CN109767812A - Method for detecting tumor peripheries blood sample series of variation - Google Patents
Method for detecting tumor peripheries blood sample series of variation Download PDFInfo
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- CN109767812A CN109767812A CN201811588416.4A CN201811588416A CN109767812A CN 109767812 A CN109767812 A CN 109767812A CN 201811588416 A CN201811588416 A CN 201811588416A CN 109767812 A CN109767812 A CN 109767812A
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Abstract
The present invention relates to a kind of methods for detecting tumor peripheries blood sample series of variation, GeneReader algorithm and SiNVICT algorithm detection tumor peripheries blood sample are respectively adopted simultaneously, the GeneReader algorithm detection obtained result of tumor peripheries blood sample is compared with the SiNVICT algorithm detection obtained result of tumor peripheries blood sample, retain consistent result data as final detection result, the GeneReader algorithm is algorithm obtained from combining the method for having supervision and unsupervised method.Method provided by the present invention for detecting tumor peripheries blood sample series of variation, high sensitivity, suitable for tumor peripheries blood sample, it can delicately detect very much variation information, the accuracy detected that makes a variation is high, substantially true abrupt information will not be missed, whether be credible variation information to effectively filter out false positive mutational site, can meet the needs of practical application well if can judge by many kinds of parameters.
Description
Technical field
The invention belongs to technical field of gene detection, and in particular to one kind is for detecting tumor peripheries blood sample series of variation
Method.
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..
Due to the acquisition of tumor tissues be it is extremely difficult, in recent years detect tumor peripheries blood series of variation (ctDNA) at
For important technical, the variation information and post-operative recovery situation of patient can be also detected to a certain extent by the technology.But outside tumour
The sequencing of all blood (ctDNA) has many differences with tissue sequencing, if sequence is short (being generally shorter than 150bp), aberration rate it is low (thousand/
One) etc..Therefore the variation detection of tumor peripheries blood is not suitable for conventional analysis process, such as official's process of GATK.It needs to this
Special setting new algorithm process and parameter adjustment.
The cell that tissue includes is purer, and detection variation difficulty is relatively low, and the source DNA that blood sample contains is more
Add it is 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.GATK algorithm
Defect is, firstly, being not sensitive enough to detect mutation rate site extremely low in blood.The model parameter that second, GATK are used
It is to be trained using group organization data, is not suitable for blood sample.Third, GATK can use random drop for the high efficiency of algorithm
The mode of (downsampling) is sampled to reduce data volume, and this processing mode can allow staff to miss true mutation letter
Breath.4th, GATK do not have flexile filtration parameter, can not filter out false positive mutation using Multi-parameter Combined Tool well
Site.
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 for detecting tumor peripheries blood sample series of variation 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 for detecting tumor peripheries blood sample series of variation, at the same be respectively adopted GeneReader algorithm and
SiNVICT algorithm detects tumor peripheries blood sample, and GeneReader algorithm is detected the obtained result of tumor peripheries blood sample
It is compared with the SiNVICT algorithm detection obtained result of tumor peripheries blood sample, retains consistent result data as most
Whole testing result.
Further, the GeneReader algorithm is that the method for having supervision and unsupervised method are combined and obtained
Algorithm.
Further, when detecting tumor peripheries blood sample using GeneReader algorithm, when discovery insertion and deletion mutation
When situation, 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, scans the local sequence near soft slice with unsupervised method to search more insertions and missing
Mutation.
Further, the local sequence near soft slice is scanned with unsupervised method to search more insertions and missing
The step of mutation includes: to search consensus sequence from the soft Slice Sequence that allele group position is sheared, if found shared
Sequence, then using it come it is customized apart from it is interior search whether there is or not matched sequences；It is found when in the position far from Slice Sequence
When matching 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,
It is determined as detecting the mutation of insertion type.
Further, the customized distance is 125bp.
Further, for obtained potential mutation as a result, being distributed the Heuristic Model with Poisson distribution in conjunction with Bayes
It screens, and obtains most reliable mutational site.
Further, when detecting potential variation information, it is to judge by many kinds of parameters using GeneReader algorithm
No is credible variation information, and many kinds of parameters includes: lowest depth, minimum support mutation count and chain deviation.
Further, when detecting tumor peripheries blood sample using SiNVICT algorithm, SiNVICT algorithm is first with Poisson
Distributed model detects potential abrupt information, does in conjunction with more screening parameters as hard as filter.
Method provided by the present invention for detecting tumor peripheries blood sample series of variation, high sensitivity are suitable for tumour
Peripheral blood sample can delicately detect variation information very much, and the accuracy for the detection that makes a variation is high, will not miss substantially true prominent
Become information, whether be credible variation information to effectively filter out false positive mutational site, can if can judge by many kinds of parameters
To meet the needs of practical application well.
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 for detecting tumor peripheries blood sample series of variation, at the same be respectively adopted GeneReader algorithm and
SiNVICT algorithm detects tumor peripheries blood sample, and GeneReader algorithm is detected the obtained result of tumor peripheries blood sample
It is compared with the SiNVICT algorithm detection obtained result of tumor peripheries blood sample, retains consistent result data as most
Whole testing result.
In the present invention, gene frequency is more accurately estimated using GeneReader algorithm, optimizes part
The precision of comparison, and accomplished in speed with the linear growth of increase that depth is sequenced.GeneReader algorithm will
There is the method for supervision and algorithm that unsupervised method combines.Gene frequency is for measuring base in a population
Yin Ku enriches the measurement of degree.It is inserted into the sequence length much shorter with deletion mutation than reading, positioned at the centre bit for reading sequence
Set, usually with it is most of compare tool and obtain the gap of sequence be aligned.This mutation, which normally results in, forces mispairing pair, works as pairing
Sequence errors it is too many when will form soft slice, these would generally by other mutation location algorithms and tool ignore and missing inspection, but
They but provide the important evidence of insertion and deletion mutation.
