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Alignment of high-throughput sequencing data inside in-memory databases.

Daniel Firnkorn1, Petra Knaup-Gregori1, Justo Lorenzo Bermejo1

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SAP HANA

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Database Technology

Background:

  • High-throughput DNA sequencing generates vast amounts of data, necessitating efficient analysis methods.
  • Current computer-supported DNA sequence analysis is time-consuming.
  • Read alignment is a critical initial step in DNA sequence analysis.

Purpose of the Study:

  • To evaluate the performance of SAP's High Performance Analytic Appliance (HANA), an in-memory database, for DNA sequence analysis.
  • To compare the execution time and memory management of HANA against MySQL for read alignment using the Burrows-Wheeler Aligner (BWA).

Main Methods:

  • Implemented stored procedures for exact and inexact DNA read searching within the GRCh37 reference genome.
  • Compared performance between SAP HANA and MySQL (running in-memory) using the Burrows-Wheeler Aligner (BWA).
  • Focused on exact search due to technical limitations in SAP HANA for recursion-based inexact matching.

Main Results:

  • SAP HANA demonstrated approximately 27 times faster execution time compared to MySQL for exact DNA read searching.
  • In-memory database technology shows significant potential for accelerating DNA analysis tasks.

Conclusions:

  • In-memory database technology, exemplified by SAP HANA, offers substantial performance improvements for DNA sequence analysis.
  • This approach holds promise for future advancements in computational biology and bioinformatics.