CN106778072B - For the process bearing calibration of second generation Oncogenome high-flux sequence data - Google Patents
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Abstract
The invention discloses a kind of process bearing calibrations for second generation Oncogenome high-flux sequence data.This method uses a series of 32 unsigned numbers for mark amount, the variation of corresponding every blood system or somatic variation data are recorded respectively, generate the read data for embodying purity and different subclone proportions, the Variation Relationship of succession, brother's subclone mutual exclusion is subcloned according to father and son, the calibration data of the somatic variation of filial generation subclone and its brother's subclone is obtained, is corrected for the process flow to the two generations Oncogenome high-flux sequence data.
Description
Technical field
The invention belongs to be the decision of the accurate diagnosis and treatment of tumour using accurate medicine as the data science technical field of application background
A set of auxiliary corrective system of support system.
Background technique
Nearly ten years, have benefited from the fast development of high-throughput genome, transcript profile sequencing technologies, cancer genomics and swollen
No matter the accurate diagnosis and treatment of tumor all achieve the achievement to attract people's attention in terms of the depth in medical research or the range in clinical application.
Cancer genomics research and the accurate medicine of tumour all rely on tumour high-flux sequence data.The gene exported from sequenator
Group, transcript profile sequencing data are known as read data (English name is read data), since short and there are sequencing errors, so
It cannot directly be used by tumor research personnel and clinician.Must use some flow chart of data processing by read data into
Row processing is exactly to be come out genetic mutation signal extraction therein by genome informatics algorithm and tool, mentions in simple terms
Data after taking are known as the data that make a variation.Variation data are the data that researcher and doctor can understand.Similar medical test knot
Fruit, variation data are the important reference indicators of clinical diagnosis, multiple in ideas of cancer therapy design, drug screening or recalibration etc.
In committed step, variation data are all important decision foundations.
Due to the limitation of current high throughput sequencing technologies and Oncogenome informatics technology, can not be kept away in the data that make a variation
Exempt from there are false positives and false negative result.Target is to reduce false positive and false negative result as far as possible, improves variation data
Precision improve medication and diagnosis and treatment efficiency in the hope of reducing the probability of misdiagnosis rate and inefficient diagnosis and treatment.For this purpose, (1) is all to be directed to tumour
The process flow of genome high-flux sequence data must all carry out process correction before being put into use；(2) use has been put into
Process when encountering special case, also it is necessary to personalized correct again.The main purpose of process correction is in adjustment process
Parameter setting, be allowed to be consistent as far as possible with the feature of the read data of lower machine, the tumour purity of case, Tumor Heterogeneity feature,
To obtain high-precision variation data.
Have some process bearing calibrations for second generation genome high-flux sequence data at present, but these methods
All without the heterogeneous problem for pointedly considering tumor tissues, so not all being suitable for the processing stream of Oncogenome data
Journey.
The design philosophy of existing method of calibration is to firstly generate one group of variation data, is then based on group variation data
Read data are generated, verification is finally implemented.Although these methods generate variation data and/or read data model on
Difference, but its core concept is substantially completely consistent.According to the similarities and differences of these methods, it is generally segmented into three classes: first
Class concern generate read data when simulate sequenator error, including Wgsim, ART, ArtificialFastqGenerator,
PIRS and Wessim；Second class pays close attention to different types of variation, including SInC and RSVSim[7]It can be realized the structure of genome
Variation；Third class pays close attention to population genome signature, including GENOME, FREGene and FIGG.
It is as follows that existing method is not suitable for the reason of Oncogenome flow chart of data processing:
First, tumor tissues are the mixtures of the tumour cell of normal cell and different subclones.Modern tumour theory
It is believed that tumour cell inherits normally under normal circumstances because tumour cell is developed from normal cell
The variation of cell, while carrying the mutation different from normal cell.Mutation includes two kinds, first is that the variation inherited, i.e. tumour
The variation that cell and normal cell share is known as the variation of blood system；Another is that the exclusive variation of tumour cell is known as somatic variation.
The somatic variation that different tumour cells carries is different.The tumour cell of identical somatic variation is carried, if swollen
Tumor shows selective advantage in developing, then being regarded as a subclone of tumor tissues.Tumor tissues are generally all by counting
A or even ten several subclone compositions.Therefore, the process bearing calibration for second generation genome high-flux sequence data is necessary
The somatic variation data of blood system variation data and each subclone are sequentially generated according to succession and Evolvement.This is existing
What method cannot achieve.
