CN109337957A - The method for detecting genome multimutation type - Google Patents
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Abstract
The present invention relates to a kind of methods for detecting genome multimutation type, based on BAM file as input file, the step of detecting to tumor tissues sample, detecting the various mutations information of different sample types, detect to tumor tissues sample includes: 1) to detect single nucleotide mutation；2) insertion and deletion is detected；3) detection structure makes a variation；4) detection of complex makes a variation.The method of detection genome multimutation type provided by the invention, insertion and deletion is detected using the GeneReader algorithm that the method and unsupervised method that have supervision combine, optimize the precision of Local Alignment, and the linear growth of increase with sequencing depth is accomplished in speed, detection effect is good, testing result is accurate, can meet the needs of practical application well.
Description
Technical field
The invention belongs to technical field of gene detection, and in particular to a method of detection genome multimutation type.
Background technique
With the technology maturation and price decline of the sequencing of two generations, gene order-checking obtains various extensive on medical domain
Using.By taking the drug of tumour is used with clinical trial as an example, researcher can take cancerous tissue or blood sample to study tool
There are the cancer types of different biomarkers to the mark of validity, the cancer process mechanism shifted and generation of drug
Object, screening tumour early stage or marker of recurrence etc..
Gene has various mutations type, can be generally divided into single nucleotide mutation (SNV), DNA fragmentation insertion
(Insertion) and (Deletion) and increasingly complex copy number variation (Copy number variant) is lacked and is tied
Structure makes a variation (Structural variant).Various cancers type can all have different mutation type features, most of at present flat
Platform and algorithm single can only have good detection effect to a or two kind of mutation type.For example, being most commonly used to tissue SNV at present
The software of detection is GATK-mutect2, which does severe quality correction to sequencing data well, is also largely faced using warp
Bed medical data trains reliable Bayesian model and Markov model to detect SNV variation.But inserting for large fragment
Enter missing, copy number variation and structure variation, the detection effect of GATK is with regard to unsatisfactory.
Since the acquisition of tumor tissues is extremely difficult, the series of variation (ctDNA) for detecting tumor peripheries blood in recent years becomes
Important technical can also detect the variation information and post-operative recovery situation of patient by the technology to a certain extent.But tumor peripheries
Blood (ctDNA) sequencing has many differences with tissue sequencing, and if sequence is short (being generally shorter than 150bp), aberration rate is low (one thousandth)
Deng.Therefore the variation detection of tumor peripheries blood is not suitable for conventional analysis process, such as official's process of GATK.It needs special to this
New algorithm process and parameter adjustment are set.
It is only to be examined using a kind of common GATK algorithm or a kind of algorithm of oneself research and development that algorithm platform is common at present
Survey variation information, but the information that makes a variation has various multiplicity, such as single nucleotide mutation (SNV), DNA fragmentation be inserted into (Insertion) and
Lack (Deletion) and increasingly complex copy number variation (Copy number variant) and structure variation
(Structural variant), single algorithm are the detections for being difficult to take into account all variation types.The detection variation of present mainstream
Algorithm is all that can be very good to identify single nucleotide mutation, and the missing insertion mutation of large fragment and more complicated variation are often
Detection effect is poor, or even is ignored.It is also required to assess various abrupt informations, such as ovary in the guidance of many clinical medicines
HRD marker in cancer, is not only the single nucleotide mutation of detection gene, while the copy number that also detect how many ratio becomes
Different and structure variation event.
For tissue samples and blood sample variation detection, should also treat with a certain discrimination.The cell that tissue includes is purer,
Detection variation difficulty is relatively low, and the source DNA that blood sample contains is more mixed and disorderly, the content of the DNA fragmentation of Tumor mutations
Be it is at a fairly low, need more sensitive algorithm to identify.For GATK algorithm, firstly, being not sensitive enough to detect in blood
Extremely low mutation rate site.The model parameter that second, GATK are used is not suitable for blood sample using group organization data training
This.Third, GATK can reduce data volume with the mode of random down-sampled (downsampling) for the high efficiency of algorithm,
And this processing mode meeting let us misses true abrupt information.4th, GATK do not have flexile filtration parameter, can not be very
False positive mutational site is filtered out using Multi-parameter Combined Tool well.
