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“DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline For High-Throughput Analysis Of Next-Generation Sequencing Data.” Metadata:

  • Title: ➤  DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline For High-Throughput Analysis Of Next-Generation Sequencing Data.
  • Authors: ➤  
  • Language: English

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  • Internet Archive ID: pubmed-PMC3738164

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28DNA%20Research:%20An%20International%20Journal%20for%20Rapid%20Publication%20of%20Reports%20on%20Genes%20and%20Genomes%29" rel="nofollow">DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28DNA%20Research:%20An%20International%20Journal%20for%20Rapid%20Publication%20of%20Reports%20on%20Genes%20and%20Genomes%29%20AND%20volume%3A%2820%29" rel="nofollow">volume 20</a>.<h2>Abstract</h2>High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.

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