Leon-rc compression method for genome sequencing data

Abstract

The invention relates to a Leon-RC compression method for genome sequencing data, which mainly improves the steps of constructing an anchor dictionary by the LEON algorithm, including the following steps: (1) dividing short reads into multiple Kmers; (2) selecting one Kmer, calculate the Kmer value of its direct repetition, mirror repetition, inverted repetition, and complementary palindrome, compare these four values, and obtain the smallest Kmer value; (3) Put the smallest Kmer value into the Bloom filter for matching Search, Solid Kmer is stored in the Bloom filter, and judge whether there is a minimum Kmer value in Solid Kmer; if it exists, add the minimum Kmer value to the anchor dictionary and end the search; if not, obtain the next One Kmer, repeat steps (2), (3); (4) If the minimum Kmer value of all Kmers does not exist in Solid Kmer, it means that there is no anchor point for this short read; (5) Pass step (1) ~(4) Construct the anchor dictionary.

Description

Technical field [0001] The present invention relates to the field of biological information, and more particularly to the Leon-RC compression method of genomic sequencing data. Background technique [0002] There are two main second-generation sequencing data compression methods: one is based on a compression algorithm based on a reference genome, such as QuIP, CRAM, PATHENC, and FASTQZ, etc., the compressed file stores a map between short reading and reference genome. information. Highly similarity between the homologous species genome, as an example of human beings, the content of any two people's genome is as high as 99%, so, in the case of obtaining the reference genome, if the 1% additional information can be stored, It is possible to store the target genome. [0003] The second-generation sequencing data compression process based on the reference genome is as follows: [0004] (1) Select the appropriate reference genome, and homologous species sequences have advantages due ...

AI Technical Summary

Problems solved by technology

[0016] Most of the compression algorithms based on the reference genome and the compression algorithm without the reference genome have a very low compression rate, because the process of comparing / mapping the original data and the reference genome takes too long

Even though Leon uses the Bloom filter and the De Bruijn graph mosaic algorithm, which can efficiently compare and map the original file and the temporary reference genome with a time complexity of O(1), but in the process of implementation, the second generation The similarity feature of sequencing data is not fully used, and only two cases of direct repetition and complementary palindrome are considered

If only direct repeats and complementary palindrome are considered in the matching process of Kmer and De Bruijn diagrams, but the possible mirror repeats and inverted repeats are not considered, it may cause some short reads not to be found Anchors, so that it cannot be mapped to the provisional reference genome, anchors may also exist, but the time to find anchors is too long

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