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Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping, and personal genomics.

It is modeled after the short read mapping program RMAP , and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences.

Ravelry: Cloudburst pattern by Arienne Grey

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. CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments.

In a large remote compute clouds with 96 cores, CloudBurst reduces the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome.

CloudBurst is available open-source as a model for parallelizing other bioinformatics algorithms with MapReduce. CloudBurst uses well-known seed-and-extend algorithms to map reads to a reference genome.


For example, if a 30bp read aligns to a reference with at 1 difference, there must be a substring of at least 15bp that aligns exactly if the first base is different, the remaining 29 must match; if the 2nd base is different then the remaining 28 must match; an so forth. Given an exact seed, CloudBurst attempts to extend the alignment into an end-to-end alignment with at most k mismatches or differences by either counting mismatches of the two sequences, or with a dynamic programming algorithm to allow for gaps.

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CloudBurst uses the Hadoop implementation of MapReduce to catalog and extend the seeds. In the map phase, the map function emits all length-s k-mers from the reference sequences, and all non-overlapping length-s kmers from the reads. In the shuffle phase, read and reference kmers are brought together. In the reduce phase, the seeds are extended into end-to-end alignments. The power of MapReduce and CloudBurst is the map and reduce functions run in parallel over dozens or hundreds of processors. The running time of a very sensitive alignment algorithms, such as CloudBurst, are usually very high because there will be many random seed matches to evaluate.

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However, CloudBurst is designed to scale, and the running time of CloudBurst scales linearly with the number of processors that it runs on, i. Therefore CloudBurst can compute very sensitive mapping quickly even at high sensitivity.

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The country has a cloudburst management plan as part of the Copenhagen climate adaptation plan. Some of the measures suggested under the plan include steps to separate stormwater from wastewater, and diverting drain water to the sea through roads, canals or urban waterways, and subterranean tunnels.

It was estimated in when the plan was developed that the constructions costs are likely be around 3. For Uttarakhand where even the sewage runs down the mountainside, setting up a system to remove water safely would require much more money and effort. Recent cloudburst-induced landslides around Okhimath, Uttarakhand. Simulation of a Himalayan cloudburst event. We are a voice to you; you have been a support to us.

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