Summary of the invention
For above-mentioned problems of the prior art, it can avoid above-mentioned skill occur the purpose of the present invention is to provide one kind
The method of the detection genome multimutation type of art defect.
In order to achieve the above-mentioned object of the invention, technical solution provided by the invention is as follows:
A method of detection genome multimutation type, based on BAM file as input file, to tumor tissues sample
It is detected, detects the various mutations information of different sample types.
Further, the step of detecting to tumor tissues sample include:
1) single nucleotide mutation is detected；2) insertion and deletion is detected；3) detection structure makes a variation；4) detection of complex makes a variation.
Further, using in GATK mutect2 and filter algorithm detect single nucleotide mutation；Process includes: pair
Bam file does base mass calibration, secondary comparison, and mutect2 does variation detection.
Further, detection insertion and deletion includes: to estimate gene frequency using GeneReader algorithm；
The algorithm that GeneReader algorithm will have the method for supervision and unsupervised method to combine；When discovery insertion and missing
When mutation, the sequence of mispairing is read using there is the method for supervision, is added in the gene pool of insertion and deletion mutation, come
Increase gene frequency；Unsupervised method is used to scan the local sequence near soft slice to search more insertions and lack
Lose mutation.
Further, scanning the local sequence near soft slice using unsupervised method includes: from allele group position
It sets in the soft Slice Sequence of shearing and searches consensus sequence, if consensus sequence can be found, using consensus sequence customized
Distance in search whether there is or not matched sequence, when in the position discovery matching sequence far from Slice Sequence, then it is assumed that detect
The mutation of deletion type；When the matching of the end of consensus sequence is adjacent with soft Slice Sequence, that is, detect the mutation of insertion type；
To finally there are supervision and the unsupervised mutation result detected to merge, as finally detected insertion and deletion mutation.
Further, the customized distance is 125bp.
Further, with CNVkit and LUMPY come detection structure variation.
Further, complicated variation refers to the combination of insertion and deletion mutation, when detection of complex makes a variation, this combination is prominent
Change is considered as a gene mutation, when detecting one of an insertion or deletion mutation, detects in same sequence
Whether another mutation is had, if it find that, then it is combined, is considered as a kind of combined abrupt.
Further, it is described detection genome multimutation type method further include: based on it is processed at BAM file
As input file, tumor peripheries blood sample is detected, detects the various mutations information of different sample types.
Further, the method for detection tumor peripheries blood sample variation information includes: first using GeneReader algorithm
It is detected, the algorithm that GeneReader algorithm will have the method for supervision and unsupervised method to combine, optimization office
The precision that portion compares utilizes Heuristic Model when detecting mutation model, adjusts ginseng Bayesian model and Poisson point using intelligence
Both models of cloth model detect variation information, detect potential variation information, use many kinds of parameters whether to judge to be credible
Make a variation information.
The method of detection genome multimutation type provided by the invention, using the method and unsupervised method for having supervision
The GeneReader algorithm detection insertion and deletion combined, optimizes the precision of Local Alignment, and do in speed
The linear growth of increase with sequencing depth is arrived, detection effect is good, and testing result is accurate, can meet practical application well
Needs.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, combined with specific embodiments below to this
Invention is described further.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit
The present invention.Based on the embodiments of the present invention, those of ordinary skill in the art are obtained without making creative work
The every other embodiment obtained, shall fall within the protection scope of the present invention.
A method of detection genome multimutation type, based on it is processed at BAM file be used as input file, divide
It is other that tumor tissues sample and tumor peripheries blood sample are detected using different detection variation Integrated Algorithms, it detects simultaneously
The various mutations information of different sample types.
