend library, which gives API-based access to Galaxy’s main features. Metadata about samples, runs, as well as the workflow results are stored in the LIMS. For metagenomic classification and exploration purposes, we show, as a proof of concept, that integration of intuitive exploratory tools, like Krona for representation of taxonomic classification, can be achieved very easily. In the trend of Galaxy, the interface enables the sharing of scientific results to fellow team members.

Keywords

High Throughput Sequencing, Next-Generation Sequencing, Laboratory Information Management System, Galaxy, Django

Introduction

Background HTS & metagenomics

The detection and characterization of emerging infectious agents has been a continuing public health concern. High Throughput Sequencing (HTS) Next-Generation Sequencing (NGS) technologies have proven to be promising for unbiased detections of pathogens in complex biological samples. They are efficient and provide access to comprehensive analyses.

In most large-scale genomic (re)sequencing initiatives involving both sequencing technology, genotyping expertise and computational analyses, the ultimate goal targets analysis of the data in a reference-free context. Depending on sufficient sequencing throughput and availability of reference genomes, raw sequence reads are to be handled by de novo assembly protocols. The choice of the most appropriate assembly algorithm will both depend on the number of sequenced DNA fragments and the genome size of the targeted species. Most well-acknowledged computational bottlenecks for those short-read assemblers concern memory footprints and difficulties in correctly handling repetitive sequences. Assembly very often results in discontinuous sequence contigs and hence insufficient genome coverage. Currently, de novo assembly yields better coverage for small genomes (i.e. bacterial and/or viral species), though assembly of genomes in a metagenomics setup is nowadays considered as complicated and very challenging. Concerning species with no reference in public databases, pre-processing steps are required to increase genome coverage. For example, the use of paired-end sequence data using different insert size libraries is a well established technique to increase assembly scaffold sizes.

Genotype calling from low coverage data may require extra steps of imputation, filling the gaps that remain due to lack of coverage, and results in more accurate genotypes. Identification of candidate haplotypes and inferring the genotype, by either “phasing” the data to known haplotypes or derivation from external reference panels, allows to better characterize missing genotypes among the individuals.

Bioinformatics and HTS projects

Current NGS platforms including Illumina, Ion Torrent/Life Technologies, Pacific Bioscience and Nanopore can generate reads of 100–10,000 bases long allowing better coverage of the genome at lower cost. However, these platforms also generate huge amounts of raw data. For example, the raw data produced by an Illumina HiSeq-2500 platform adds up to 1TB per run. Sequencing reads are recorded as FastQ formatted files along with the corresponding quality score for each nucleotide.

In addition to those sequence files, it has become important to also consider and store associated sample related metadata (collection date, location, etc.). Thus, NGS projects usually represent such a huge amount of relevant sample-specific sequences that efficient data management and visualization resources have become mandatory. The challenges accompanying HTS technologies raise the following issues: (1) How do we best manage the enormous amount of sequencing data? (2) What are the most appropriate choices among the available computational methods and analysis tools? The issue concerning the growing amount of data can be managed through a dedicated Laboratory Information Management System (LIMS), solely to organize and provide perspective regarding the information contained. The question regarding the lack of adapted intertwining among the wide spectrum of available tools, was in part filled by workflow management systems, even though it still requires fairly advanced knowledge of the tools available at hand.

Indeed, today, hundreds of bioinformatics tools are available, each with specific parameters and each available either through GUI or command lines. Galaxy13, is a scientific workflow management system, which provides means to build multi-step computational data-processing, quality control, and analytic results aggregation, while additionally ensuring analysis reproducibility. In addition to a system for composing pipelines, there is a need for an adapted computational infrastructure capable of doing the processing and data storage in a scalable manner.

MetaGenSense is a managing and analytical bioinformatics framework that is engineered to run dedicated Galaxy workflows for the detection and eventually classification of pathogens. It aims to integrate the capacity for large-scale genomic analysis and technical expertise in sequencing and genotyping technology among project partners. The web application was produced in order to facilitate access to high throughput sequencing analysis tools, acting as an information resource for the project and interacting research partners. This user-friendly interface has been designed to associate bio-IT provider resources (a local Galaxy instance, sufficient storage and grid computing power), with the input data to analyse and its metadata. The use of the available Galaxy tools is automated with MetaGenSense. Galaxy, as a pipeline management software, lets you define workflows and pushes the data through that pipeline. The pipeline manager ensures that all the tools in the pipeline get to run successfully, typically spreading the workload over a computational cluster. MetaGenSense is used at the Pasteur Institute to do the bulk of the data processing for a number of HTS projects, and can be adapted to launch any of the software packages available in the Galaxy workflow designer interface. A dedicated LIMS (postgreSQL-based) was developed to ensure data coherence. In more detail, the web interface design is based on the Django web framework (http://www.djangoproject.com). Moreover, the communication with Galaxy is ensured by the BiobBlend library2 which provides a high-level interface for interacting with the Galaxy application, promoting faster interaction, and facilitating reuse and sharing of scripts.