Second, ratio shared by tumour cell is known as tumour purity in tumor tissues, the tumour cell of difference subclone is accounted for
The specific gravity of tumour cell is known as Tumor Heterogeneity proportion.Different types of tumour purity and Tumor Heterogeneity proportion difference are very big.
As previously mentioned, purity and heterogeneous proportion are the important goals of correction.Oncogenome informatics research thinks, there are purity and
The coverage of the tumour sequencing read data of heterogeneity proportion obeys multiple Beta distribution or multiple Dirichlet distribution.However,
If, can only be by the read data investigation based on single variation data, thus obtained read data clothes using existing method
It is uniformly distributed from multiple, the multiple Beta or multiple Dirichlet that can only level off to as far as possible is distributed, and can not be fitted with correct situation.
It is existing for second generation genome high-flux sequence data in conclusion due to the particularity of tumor tissues
Process bearing calibration is not all suitable for the process flow of Oncogenome data.The current demand needs of the accurate diagnosis and treatment of tumour are directed to
The process bearing calibration of second generation Oncogenome high-flux sequence data.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is that providing a kind of for second
For the process bearing calibration of Oncogenome high-flux sequence data, the problem of tumor structure heterogeneity is pointedly considered.
The invention adopts the following technical scheme:
For the process bearing calibration of second generation Oncogenome high-flux sequence data, using a series of 32 without symbol
Number is mark amount, records the variation of corresponding every blood system or somatic variation data respectively, generates and embodies purity and different sub- gram
The read data of grand proportion, according to father and son be subcloned inherit, brother subclone mutual exclusion Variation Relationship, obtain filial generation subclone and
The somatic variation calibration data of its brother's subclone, for the processing to the two generations Oncogenome high-flux sequence data
Process is corrected.
Preferably, it includes following that the data that the father and son is subcloned the Variation Relationship of succession, brother's subclone mutual exclusion, which generate,
Step:
S1, the Variation Relationship for determining that one group of father and son is subcloned succession, brother's subclone mutual exclusion with reference to genome sequence is read
Data；
S2, the variation data according to step S1 generate the somatic variation data of first generation clone；
S3, the somatic variation data according to step S2 generate the somatic variation of the filial generation subclone of first generation clone
Data；
S4, the somatic variation data according to step S3 generate the somatic variation of brother's subclone of filial generation subclone
Data.
Preferably, the step S1 specifically includes the following steps:
S11, it reads with reference to genome sequence；
S12, variant sites are chosen on reference genome sequence according to preset condition；
S13, the variation type for determining each variant sites, genotype and other attributes；
S14, by the variance of the step S13 variant sites determined, 32 unsigned number formats are stored in nor.sim accordingly
In file, and the variation data of heterozygous variance therein are stored in nor_AB.idx file simultaneously.
Preferably, the step S2 specifically includes the following steps:
S21, it reads with reference to tri- genome sequence, nor.sim and nor_AB.idx files；
S22, the variant sites for choosing first generation clone on reference genome sequence according to preset condition；
S23, each variant sites are checked, if the variant sites appear in nor.sim, but not in nor_AB.idx
In, as homozygosis makes a variation, then return step S22, otherwise definitive variation type and other attributes；
S24, by the variance of the step S23 variant sites determined, 32 unsigned number formats are stored in accordingly
In founding_clone.sim file.
Preferably, the step S3 specifically includes the following steps:
S31, it reads with reference to genome sequence, nor.sim, nor_AB.idx and founding_clone.sim；
S32, variant sites are chosen on reference genome sequence according to preset condition；
S33, each variant sites are checked, if the variant sites appear in nor.sim and founding_
In clone.sim, but not in nor_AB.idx, as homozygous variation, return step S32, otherwise definitive variation type and its
His attribute；
S34, by the variance of the step S33 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneX.sim file, wherein X is reference number of a document.
S35, the specificity variation in the subcloneX.sim file is stored in subcloneX_uniq.idx file
In, wherein X is reference number of a document, if the somatic variation data mostly for filial generation subclone, repeatedly step S31-S35 need to be generated.
