ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

CloudBurst: highly sensitive read mapping with MapReduce | JoVE Visualize

Home
Research Domains
Information And Computing Sciences

CloudBurst: highly sensitive read mapping with MapReduce

Michael C Schatz¹

¹Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA. mschatz@umiacs.umd.edu

Bioinformatics (Oxford, England)|April 10, 2009

View abstract on PubMed

Summary

CloudBurst significantly accelerates next-generation sequencing data analysis by parallelizing read-mapping. This new algorithm dramatically reduces processing time for tasks like SNP discovery and personal genomics.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing generates vast amounts of data, challenging traditional single-processor read-mapping algorithms.
Existing methods struggle to keep pace with the increasing volume of sequence information.

Purpose of the Study:

To develop a parallel read-mapping algorithm optimized for next-generation sequence data.
To enhance the efficiency of genomic analyses such as SNP discovery, genotyping, and personal genomics.

Main Methods:

CloudBurst, a parallel read-mapping algorithm modeled after RMAP.
Utilizes the open-source Hadoop implementation of MapReduce for parallel execution across multiple compute nodes.
Reports all alignments or the best alignment for each read, accommodating mismatches.

Main Results:

CloudBurst exhibits linear scaling with the number of reads and near-linear speedup with increased processors.
Achieved up to 30x speedup compared to RMAP on a single core using 24 cores.
Demonstrated over 100-fold performance improvement on a 96-core cloud, reducing hours to minutes for millions of reads.

Conclusions:

CloudBurst offers a highly efficient solution for mapping next-generation sequencing data to reference genomes.
The algorithm's parallel architecture and MapReduce implementation enable significant speedups for large-scale genomic analyses.
CloudBurst is available open-source as a model for parallelizing algorithms.

Distributed Computing And Systems Software

Cloud Computing

Cloudburst: Highly Sensitive Read Mapping With Mapreduce