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      A case study for cloud based high throughput analysis of NGS data using the globus genomics system

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          Abstract

          Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.

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          RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

          Bo Li, Colin Dewey (2011)
          Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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            Manipulation of FASTQ data with Galaxy

            Daniel Blankenberg, Assaf Gordon, Gregory Von Kuster … (2010)
            Summary: Here, we describe a tool suite that functions on all of the commonly known FASTQ format variants and provides a pipeline for manipulating next generation sequencing data taken from a sequencing machine all the way through the quality filtering steps. Availability and Implementation: This open-source toolset was implemented in Python and has been integrated into the online data analysis platform Galaxy (public web access: http://usegalaxy.org; download: http://getgalaxy.org). Two short movies that highlight the functionality of tools described in this manuscript as well as results from testing components of this tool suite against a set of previously published files are available at http://usegalaxy.org/u/dan/p/fastq Contact: james.taylor@emory.edu; anton@bx.psu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              A survey of tools for variant analysis of next-generation genome sequencing data

              Stephan Pabinger, Andreas Dander, Maria Fischer … (2013)
              Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.
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                Author and article information

                Contributors
                Subha Madhavan
                Journal
                Journal ID (nlm-ta): Comput Struct Biotechnol J
                Journal ID (iso-abbrev): Comput Struct Biotechnol J
                Title: Computational and Structural Biotechnology Journal
                Publisher: Research Network of Computational and Structural Biotechnology
                ISSN (Electronic): 2001-0370
                Publication date PMC-release: 07 November 2014
                Publication date Collection: 2015
                Publication date (Electronic): 07 November 2014
                Volume: 13
                Pages: 64-74
                Affiliations
                [a ]Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC 20007, USA
                [b ]Computation Institute, University of Chicago, Argonne National Laboratory, 60637, USA
                [c ]Globus Genomics, USA
                Author notes
                [* ]Corresponding author at: Innovation Center for Biomedical Informatics (ICBI), Georgetown University Medical Center, 2115 Wisconsin Ave NW, Suite 110, Washington, DC 20007, USA. sm696@ 123456georgetown.edu
                Article
                Publisher ID: S2001-0370(14)00042-7
                DOI: 10.1016/j.csbj.2014.11.001
                PMC ID: 4720014
                PubMed ID: 26925205
                SO-VID: ff43bb3d-ef36-449a-a26f-124d69209e86
                Copyright © © 2014 Bhuvaneshwar et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology.
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

                History
                Date received : 29 August 2014
                Date revision received : 31 October 2014
                Date accepted : 3 November 2014
                Categories
                Subject: Research Article

                Keywords: next generation sequencing,galaxy,cloud computing,translational research
                Data availability:
                Keywords: next generation sequencing, galaxy, cloud computing, translational research

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