The step of detecting to tumor tissues sample include:
1) single nucleotide mutation (SNV) is detected
For single nucleotide mutation using in GATK mutect2 and filter algorithm come complete single nucleotide mutation detect；
Process includes: to do base mass calibration (BQSR) to bam file, and secondary comparison (IndelRealigner), mutect2 makes a variation
Detection.
2) insertion and deletion is detected
Gene frequency is more accurately estimated using GeneReader algorithm.GeneReader algorithm will have prison
The algorithm that the method and unsupervised method superintended and directed combine.Gene frequency is for measuring gene pool in a population
The measurement of abundant degree.It is inserted into the sequence length much shorter with deletion mutation than reading, positioned at the center for reading sequence, often
Often with it is most of compare tool and obtain the gap of sequence be aligned.This mutation, which normally results in, forces mispairing pair, when the sequence of pairing
Column mistake will form soft slice when too many, these would generally be by other mutation location algorithms and tool ignores and missing inspection, but they
But the important evidence of insertion and deletion mutation is provided.
In the method for the invention, when finding this catastrophe, the sequence of mispairing is read using there is the method for supervision
Column, are added in the gene pool of insertion and deletion mutation, to increase gene frequency.
And unsupervised method is then the local sequence near the soft slice of scanning to search more insertions and deletion mutation.
The specific steps are search consensus sequence from the soft Slice Sequence that allele group position is sheared.If shared sequence can be found
Column are then allowed small-scale using it come whether there is or not matched sequences for lookup in customized distance (being defaulted as 125bp) at this time
Non-match error.When in the position discovery matching sequence far from Slice Sequence, then it is assumed that detect the mutation of deletion type；When
When the end matching of consensus sequence is adjacent with soft Slice Sequence, that is, detect the mutation of insertion type.
To finally there are supervision and the unsupervised mutation result detected to merge, it is prominent as finally detected insertion and missing
Become.
3) detection structure makes a variation
Structure variation includes copy number variation and chromosomal structural variation, belongs to large fragment variation information, general system
Meter learns the method for inspection and is difficult to detect, needs individually to list using independent algorithm and complete.Come used here as CNVkit and LUMPY
Detection structure variation.CNVkit and LUMPY can detect copy number variation, since copy number variation difficulty is larger and each
The result that a algorithm detected also is not quite similar, therefore here considers to weigh using two kinds of algorithms to do.If clinically
Detection uses, and for reliability, two kinds of arithmetic results can be taken intersection.Two can be calculated if it is research new mutation is thought in scientific research then
Method does union.Big segment variation in structure, is detected merely with LUMPY.
4) detection of complex makes a variation
Complexity variation refers to the combination of insertion and deletion mutation, for this variation, current existing most numerical mutation inspection
Survey tool detectability is limited, cannot accurately identify.This combinatorial mutagenesis can be considered as a base by method proposed by the present invention
Because of mutation, when detecting one of an insertion or deletion mutation, this method will detect in same sequence is
It is no to have another mutation, if it find that, then it is combined, is considered as a kind of combined abrupt.
The demand of tumor peripheries blood sample variation detection also increasingly increases, the medicine ginseng of tumor peripheries blood sample variation detection
It is very high to examine value.But the suitable height of the detection difficulty of tumor peripheries blood sample, also detects tumor peripheries blood without professional standard at present
Sample variation information.The step of detecting to tumor peripheries blood sample include:
The method that the present invention detects tumor peripheries blood sample variation information includes: to be carried out first using GeneReader algorithm
Detection, the algorithm that GeneReader algorithm will have the method for supervision and unsupervised method to combine optimize part
The precision of comparison, and accomplished in speed with the linear growth of increase that depth is sequenced.It is sharp when detecting mutation model
With Heuristic Model, ginseng Bayesian model and both models of Poisson distribution model are adjusted to detect variation information using intelligence.Inspection
When measuring potential variation information, there are the method for supervision and unsupervised method to use many kinds of parameters whether to judge for credible variation
Information, such as lowest depth, minimum support mutation count, chain deviation.