Preferably, the step S4 specifically includes the following steps:
S41, it reads with reference to genome sequence, nor.sim, nor_AB.idx, subcloneX.sim, subcloneX_
uniq.idx；
S42, variant sites are chosen on reference genome sequence according to preset condition；
S43, each variant sites are checked, if the variant sites appear in nor.sim, subcloneX.sim, but
Not in nor_AB.idx, otherwise as homozygous variation, return step S42 checks whether it is already present in generation subclone
Subclone*_uniq.idx in, wherein * indicates all same generations, and if it exists, return step S42, otherwise, it determines variation class
Type and other attributes；
S44, by the variance of the step S43 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneY.sim file, Y is reference number of a document.
S45, the specificity variation in the subcloneY.sim file is stored in subcloneY_uniq.idx file
In, Y is reference number of a document, if the somatic variation data of multiple brother's subclones, repeatedly step S41-S45 need to be generated.
Preferably, the variation data type that this method generates includes: single-point variation, insert type structure variation, missing
Type structure variation, inversion type structure variation.
Preferably, the insert type structure variation includes: short insert type structure variation, long insert type structure variation, missing-
The variation of insert type labyrinth and tandem sequence repeats type structure variation.
Preferably, 32 unsigned numbers are that mark amount is made of 5 parts, specific as follows:
si=< inli,insi,vi,gi,ci>
Wherein, siIndicate i-th record, ciIndicate base symbol, giIndicate genotype, viIndicate variation type, insiTable
Show the base symbol of insetion sequence, inliIndicate the length (inl of short insetion sequencei< 10bp), bp is base-pair digit.
It is further, described to generate the read data for embodying purity and different subclone proportions, comprising the following steps:
The first step reads all .sim files；
The proportion of each .sim file is arranged in second step, and all .sim files are 1 with the sum of ratio；
Third step, to choose a .sim file as probability with ratio, further according to the updated distinguished sequence of this document
The double end read of upper stochastical sampling or single end read, if double end read data, then be respectively stored in left.fq and
In right.fq file, wherein insertion distance Normal Distribution；If single end read data, then be stored in single.fq
In file.
Compared with prior art, the present invention at least has the advantages that
Bearing calibration of the present invention uses 32 unsigned numbers for mark amount, records every variation data, generates and embodies
The read data of purity and different subclone proportions, sequentially generate the parent-with inheritance with Evolvement according to inheriting
The somatic variation calibration data of filial generation subclone and brother's subclone with mutex relation, correction data result is close to really
The problem of being worth, essentially eliminating peakdeviation, false positive peak is few.
Further, the present invention sequentially generates the body of blood system variation data and each subclone according to succession and Evolvement
Cytometaplasia data are the evolutionary processes that tumor tissues are simulated by mathematical model.The direct result that tumour develops is exactly tumour
It is heterogeneous.The presence of Tumor Heterogeneity is one of the reason of existing method is not suitable for Oncogenome flow chart of data processing.Cause
This could embody what filial generation subclone inherited its parent only by the evolutionary process of simulation tumour in correction data
Data-signal, while in the data-signal for the exclusive somatic variation for wherein embodying filial generation subclone.
Further, this method can generate the variation data of six seed types such as short mistake-insert type complexity variation, include
The all six kinds of genome mutation types relevant to tumour being currently known, can be improved the precision of calibration data.
Further, the present invention selects 32 unsigned numbers as mark amount, is only an integer in data structure level
Numerical value, but can be parsed out information whole needed for one variation of description, including site, variation type, genotype and other
Attribute saves memory space, improves computational efficiency.
Further, the present invention can generate the data for embodying purity and different subclone proportions, solve existing method
Another reason for being not suitable for Oncogenome flow chart of data processing can correctly be fitted with Tumor Heterogeneity situation.One
Aspect, under the conditions of existing second generation sequencing technologies, tumour purity problem is inevitable.On the other hand, tumour purity
It is just one of the key point of correction of Oncogenome flow chart of data processing with different subclone proportions.
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is the allelic variation frequency distribution result figure of emulation experiment of the present invention；
Fig. 3 is the comparing result figure of the method for the present invention and two methods on truthful data.
Specific embodiment
Refering to Figure 1, the invention discloses a kind of streams for second generation Oncogenome high-flux sequence data
Journey bearing calibration uses 32 unsigned numbers for mark amount, records every blood system variation data, generates and embodies purity and not
With the read data of subclone proportion, succession is subcloned according to father and son, the blood system variation data of brother's subclone mutex relation are closed
System obtains the somatic variation calibration data of brother's subclone of filial generation subclone, for the second generation Oncogenome
The process flow of high-flux sequence data is corrected.