In the method for the invention, using GeneReader algorithm detect tumor peripheries blood sample when, when discovery insertion and
When deletion mutation situation, the sequence of mispairing is read using there is the method for supervision, is added to the base of insertion and deletion mutation
Because in library, Lai Zengjia gene frequency.
When detecting tumor peripheries blood sample using GeneReader algorithm, scanned near soft slice with unsupervised method
For local sequence to search more insertions and deletion mutation, specific steps include: soft to cut from what is sheared in allele group position
Consensus sequence is searched in piece sequence, if consensus sequence can be found, (is defaulted as using it in customized distance
Whether there is or not matched sequences for lookup in 125bp), allow small-scale non-match error at this time；When in the position far from Slice Sequence
It was found that when matching sequence, then it is assumed that detect the mutation of deletion type；When the matching of the end of consensus sequence and soft Slice Sequence phase
When adjacent, that is, it is determined as detecting the mutation of insertion type.
For obtained potential mutation as a result, being screened in conjunction with the Heuristic Model of Bayes's distribution and Poisson distribution, and
Obtain most reliable mutational site.
Detection mutation model uses the Heuristic Model for combining Bayesian model and Poisson distribution model, utilizes
Intelligence, which is adjusted, joins a variety of models to detect variation information.When detecting potential variation information, passed through using GeneReader algorithm more
Whether kind parameter is credible variation information, such as lowest depth, minimum support mutation count, chain deviation to judge, thus effectively
Filter out false positive mutational site.
Tumor peripheries blood sample is detected using SiNVICT algorithm simultaneously.SiNVICT algorithm is first with Poisson distribution model
Potential abrupt information is detected, is done in conjunction with more screening parameters as hard as filter.Same patient can also be using SiNVICT algorithm
Time series analysis, to monitor the post-operative recovery situation of tumor patient.
It will be outside the GeneReader algorithm detection obtained result of tumor peripheries blood sample and SiNVICT algorithm detection tumour
All obtained results of blood sample are compared, and retain consistent result data as final detection result, delete inconsistent
Data, ensure that the high degree of accuracy of testing result, avoid missing variation information to the maximum extent.
Method provided by the present invention for detecting tumor peripheries blood sample series of variation, high sensitivity are suitable for tumour
Peripheral blood sample can delicately detect variation information very much, and the accuracy for the detection that makes a variation is high, will not miss substantially true prominent
Become information, whether be credible variation information to effectively filter out false positive mutational site, can if can judge by many kinds of parameters
To meet the needs of practical application well.
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 (8)
1. a kind of method for detecting tumor peripheries blood sample series of variation, which is characterized in that be respectively adopted simultaneously
GeneReader algorithm and SiNVICT algorithm detect tumor peripheries blood sample, and GeneReader algorithm is detected tumor peripheries blood
The obtained result of sample is compared with the SiNVICT algorithm detection obtained result of tumor peripheries blood sample, retains consistent
Result data as final detection result.
2. the method according to claim 1 for detecting tumour blood sample series of variation, which is characterized in that described
GeneReader algorithm is algorithm obtained from combining the method for having supervision and unsupervised method.
3. the method according to claim 1 for detecting tumour blood sample series of variation, which is characterized in that use
When GeneReader algorithm detects tumor peripheries blood sample, when discovery insertion and deletion mutation situation, using the side for having supervision
Method reads the sequence of mispairing, is added in the gene pool of insertion and deletion mutation, Lai Zengjia gene frequency, with nothing
The method of supervision scans the local sequence near soft slice to search more insertions and deletion mutation.
4. the method according to claim 1 for detecting tumour blood sample series of variation, which is characterized in that with unsupervised
Method the step of scanning the local sequence near soft slice to search more insertions and deletion mutation include: from equipotential base
Because searching consensus sequence in group soft Slice Sequence of position shearing, if finding consensus sequence, using it come customized
Apart from interior lookup, whether there is or not matched sequences；When in the position discovery matching sequence far from Slice Sequence, then it is assumed that detect scarce
Lose the mutation of type；When the matching of the end of consensus sequence is adjacent with soft Slice Sequence, that is, it is determined as detecting insertion type
Mutation.
5. the method according to claim 1 for detecting tumour blood sample series of variation, which is characterized in that described to make by oneself
The distance of justice is 125bp.
6. the method described in -5 for detecting tumor peripheries blood sample series of variation according to claim 1, which is characterized in that needle
To obtained potential mutation as a result, being screened in conjunction with the Heuristic Model of Bayes's distribution and Poisson distribution, and obtain most reliable
Mutational site.
7. the method described in -6 for detecting tumour blood sample series of variation according to claim 1, which is characterized in that detect
When potential variation information, whether be credible variation information, described more if being judged using GeneReader algorithm by many kinds of parameters
Kind parameter includes: lowest depth, minimum support mutation count and chain deviation.
8. the method described in -7 for detecting tumor peripheries blood sample series of variation according to claim 1, which is characterized in that benefit
When detecting tumor peripheries blood sample with SiNVICT algorithm, SiNVICT algorithm is potential prominent first with Poisson distribution model detection
Become information, does in conjunction with more screening parameters as hard as filter.
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| US20180230530A1 (en) * | 2013-12-28 | 2018-08-16 | Guardant Health, Inc. | Methods and systems for detecting genetic variants |
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