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Fragment assignment in the cloud with eXpress-D.

Adam Roberts, Harvey Feng, Lior Pachter1

  • 1Department of Computer Science, 387 Soda Hall, UC Berkeley, Berkeley, CA 94720, USA. lpachter@math.berkeley.edu.

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|December 10, 2013
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Summary
This summary is machine-generated.

This study presents eXpress-D, a scalable cloud-based solution for assigning ambiguously mapped sequencing fragments. It improves accuracy and efficiency for massive high-throughput sequencing data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing generates massive datasets requiring efficient analysis.
  • Probabilistic fragment assignment improves accuracy in RNA-Seq and ChIP-Seq.
  • Existing Expectation-Maximization (EM) methods struggle to scale with large datasets.

Purpose of the Study:

  • To develop a scalable solution for the fragment assignment problem in high-throughput sequencing.
  • To address the limitations of traditional EM algorithms in handling large datasets.
  • To provide an accurate and efficient method for analyzing massive sequencing data.

Main Methods:

  • Implemented a distributed Expectation-Maximization (EM) algorithm using Apache Spark.
  • Leveraged cloud computing resources for scalable data processing.
  • Developed eXpress-D software for fragment assignment.

Main Results:

  • The eXpress-D implementation scales to billions of sequenced fragments.
  • Achieved exact maximum likelihood assignment of ambiguous fragments.
  • Demonstrated improved accuracy compared to existing tools.
  • Showcased constant runtime with linearly scaled cluster resources.

Conclusions:

  • Cloud computing, exemplified by Spark, offers solutions for analyzing massive sequencing data.
  • Bioinformaticians should adopt distributed systems for future-scale datasets.
  • The eXpress-D software is available for public use to facilitate scalable analysis.