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      The MG-RAST API explorer: an on-ramp for RESTful query composition

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          Abstract

          Background

          The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with.

          Results

          We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging.

          Conclusions

          The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.

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          Metagenomics - a guide from sampling to data analysis

          Torsten Thomas, Jack P. Gilbert, Folker Meyer (2012)
          Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared.
          Article 0 comments Cited 269 times – based on 0 reviews     Review now
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            IMG/M: integrated genome and metagenome comparative data analysis system

            I-Min Chen, Victor Markowitz, Ken Chu … (2016)
            The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review (ER) companion system (IMG/M ER: https://img.jgi.doe.gov/mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.
            Article 0 comments Cited 229 times – based on 0 reviews     Review now
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              CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

              Samuel V. Angiuoli, Malcolm Matalka, Aaron Gussman … (2011)
              Background Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. Results We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. Conclusion The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.
              Article 0 comments Cited 117 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Folker Meyer:
                ORCID: http://orcid.org/0000-0003-1112-2284
                folker@anl.gov
                Journal
                Journal ID (nlm-ta): BMC Bioinformatics
                Journal ID (iso-abbrev): BMC Bioinformatics
                Title: BMC Bioinformatics
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2105
                Publication date (Electronic): 8 November 2019
                Publication date PMC-release: 8 November 2019
                Publication date Collection: 2019
                Volume: 20
                Electronic Location Identifier: 561
                Affiliations
                [1 ]ISNI 0000 0001 1939 4845, GRID grid.187073.a, Argonne National Laboratory, ; Lemont, IL USA
                [2 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, University of Chicago, ; Chicago, IL USA
                Author information
                http://orcid.org/0000-0003-1112-2284
                Article
                Publisher ID: 2993
                DOI: 10.1186/s12859-019-2993-0
                PMC ID: 6842160
                PubMed ID: 31703549
                SO-VID: ce323a8e-4fe5-4a2a-8d18-b1e0d27c408d
                Copyright © © The Author(s). 2019
                License:

                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.

                History
                Date received : 11 December 2018
                Date accepted : 11 July 2019
                Funding
                Funded by: National Institutes of Health (US)
                Award ID: 1R01AI123037-01
                Award Recipient :
                Funded by: National Science Foundation (US)
                Award ID: 1645609
                Award Recipient :
                Categories
                Subject: Software
                Custom metadata
                issue-copyright-statement © The Author(s) 2019

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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