In conjunction with SiNVICT algorithm, SiNVICT algorithm is exclusively for algorithm designed by peripheral blood sample.This algorithm utilizes
Poisson distribution model first can sensitively detect potential abrupt information, do in conjunction with more screening parameters as hard as filter.This algorithm
Same patient's time series analysis can also be done, to monitor the post-operative recovery situation of tumor patient.
Bayesian model and Poisson distribution model this two model have respective characteristic respectively, and Bayesian model accuracy is good, and
Poisson distribution model sensitivity is good.Bayesian model can find out correctly variation information well, but it is also true for omitting many
Real variation information.And Poisson distribution model is on the contrary, the information that largely makes a variation can be detected, but also will detect that simultaneously incorrect
Variation information.The present invention mixes two kinds of models, detects variant sites with heuristic tune ginseng mode, in this way can be well
Weigh accuracy and sensitivity.
It is after being detected using GeneReader algorithm as a result, with obtained result after the detection of SiNVICT algorithm into
Row compares, and chooses in two results identical partial data as final result data.
Compare the 15bp deletion fragment of the 19th exon of EGFR in detection lung cancer cell line PC-9.
| Method | Detect true missing | Overburden depth | Allelic frequency |
| GATK-UnifiedGenotyper | 338 | 496 | 68% |
| GATK-HaplotypeCaller | 404 | 482 | 84% |
| Inventive algorithm | 493 | 575 | 86% |
More most common GATK algorithm can be detected since algorithm of the invention can be handled soft slice
More true missing informations, can preferably assess allelic frequency.
The method of detection genome multimutation type provided by the invention, using the method and unsupervised method for having supervision
The GeneReader algorithm detection insertion and deletion combined, optimizes the precision of Local Alignment, and do in speed
The linear growth of increase with sequencing depth is arrived, detection effect is good, and testing result is accurate, can meet practical application well
Needs.
Embodiments of the present invention above described embodiment only expresses, the description thereof is more specific and detailed, but can not
Therefore limitations on the scope of the patent of the present invention are interpreted as.It should be pointed out that for those of ordinary skill in the art,
Without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection model of the invention
It encloses.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of method for detecting genome multimutation type, which is characterized in that based on BAM file as input file, to swollen
Tumor tissue sample is detected, and the various mutations information of different sample types is detected.
2. the method for detection gene mutation type according to claim 1, which is characterized in that carried out to tumor tissues sample
The step of detection includes:
1) single nucleotide mutation is detected；2) insertion and deletion is detected；3) detection structure makes a variation；4) detection of complex makes a variation.
3. the method for detection gene mutation type according to claim 1, which is characterized in that using in GATK
Mutect2 and filter algorithm detect single nucleotide mutation；Process includes: to do base mass calibration, secondary ratio to bam file
Right, mutect2 does variation detection.
4. the method for detection gene mutation type according to claim 1, which is characterized in that detecting insertion and deletion includes:
Gene frequency is estimated using GeneReader algorithm；GeneReader algorithm will have the method for supervision and unsupervised
The algorithm that method combines；When discovery insertion and deletion mutation, the sequence of mispairing is read using there is the method for supervision,
It is added in the gene pool of insertion and deletion mutation, to increase gene frequency；It is soft using unsupervised method scanning
Local sequence near slice is to search more insertions and deletion mutation.
