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Using Apache Spark on genome assembly for scalable overlap-graph reduction.

Alexander J Paul¹, Dylan Lawrence², Myoungkyu Song³

¹Bioinformatics and Computational Biology Program, Saint Louis University, St. Louis, MO, USA.

|October 23, 2019

Summary

This summary is machine-generated.

Scalable Overlap-graph Reduction Algorithms (SORA) offers a new approach for de novo genome assembly, efficiently processing large graphs on distributed systems and single machines. This method enhances speed and accuracy in genomic data analysis.

Keywords:

Apache spark Cloud computing Genome assembly Graph reduction Overlap-layout-consensus

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Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

De novo genome assembly reconstructs a genome from overlapping DNA fragments without a reference.
Current high-throughput sequencing methods generate massive datasets, leading to huge overlap graphs.
Existing techniques face challenges with high memory requirements and parallel computation for large graphs.

Purpose of the Study:

To introduce an innovative algorithmic approach, Scalable Overlap-graph Reduction Algorithms (SORA), for efficient de novo genome assembly.
To address the computational limitations of existing genome assembly methods.

Main Methods:

Developed SORA, an algorithm package utilizing Apache Spark for string graph reduction.
Implemented SORA to perform de novo genome assembly on single machines and distributed platforms.
Leveraged Apache Spark's GraphX and GraphFrames for scalable graph processing.

Main Results:

SORA successfully processed a nearly one billion edge graph on a distributed cloud cluster.
Mid-to-small size graphs were processed efficiently on a single workstation.
Demonstrated linear-scaling performance with increased computing instances.

Conclusions:

SORA provides a scalable and efficient solution for de novo genome assembly.
The algorithm package effectively reduces computational demands for large genomic datasets.
SORA enables faster and more accurate genome assembly across different computing environments.