Software tool - implementation

MetaGenSense global description

MetaGenSense is a bioinformatics application that is geared to ease the scientists’ work in management of NGS project-related data and results. MetaGenSense is built upon three major components, two of which are specific to the project: a dedicated LIMS and a Django-based web user-interface. The third component is Galaxy, which is the bioinformatics workflow management system. In the following paragraphs, we describe the interface’s implementation and discuss how communication between the different parts takes place, in a smooth and user-friendly, managing web-user interface.

A dedicated LIMS

A LIMS can be described as a software-based laboratory that offers a set of key features that support modern laboratory operations. Those systems have become mandatory to manage the quantity of metadata related to both raw data and their analysis results, obtained through bioinformatic tools. In this project, the LIMS is based on a postgresql database. It was designed and structured with expert knowledge from biologists and bioinformaticians with sequencing competence, in order to answer their specific needs ensued by the sample management. Its main feature is that it was also designed to store interesting and worth sharing information obtained by the analysis, as well as the information about the type of workflow that was used to perform the bioinformatics treatment. The database’s schema is available in the Supplementary Figure 1. We provide here an excerpt of the existing tables (divided in three categories): (1) experimental data (LIBRARY_PREPARATION, SAMPLE, TECHNOLOGY, RUN, GEOGRAPHIC_LOCATION, GPS_COORDS), (2) bioinformatic metadata (RAW_DATA, FILE_INFORMATION, WORKFLOW_DATA, RUN_WORKFLOW, WORKFLOW), and (3) user and project data (PROJECT, PROJECT_SUBSCRIBERS, AUTH_USER).

MetaGenSense Django-based web user interface

Django is a high-level Python Web framework. It encourages rapid development and clean, pragmatic design. It is used by many known websites. Moreover, the python language (https://www.python.org/) has become a reference for scientific applications.

MetaGenSense is divided in 4 sub-applications which are: 1) user_management, 2) lims, 3) workflow_remote, 4) analyse. Each has a specific function, and the task-partitioning has been designed to allow independent evolution of each part according to the user’s needs.

  • 1. user_management: manages user authentication. Examples of implementations comprehend communication with an LDAP user authentication database, but it can be used as a user management database.

  • 2. lims: ensures the organization and the data partitioning according to the selected project. A project contains sample metadata, and enables to share them only with selected. This part of the application handles sample traceability, an important component of any present-day core resource laboratory.

  • 3. worflow_remote: is in charge of the communication with Galaxy. It manages: (a) the instance connection, (b) the user histories (c) the data from galaxy libraries, (d) import of data from a data library to a Galaxy user-history instance. (e) Execution of the selected galaxy workflow. This application handles data storage and links the samples to the selected workflow. In practice, this application could access any of the BioBlend functionalities.

  • 4. analyse: deals with the workflow result files. The user can choose to “save” a file in order to share the results with the other users involved in the project. Large data result files can be exported using the Galaxy export functionality or can be downloaded (if the results file can be dealt with through a web browser).

Communication with Galaxy

The following paragraph discusses communication between MetaGenSense and Galaxy. Scientists and data-managers use Galaxy to facilitate bioinformatics analysis. A large number of XML formatted tool-configuration files have already been integrated, facilitating the execution of e.g. a mapping tool like BWA4 through galaxy instead of executing it using the command line.

For programming purposes and in order to interact with Galaxy using command line, the Galaxy team initially implemented a Galaxy-API (which allowed, for example, retrieval of the user list of a Galaxy instance, to create a library for a specific user, etc.,). However, this project was rapidly replaced by a highly dedicated and specific python library called BioBlend5. This API gives access to most Galaxy functionalities through scripts and command lines. We prototyped our instances of BioBlend, and validated each task that MetaGenSense was submitting to Galaxy (Figure 1). At the time of development, specific functionalities were not fully ready to use (e.g. the Tools.run_tools function), which made us interact with the BioBlend development team for concomitant finishing and perfection of the tools and accompanying API.

