Animal mitochondrial genome assembling method based on second-generation data

Abstract

The invention belongs to the technical field of genome sequencing, and particularly relates to an animal mitochondrial genome assembling method based on second-generation data. The assembling method comprises the following steps: firstly, carrying out rapid screening on huge data through a profile HMM to obtain potential mitochondrial coding DNA fragments, then, carrying out assembling acquisition on the fragments by utilizing a linear iterative algorithm, and carrying out scoring by utilizing the profile HMM to obtain a seed sequence; and finally, quickly assembling the mitochondrial DNA reads on the seed sequence from the sequencing data by using a linear iteration algorithm until the mitochondrial whole genome is assembled. According to the method, the seed sequence does not need to be manually delivered, the mitochondrial genome can be rapidly, accurately and completely assembled, and the method has good compatibility for second-generation data of different species, data sizes and DNA reads lengths.

Description

technical field [0001] The invention belongs to the technical field of genome sequencing, and in particular relates to an animal mitochondrial genome assembly method based on second-generation data. Background technique [0002] For animals, mitochondrial DNA is an important marker in biological research because of its maternal inheritance, fast evolution rate, and lack of recombination. It is widely used in phylogeny, evolutionary biology, biodiversity detection, and population genetics. Research areas. Compared with mitochondrial fragments, the whole mitochondrial genome is a more objective and reliable marker than mitochondrial fragments because it is complete and less susceptible to convergence. [0003] With the continuous updating of next-generation sequencing technology, the field of biological research has entered an era of data explosion, and a considerable amount of second-generation data can be obtained for both model animals and non-model animals. Mitochondrial...

AI Technical Summary

Problems solved by technology

The artificially provided seed sequences need to be retrieved from the database (for example, NCBI) for specific species, but since the data in the database are uploaded by researchers themselves, the number of species contained in it is limited, so for many species, in Results may not necessarily be retrieved from the database

Therefore, the requirement to manually provide the seed sequence limits the application of this type of algorithm

[0006] Algorithms (MitoZ and Norgal) that do not need to provide seed sequences take advantage of the higher sequencing depth of mitochondrial data in the second-generation data, and filter the low-depth data for assembly, but this will lead to incomplete assembly results, and Very time consuming due to possible secondary assembly

[0007] In addition to the above problems, the aforementioned algorithm also has the problem of data compatibility

In the previous work of the inventor, he tried to use the software using the above algorithm to process the second-generation data of animal mitochondrial genes, but due to incompatibility of the data, it was always impossible to completely extract the whole mitochondrial genome

In addition, it is difficult to satisfy the assembly speed and integrity of the aforementioned algorithms at the same time.

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