5. the method for gene mutation type is detected described in -4 according to claim 1, which is characterized in that use unsupervised method
Scanning the local sequence near soft slice includes: to search consensus sequence from the soft Slice Sequence that allele group position is sheared,
If consensus sequence can be found, using consensus sequence it is customized apart from it is interior search whether there is or not matched sequences, when remote
When position discovery from Slice Sequence matches sequence, then it is assumed that detect the mutation of deletion type；When the end of consensus sequence
With it is adjacent with soft Slice Sequence when, that is, detect insertion type mutation；To finally there are supervision and the unsupervised mutation detected
As a result merge, as finally detected insertion and deletion mutation.
6. the method for detection gene mutation type according to claim 1, which is characterized in that the customized distance is
125bp。
7. the method for gene mutation type is detected described in -6 according to claim 1, which is characterized in that use CNVkit and LUMPY
Carry out detection structure variation.
8. the method for gene mutation type is detected described in -7 according to claim 1, which is characterized in that complexity variation refers to insertion
With the combination of deletion mutation, detection of complex make a variation when, this combinatorial mutagenesis is considered as a gene mutation, when detect one insert
Enter or when one of deletion mutation, another mutation has been detected whether in same sequence, if it find that, then by its group
It closes, is considered as a kind of combined abrupt.
9. the method for gene mutation type is detected described in -8 according to claim 1, which is characterized in that the detection genome is more
The method of mutation type further include: based on it is processed at BAM file as input file, tumor peripheries blood sample is carried out
Detection, detects the various mutations information of different sample types.
10. the method for gene mutation type is detected described in -9 according to claim 1, which is characterized in that detection tumor peripheries blood
The method of sample variation information includes: to be detected first using GeneReader algorithm, and GeneReader algorithm will have prison
The algorithm that the method and unsupervised method superintended and directed combine, optimizes the precision of Local Alignment, when detecting mutation model
Using Heuristic Model, ginseng Bayesian model and both models of Poisson distribution model are adjusted to detect variation information using intelligence,
Potential variation information is detected, uses many kinds of parameters whether to judge for credible variation information.
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| CN111462823A (en) * | 2020-04-08 | 2020-07-28 | 西安交通大学 | Homologous recombination defect judgment method based on DNA sequencing data |
| CN111696622A (en) * | 2020-05-26 | 2020-09-22 | 北京吉因加医学检验实验室有限公司 | Method for correcting and evaluating detection result of mutation detection software |
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| CN110846411A (en) * | 2019-11-21 | 2020-02-28 | 上海仁东医学检验所有限公司 | Method for distinguishing gene mutation types of single tumor sample based on next generation sequencing |
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| CN111462823A (en) * | 2020-04-08 | 2020-07-28 | 西安交通大学 | Homologous recombination defect judgment method based on DNA sequencing data |
| CN111462823B (en) * | 2020-04-08 | 2022-07-12 | 西安交通大学 | Homologous recombination defect judgment method based on DNA sequencing data |
| CN111696622A (en) * | 2020-05-26 | 2020-09-22 | 北京吉因加医学检验实验室有限公司 | Method for correcting and evaluating detection result of mutation detection software |
| CN111696622B (en) * | 2020-05-26 | 2023-11-21 | 北京吉因加医学检验实验室有限公司 | Method for correcting and evaluating detection result of mutation detection software |
| CN111793678A (en) * | 2020-07-30 | 2020-10-20 | 臻悦生物科技江苏有限公司 | Method and kit for detecting homologous recombination pathway gene mutation based on next-generation sequencing technology |
| CN114067908A (en) * | 2021-11-23 | 2022-02-18 | 深圳基因家科技有限公司 | Method, device and storage medium for evaluating single-sample homologous recombination defects |
| CN114566214A (en) * | 2022-04-26 | 2022-05-31 | 北京泛生子基因科技有限公司 | Method for detecting genome deletion insertion variation, detection device, computer-readable storage medium and application |
| CN114566214B (en) * | 2022-04-26 | 2022-07-05 | 北京泛生子基因科技有限公司 | Method for detecting genome deletion insertion variation, detection device, computer readable storage medium and application |
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