27f20a0f-8d27-422f-baf8-1c5f3379ffb6_figure1.gif

Figure 1. Communication details between MetaGenSense and Galaxy using BioBlend.

As mentioned earlier, the sub-application workflow_remote from the web interface uses BioBlend functionalities described in Figure 1.

Data management

Everything is integrated and automated except the big data management. Indeed, MetaGenSense senses when new files are copied within the exchange galaxy project directory, but those data need to be copied there using a UNIX terminal or a FileZilla-like solution (https://filezilla-project.org/).

Pre-designed Galaxy workflows

The MetaGenSense project was initially implemented and validated for metagenomic analyses; most of its uses concern two prototyped workflows designed to preprocess raw fastq data, analyse it and determine the taxonomy distribution within the sample. However, any other type of workflow can be associated to the MetaGenSense application. This only requires an admin user and a workflow identifier.

Case study - use example

We exemplify a use-case of MetaGenSense’s use through the analysis of a batch of biological samples for a dedicated project. The necessary steps to obtain a running MetaGenSense instance, with management of project data and analysis using workflows, are the following:

  • 0/ Log onto MetaGenSense.

  • 1/ Creation of a new project, with a name, a context, a short description and (most importantly) the other persons involved in the project.

  • 2/ Start filling the LIMS database. Enter: a. the sample information b. the library sequencing protocol, c. the run details and d. the raw data file list. The raw data will be subjected to bioinformatic analysis.

  • 3/ At this step, the user needs to use a terminal (or a FileZilla-like tool) to connect to its transfer directory. Create a subdirectory named after the project, and copy the raw data in that directory. This protocol enables MetaGenSense to detect (Sense) the files that will be copied into the Galaxy instance and analysed.

  • 4/ Back on the MetaGenSense GUI, the user needs to click on the “Workflows” button, and click on “import new files” button to import into Galaxy the inputs that were transferred at the previous step.

  • 5/ Create a Galaxy history,

  • 6/ select the workflow,

  • 7/ select the workflow input(s),

  • 8/ launch the analysis,

  • 9/ follow the workflow status,

  • 10/ At each step, the user has three choices: If the results file sizes are larger than 2 GB, they can be exported (using the native Galaxy tools), or if the file sizes are smaller than 2 GB, they can be downloaded or saved in the LIMS, be tagged as interesting and shared with other project members.

  • 11/ Visualization of the results by clicking on the “Analyse” button. All workflow inputs as well as LIMS result files are visible on this tab. Krona6 representation can easily be visualized if stored in html files.

Discussion and conclusions

The technology evolution in molecular biology, especially in NGS, has moved biology into the big data era (consisting of handling data, computation requirements, efficient workflow design, and knowledge extraction). With this trend, the challenges faced by life scientists have been shifted from data acquisition to data management, processing, and knowledge extraction. While many studies have recognized the big data challenge, few systematically present approaches to tackle it. New findings in biological sciences usually come out of multi-step data pipelines (workflows). Galaxy is such a workflow-managing tool dealing with big data. However, it is still necessary to globally optimize the data flow in an overall multi-step workflow in order to eliminate unnecessary data movement and redundant computation. On the other hand, data information traceability has become an inevitable requirement in a present-day laboratory setup. In the meantime, knowledge-embedded data and workflows are expected to be an integral part of future scientific publications.

We therefore, engineered MetaGenSense, a Django-based web interface which helps biologists, who are unfamiliar with the design of Galaxy workflows, to quickly obtain analysis results from HTS sequencing projects. It uses Galaxy as a workflow management software and the BioBlend API to remotely manage data upload, workflow execution as well as analysis of results. MetaGenSense covers data processing up to presentation of data and results in a genome browser compatible data format. Its main advantages encompass data handling through its incorporated LIMS, user and project handling in a cooperative context, it enables data sharing without compromising data confidentiality, it features automated workflow execution, resulting altogether in decreasing the data and analysis delivery time. MetaGenSense is available as open-source from GitHub, and can be deployed very easily. Though the prototyped tool is mainly focused on metagenomic sample analysis, its modularity allows it to be easily complemented, through project-specific Galaxy workflows, for a variety of other NGS related initiatives.