Wherein, the succession of father and son's subclone, the variation data algorithm of brother's subclone mutex relation are specific as follows:
S1, as previously mentioned, tumour cell inherits the variation of blood system, so firstly generating one group of blood system variation data.
Specific generation method is as follows:
S11, it reads with reference to genome sequence；
S12, variant sites are chosen on reference genome sequence according to preset condition；
S13, the variation type for determining each variant sites, genotype and other attributes；
S14, by the variance of the step S13 variant sites determined, 32 unsigned number formats are stored in nor.sim accordingly
In file, and the variation data of heterozygous variance therein are stored in nor_AB.idx file simultaneously.
S2, the somatic variation data for then generating first generation clone.
Specific generation method is as follows:
S21, it reads with reference to tri- genome sequence, nor.sim and nor_AB.idx files；
S22, the variant sites for choosing first generation clone on reference genome sequence according to preset condition；
S23, each variant sites are checked, if the variant sites appear in nor.sim, but not in nor_AB.idx
In, as homozygosis makes a variation, then return step S22, otherwise definitive variation type and other attributes；
S24, by the variance of the step S23 variant sites determined, 32 unsigned number formats are stored in accordingly
In founding_clone.sim file.
The somatic variation data that S3, the filial generation for then generating first generation clone are subcloned.
Specific generation method is as follows:
S31, it reads with reference to genome sequence, nor.sim, nor_AB.idx and founding_clone.sim；
S32, variant sites are chosen on reference genome sequence according to preset condition；
S33, each variant sites are checked, if the variant sites appear in nor.sim and founding_
In clone.sim, but not in nor_AB.idx, as homozygous variation, return step S32, otherwise definitive variation type and its
His attribute；
S34, by the variance of the step S33 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneX.sim file, wherein X is reference number of a document.
S35, the specificity variation in the subcloneX.sim file is stored in subcloneX_uniq.idx file
In, wherein X is reference number of a document.If the somatic variation data mostly for filial generation subclone need to be generated, repeatedly step S31-S35.
The somatic variation data that S4, the brother for ultimately producing filial generation subclone are subcloned.
Specific generation method is as follows:
S41, it reads with reference to genome sequence, nor.sim, nor_AB.idx, subcloneX.sim, subcloneX_
uniq.idx；
S42, variant sites are chosen on reference genome sequence according to preset condition；
S43, each variant sites are checked, if the variant sites appear in nor.sim, subcloneX.sim, but
Not in nor_AB.idx, otherwise as homozygous variation, return step S42 checks whether it is already present in generation subclone
Subclone*_uniq.idx in, wherein * indicates all same generations, and if it exists, return step S42, otherwise, it determines variation class
Type and other attributes；
S44, by the variance of the step S43 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneY.sim file, wherein Y is reference number of a document.
S45, the specificity variation in the subcloneY.sim file is stored in subcloneY_uniq.idx file
In, wherein Y is reference number of a document.If the somatic variation data of multiple brother's subclones need to be generated, repeatedly step S41-S45.
This method, as mark amount, records every variation data using 32 unsigned numbers.
Wherein, 32 bit identification amounts are made of 5 parts.I-th is recorded, si=< inli,insi,vi,gi,ci>, wherein
siIndicate i-th record, ciIndicate base symbol, giIndicate genotype, viIndicate variation type, insiIndicate the alkali of insetion sequence
Base symbol, inliIndicate the length (inl of short insetion sequencei< 10bp), bp is base-pair digit.
The present invention generates the read data for embodying purity and different subclone proportions according to the following steps:
The first step reads all .sim files；
The proportion of each .sim file is arranged in second step, and all .sim files are 1 with the sum of ratio；
Third step, to choose a .sim file as probability with ratio, according to the updated distinguished sequence of this document
End is read in the double end read of upper stochastical sampling or single end, if double end read data, then be respectively stored in left.fq and
In right.fq file, wherein insertion distance Normal Distribution.If single end read data, then be stored in single.fq
In
The variation type that this method can be generated includes: single-point variation, the change of short insert type structure variation, short deletion form structure
Different, long insert type structure variation, long deletion form structure variation, tandem sequence repeats type structure variation, inversion type structure variation, missing-
The variation of insert type labyrinth.
