CloudBurst is a parallel read mapping algorithm optimized for mapping next-generation sequencing (NGS) data to reference genomes using MapReduce. It models the short read mapper RMAP and can report all alignments or the best alignment for each read with any number of mismatches. CloudBurst uses Hadoop's MapReduce to parallelize the computation across multiple nodes, reducing the running time from hours to minutes for typical jobs involving millions of reads.

1. CloudBurst
• CloudBurst : Highly Sensitive Short Read
Mapping with MapReduce
• New parallel read-mapping algorithm
optimized for mapping NGS data to the
human genome and other reference
genomes
• SNP discovery, genotyping, and personal
genomics

2. CloudBurst
• It is modeled after the short read mapping
program RMAP
• Reports either all alignments or the unambiguous
best alignment for each read with any number of
mismatches or differences
• This level of sensitivity could be prohibitively time
consuming, but CloudBurst uses the open-source
Hadoop implementation of MapReduce to
parallelize execution using multiple compute
nodes.

3. CloudBurst
• Running time
– scales linearly with the number of reads mapped
– with near linear speedup as the number of
processors increases.
• CloudBurst reduces the running time from
hours to mere minutes for typical jobs
involving mapping of millions of short reads to
the human genome.

4. Algorithm Overview
• CloudBurst uses seed-and-extend algorithms to
map reads to a reference genome.
• Seed
– k differences : the alignment must have a region of
length s=r/k+1 called a seed that exactly matches the
reference.
• Extend
– CloudBurst attempts to extend the alignment into an
end-to-end alignment with at most k mismatches or
differences

5. Algorithm Overview
• CloudBurst uses the Hadoop implementation of
MapReduce to catalog and extend the seeds
• Map phase emits
– all length-s k-mers from the reference sequences
– all non-overlapping length-s kmers from the reads
• Shuffle phase
– read and reference kmers are brought together
• Reduce phase
– the seeds are extended into end-to-end alignments

6. Algorithm Overview

7. Demo
Getting Started.docx 참고

9. Related Tools
• Bowtie: Ultrafast short read alignment
• SoapSNP: Accurate SNP/consensus calling
• Tophat: RNA-Seq splice junction mapper
• Cufflinks: Isoform assembly, quantitation
• Hadoop: Open Source MapReduce
• CloudBurst: Sensitive MapReduce alignment
• Crossbow: Read Mapping and SNP calling in the clouds
• Jnomics: Cloud-Scale Sequence Analysis
• Contrail: Cloud-based de novo assembly
• Myrna: Cloud-Scale differential expression of RNAseq

10. Q&A

11. Figure 1: A MapReduce approach for detecting genetic variants from high-throughput genome sequencing.
출처 : http://www.nature.com/nbt/journal/v30/n3/fig_tab/nbt.2134_F1.html