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An optimized kraken2 algorithm and its application in next-generation sequencing

A sequencing data and sequence technology, applied in the field of bioinformatics, can solve problems such as sequence error alignment, inaccurate alignment, and short read length, so as to improve accuracy, reduce meaningless detection, and reduce false positive detection Effect

Active Publication Date: 2022-03-25
SIMCERE DIAGNOSTICS CO LTD +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the characteristics of the next-generation sequencing with short read length, it is prone to sequence misalignment, or cannot be accurately aligned (such as a sequence from Streptococcus pneumoniae, which is misaligned to Streptococcus mitis, or can only be aligned to Streptococcus genus level), which is the most important factor affecting the accuracy of species detection

Method used

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  • An optimized kraken2 algorithm and its application in next-generation sequencing
  • An optimized kraken2 algorithm and its application in next-generation sequencing
  • An optimized kraken2 algorithm and its application in next-generation sequencing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0107] The design optimization of embodiment 1 method system

[0108] The problem to be solved in this embodiment is how to ensure the accuracy of the kraken2 comparison results as much as possible through the data analysis method.

[0109] 1. First of all, for this problem, it can be divided into two problems, how to reduce the detection of false positive species to improve specificity, and ensure the detection of real species to obtain higher sensitivity. The two achieve the best balance, that is, get best accuracy. By analyzing how kraken2 produces false positive species results, and the cases where the sensitivity will be reduced.

[0110] a) The redundancy of the database, whether it is refseq, genbank or nt, there is a large amount of reference genome redundancy, which is an important reason for false positive detection, and wrong comparison will also reduce the sensitivity;

[0111] b) Sequence similarity, typically, the sequence degree of Escherichia coli and Shigell...

Embodiment 2

[0177] Comparison of the effects of Example 2 and the traditional kraken2 method

[0178] 1. According to the simulation sample results of the final detection of the optimization method, the false positive and false negative detections are sorted out as follows:

[0179]

[0180] Statistical indicators:

[0181] Sensitivity is 117 / 120 (total number of non-human species)=97.5%;

[0182] Three false positive species were detected.

[0183] 2.2 According to the results of kraken2 confidence 0.5+braken process detection, the false positive detection and false negative detection are sorted out as shown in the following table:

[0184] sample taxi species reads relative abundance result sample 1 340412 Aspergillus novofumigatus 1 0.00011 false positive sample 1 984962 Heterobasidion irregular 1 0.00011 false positive sample 1 145522 Nannochloropsis oceanica 4 0.00044 false positive sample 1 28037 Streptococcus miti...

Embodiment 3

[0190] Embodiment 3 actual sample detection experiment

[0191] Nine spike-in samples were used to establish DNA libraries and RNA libraries for sequencing on the machine. The specific samples and positive species are shown in the table below:

[0192]

[0193]

[0194]

[0195]

[0196]The positive species not detected by the process of the present invention, the positive species not detected by the kraken2 confidence 0.5+bracken process, and the statistics of the positive species not detected by the kraken2+bracken process are shown in the table below (wherein reads_opt, abundance_opt represent the positive species of the process of the present invention Species detection, reads_confidence, abundance_confidence represent positive species detection of kraken2 confidence 0.5+bracken process, reads_kraken, abundance_kraken represent positive detection of kraken2+bracken process):

[0197]

[0198] The total number of positive species is 148, kraken2 confidence 0....

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Abstract

The present invention provides a bioinformatics analysis method based on the kmer score of a single kraken2 sequence and overall taxonomy structure statistics. The method can reduce false positives in bioinformatics analysis, improve the accuracy of species detection, and is suitable for second-generation metagenomic sequencing analysis.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to an optimized kraken2 algorithm and its application in next-generation sequencing. Background technique [0002] The metagenomic community is complex and huge, and a large amount of DNA needs to be sequenced. Illumina next-generation sequencing technology is a massively parallel sequencing technology, which has the characteristics of high throughput, high sequencing accuracy, and short timeliness, which perfectly matches the metagenome. The demand for metagenomics has led to the widespread application of metagenomics in infection detection. [0003] Species detection of microbial communities after sequencing is the most important work in metagenomics research. Only by accurately and reliably locating microbial communities can we associate metagenomics with research, such as studying whether a patient's disease is caused by a certain microbial infection (If a person suspects malaria,...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16B30/10
CPCG16B30/10Y02A50/30
Inventor 张岩李振中任用李诗濛郭昊梁相志陈莉戴岩李珊顾菊
Owner SIMCERE DIAGNOSTICS CO LTD
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