Wherein, the generation method of every kind of variation is specific as follows:
Single-point variation: the base of the reference genome sequence in the site is read, according to user in the site selected for one
The single-point variation ratio of setting is different from being implanted into reference to the base of genome sequence, replace former as mutation probability, random generate
Base, the i.e. variation of generation single-point.
Short insert type structure variation: one section of base is inserted into the site selected for one between the site and next site,
Length and the base of insertion can generate at random, can also be specified by user, that is, generate short insert type structure variation.
Short deletion form structure variation: the site selected for one, one section of base after deleting since the site are long
Degree can generate at random, can also be specified by user, that is, generate short deletion form structure variation.
Long insertion/deletion type structure variation: the insertion/deletion position selected for one, the insertion that reading user specifies/
Deletion condition file is generated according to the insertion/deletion position, sequence content, sequence length and the insertion genotype that set in file
Long insertion/deletion type structure variation.
Tandem sequence repeats type structure variation: the site selected for one, one section of base after reading since the site,
Length can generate at random, can also be specified by user；Then insert type structure variation is generated from the site, the base of insertion is
The base section of reading generates the base section after single-point variation with certain predetermined probability；The number specified according to user, executes repeatedly
Previous step, i.e. generation tandem sequence repeats type structure variation.
Inversion type structure variation: the site selected for one, one section of base after reading since the site, length
It can generate, can also be specified by user at random；Then the reverse complemental base section for taking this section of base is replaced former with new base section
Base section, i.e., generation inversion type structure variation, allow be inverted length little deviation and be inverted area in there are single-point variations.
Missing-insert type labyrinth variation: the site selected for one first generates one short deletion form structure and becomes
It is different, one short insert type structure variation then is generated in the same site, the short sequence being inserted into and former deletion sequence is allowed to have length
Elementary errors on degree, i.e. generation missing-insert type labyrinth variation.
Below by the practicability and validity of emulation experiment and truthful data the results show this method:
(1) emulation experiment
At random from the mankind with reference to reference sequences of one chromosome of selection as simplification in genome sequence.Consider two Asias
It clones, wherein S1For first generation clone, S2For the filial generation subclone of first generation clone, the two proportion is 3:7, and tumour purity is
90%.The distribution of allelic variation frequency is as shown in Figure 2.Distribution in figure is consistent with the calculated results.
(2) truthful data experimental result
Three tumor samples of U.S.'s Oncogenome route map plan are chosen, with the software of Wgsim, SInC and this method
Tri- kinds of methods of TNSim method as a comparison is wrapped, as a result as shown in Figure 3.Three column are respectively three samples in figure, and sample is swollen in the U.S.
Number in tumor gene group route map plan database is respectively AB-2968, BH-A18P and B5-A0JV.The three of the first row in figure
Opening figure is the truth for being published in the subclone that researching and analysing on Nature obtains respectively.The second to four row is respectively
Wgsim, SInC and TNSim are according to known variation data and parameter, the result obtained using identical data analysis process.Knot
Fruit is closer with legitimate reading, illustrates that calibration data more being capable of accurate calibration.As shown in Fig. 2, there is peak value in SInC and Wgsim
The problem of deviating (such as sample AB-2968) and false positive peak value (such as Wgsim is for BH-A18P and B5-A0JV), these problems meeting
Lead to calibration error.In contrast, the problem of peakdeviation is substantially not present in this method, false positive peak is also relatively minimal.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press
According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention
Protection scope within.
Claims (7)
1. a kind of process bearing calibration for second generation Oncogenome high-flux sequence data, which is characterized in that use one
32 unsigned numbers of series are mark amount, record the variation of corresponding every blood system or somatic variation data respectively, generate and embody
The read data of purity and different subclone proportions are subcloned the Variation Relationship of succession, brother's subclone mutual exclusion according to father and son, obtain
To filial generation subclone and its somatic variation calibration data of brother's subclone, for high-throughput to the two generations Oncogenome
The process flow of sequencing data is corrected；
This method generate the variation data type include: single-point variation, insert type structure variation, deletion form structure variation,
Inversion type structure variation；
The father and son be subcloned inherit, the data of the Variation Relationship of brother's subclone mutual exclusion generate the following steps are included:
S1, the Variation Relationship number for determining that one group of father and son is subcloned succession, brother's subclone mutual exclusion with reference to genome sequence is read
According to, specifically includes the following steps:
S11, it reads with reference to genome sequence；
S12, variant sites are chosen on reference genome sequence according to preset condition；
S13, the variation type for determining each variant sites, genotype and other attributes；
S14, by the variance of the step S13 variant sites determined, 32 unsigned number formats are stored in nor.sim file accordingly
In, and the variation data of heterozygous variance therein are stored in nor_AB.idx file simultaneously；
S2, the Variation Relationship data according to step S1 generate the somatic variation relation data of first generation clone；
S3, the somatic variation relation data according to step S2 generate the somatic variation of the filial generation subclone of first generation clone
Relation data；
S4, the somatic variation relation data according to step S3 generate the somatic variation of brother's subclone of filial generation subclone
Relation data.
2. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 1
Method, it is characterised in that: the step S2 specifically includes the following steps:
S21, it reads with reference to tri- genome sequence, nor.sim and nor_AB.idx files；
S22, the variant sites for choosing first generation clone on reference genome sequence according to preset condition；
S23, each variant sites are checked, if the variant sites appear in nor.sim, but not in nor_AB.idx,
As homozygosis makes a variation, then return step S22, otherwise definitive variation type and other attributes；
S24, by the variance of the step S23 variant sites determined, 32 unsigned number formats are stored in founding_ accordingly
In clone.sim file.
3. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 2
Method, which is characterized in that the step S3 specifically includes the following steps:
S31, it reads with reference to genome sequence, nor.sim, nor_AB.idx and founding_clone.sim；
S32, variant sites are chosen on reference genome sequence according to preset condition；
S33, each variant sites are checked, if the variant sites appear in nor.sim and founding_clone.sim,
But not in nor_AB.idx, as homozygosis makes a variation, return step S32, otherwise definitive variation type and other attributes；
S34, by the variance of the step S33 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneX.sim file, wherein X is reference number of a document；
S35, the specificity variation in the subcloneX.sim file is stored in subcloneX_uniq.idx file,
Wherein, X is reference number of a document, if the somatic variation data mostly for filial generation subclone, repeatedly step S31-S35 need to be generated.
4. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 3
Method, which is characterized in that the step S4 specifically includes the following steps:
S41, it reads with reference to genome sequence, nor.sim, nor_AB.idx, subcloneX.sim, subcloneX_
uniq.idx；
S42, variant sites are chosen on reference genome sequence according to preset condition；
S43, each variant sites are checked, if the variant sites appear in nor.sim, subcloneX.sim, but do not existed
In nor_AB.idx, otherwise as homozygous variation, return step S42 checks whether it is already present in generation subclone
In subclone*_uniq.idx, wherein * indicates all same generations, and if it exists, return step S42, otherwise, it determines variation type
With other attributes；
S44, by the variance of the step S43 variant sites determined, 32 unsigned number formats are stored in accordingly
In subcloneY.sim file, Y is reference number of a document；
S45, the specificity variation in the subcloneY.sim file is stored in subcloneY_uniq.idx file, Y
It is reference number of a document, if the somatic variation data of multiple brother's subclones, repeatedly step S41-S45 need to be generated.
5. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 1
Method, which is characterized in that the insert type structure variation includes: short insert type structure variation, long insert type structure variation, missing-
The variation of insert type labyrinth and tandem sequence repeats type structure variation.
6. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 1
Method, it is characterised in that: 32 unsigned numbers are that mark amount is made of 5 parts, specific as follows:
si=< inli,insi,vi,gi,ci>
Wherein, siIndicate i-th record, ciIndicate base symbol, giIndicate genotype, viIndicate variation type, insiIt indicates to insert
Enter the base symbol of sequence, inliIndicate the length inl of short insetion sequencei< 10bp, bp are base-pair digits.
7. a kind of process correction side for second generation Oncogenome high-flux sequence data according to claim 1
Method, which is characterized in that described to generate the read data for embodying purity and different subclone proportions, comprising the following steps:
The first step reads all .sim files；
The proportion of each .sim file is arranged in second step, and all .sim files are 1 with the sum of ratio；
Third step, using with ratio as probability selection one .sim file, further according on the updated distinguished sequence of this document with
Machine samples double end reads or single end read, if double end read data, then be respectively stored in left.fq and right.fq
In file, wherein insertion distance Normal Distribution；If single end read data, then be stored in single.fq file.
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