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SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop.

André Schumacher1, Luca Pireddu, Matti Niemenmaa

  • 1Aalto University School of Science and Helsinki Institute for Information Technology HIIT, Finland, International Computer Science Institute, Berkeley, CA, USA, CRS4-Center for Advanced Studies, Research and Development in Sardinia, Italy and CSC-IT Center for Science, Finland.

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Summary
This summary is machine-generated.

SeqPig simplifies large sequencing data analysis for bioinformaticians by leveraging Apache Pig for scalable, distributed processing. This library makes complex Hadoop MapReduce tasks accessible without requiring deep Java or Hadoop expertise.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hadoop MapReduce is scalable for large sequencing datasets.
  • Bioinformaticians often lack the necessary Hadoop and Java expertise for these methods.

Purpose of the Study:

  • To develop a user-friendly tool for scalable sequencing data manipulation and analysis.
  • To bridge the expertise gap in utilizing distributed computing for bioinformatics.

Main Methods:

  • Introduced SeqPig, a library and toolset for sequencing data.
  • Utilized Apache Pig, a Hadoop-based scripting engine, for automatic parallelization and distribution.
  • Demonstrated scalability across multiple computing nodes.

Main Results:

  • SeqPig enables scalable and simplified manipulation, analysis, and querying of sequencing data.
  • The tool democratizes the use of distributed computing for bioinformaticians.

Conclusions:

  • SeqPig effectively addresses the complexity barrier of Hadoop MapReduce for sequencing data analysis.
  • The library offers a scalable and accessible solution for bioinformaticians.