Software availability

Source code as at the time of publication

https://github.com/F1000Research/MetaGenSense

Archived source code as at the time of publication

DOI: 10.5281/zenodo.16510

License: GPLv2

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Correia D, Doppelt-Azeroual O, Denis JB et al. MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data [version 1; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2015, 4:86 (https://doi.org/10.12688/f1000research.6139.1)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
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Open Peer Review

Current Reviewer Status: ?
Key to Reviewer Statuses VIEW
ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions
Version 1
VERSION 1
PUBLISHED 02 Apr 2015
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56
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Reviewer Report 24 Jun 2015
Saskia Hiltemann, Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands 
Approved with Reservations
VIEWS 56
General Comments:

The authors describe their application, MetaGenSense, a web-based application for analysing metagenomic data. It provides a user-friendly interface which combines a LIMS system with a Galaxy backend for computation and workflow management.

The Django framework is very nice, and I ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Hiltemann S. Reviewer Report For: MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data [version 1; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2015, 4:86 (https://doi.org/10.5256/f1000research.6578.r8902)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    Thank you very much for your critical review. We posted the answers for your remarks underneath them in bold. 

    To make this work more valuable to the readers, the ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    Thank you very much for your critical review. We posted the answers for your remarks underneath them in bold. 

    To make this work more valuable to the readers, the ... Continue reading
Views
81
Cite
Reviewer Report 22 Jun 2015
Manuel Corpas, Future Business Centre, Cambridge, UK 
Benedikt Rauscher, The Genome Analysis Center, Norwich, UK 
Not Approved
VIEWS 81
MetaGenSense is intended to help find pathogen data in metagenomic data created through next generation sequencing. Measured data including the sequencing reads and metadata are fed into a Laboratory Information Management System (LIMS). The application can fetch that information and ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Corpas M and Rauscher B. Reviewer Report For: MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data [version 1; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2015, 4:86 (https://doi.org/10.5256/f1000research.6578.r8217)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    We tried to answer each of your comments. A second version of the publication will be available soon. 

    Thank you for your critical review of our article.

    Reviewer ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    We tried to answer each of your comments. A second version of the publication will be available soon. 

    Thank you for your critical review of our article.

    Reviewer ... Continue reading
Views
63
Cite
Reviewer Report 19 Jun 2015
Peter Li, GigaScience, Hong Kong SAR, China 
Approved with Reservations
VIEWS 63
General comments

The authors have developed MetaGenSense, a web-based application for analysing metagenomics data. MetaGenSense also acts as a laboratory information management system, managing the metagenomics data and the results of their analysis by Galaxy workflows.

The work done by the authors ... Continue reading
CITE
CITE
HOW TO CITE THIS REPORT
Li P. Reviewer Report For: MetaGenSense : A web application for analysis and visualization of high throughput sequencing metagenomic data [version 1; peer review: 2 approved with reservations, 1 not approved]. F1000Research 2015, 4:86 (https://doi.org/10.5256/f1000research.6578.r8901)
NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article.
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    Thank you for your review. For each of your remarks, we wrote responses in bold:

    For example, it would be interesting to learn more about the two prototyped workflows ... Continue reading
  • Reviewer Response 27 Sep 2016
    Peter Li, GigaScience, Hong Kong SAR, China
    27 Sep 2016
    Reviewer Response
    To test the authors' software, it will take over 6 hours for me to download the 2.7 GB machine image provided by the authors. I think this is due to ... Continue reading
  • Author Response 22 Nov 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Nov 2016
    Author Response
    Dear Peter Li,

    Thank you for your review and remarks.
    Since last August, we asked a non familiar with MetaGenSense colleague to read and install locally the application. This ... Continue reading
COMMENTS ON THIS REPORT
  • Author Response 22 Aug 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Aug 2016
    Author Response
    Thank you for your review. For each of your remarks, we wrote responses in bold:

    For example, it would be interesting to learn more about the two prototyped workflows ... Continue reading
  • Reviewer Response 27 Sep 2016
    Peter Li, GigaScience, Hong Kong SAR, China
    27 Sep 2016
    Reviewer Response
    To test the authors' software, it will take over 6 hours for me to download the 2.7 GB machine image provided by the authors. I think this is due to ... Continue reading
  • Author Response 22 Nov 2016
    Olivia Doppelt-Azeroual, Centre d'Informatique pour la Biologie (CIB), Institut Pasteur, F-75724, Paris, France
    22 Nov 2016
    Author Response
    Dear Peter Li,

    Thank you for your review and remarks.
    Since last August, we asked a non familiar with MetaGenSense colleague to read and install locally the application. This ... Continue reading

Comments on this article Comments (0)

Version 3
VERSION 3 PUBLISHED 02 Apr 2015
Comment
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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