A mutation detection method based on cloud computing platform spark

Abstract

The invention discloses a variation detection method based on a cloud computing platform Spark. The method comprises the steps that firstly, a Spark node intercepts part of an input sequence comparison mapping format file and distributes the format file to each Spark working node; secondly, the Spark working nodes preprocess the fragment sequence comparison mapping format file in parallel, and preprocessing information of the fragment is obtained and returned to the Spark main node; thirdly, the Spark main node performs self-defined granularity division on the input sequence comparison mappingformat file according to the preprocessing information, and distributes the format file to each Spark working node; fourthly, the Spark working nodes conduct variation detection on the fragment sequence comparison mapping format file, and the Spark main node receives data fed back by all the working nodes and writes the data into a file. The method can be used for effectively solving the problemthat a Haplotype Caller variation detection method cannot adapt to multi-node environment scenes or the load is unbalanced under the multi-node condition.

Description

technical field [0001] The invention belongs to the technical field of bioinformatics, in particular to a variation detection method based on a cloud computing platform Spark. Background technique [0002] In recent years, with the in-depth development of the second-generation sequencing technology (High Throughput Sequencing), the time and cost of human individual sequencing have been reduced to a relatively low level. From the original cost of $1 per base to $1,000-$5,000 to sequence a genome, from the time it took 13 years to complete the first human genome map to now only a few weeks, the research on the human genome has entered a low stage. The era of cost-high-throughput sequencing. However, due to the rapid growth of genetic data, massive data also makes traditional gene processing software somewhat powerless. Only when the data acquisition speed matches the processing speed can the advantages brought by high-throughput sequencing technology be better utilized. [0...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies in the prior art, and provides a variation detection method based on cloud computing platform Spark, which can effectively solve the problem that the HaplotypeCaller variation detection method cannot be adapted to multi-node environment scenarios or the load is unbalanced when multi-node

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