CN107133495A - A kind of analysis method and analysis system of aneuploidy biological information - Google Patents
A kind of analysis method and analysis system of aneuploidy biological information Download PDFInfo
- Publication number
- CN107133495A CN107133495A CN201710310451.9A CN201710310451A CN107133495A CN 107133495 A CN107133495 A CN 107133495A CN 201710310451 A CN201710310451 A CN 201710310451A CN 107133495 A CN107133495 A CN 107133495A
- Authority
- CN
- China
- Prior art keywords
- chromosome
- ratio
- sample
- window
- unique reads
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Biology (AREA)
- Theoretical Computer Science (AREA)
- Biotechnology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioethics (AREA)
- Databases & Information Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Software Systems (AREA)
- Public Health (AREA)
- Evolutionary Computation (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Genetics & Genomics (AREA)
- Molecular Biology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The invention discloses a kind of analysis method and analysis system of aneuploidy biological information.Wherein, reference database 1) is built different；2) UR ratio are calculated；3) reference database statistics parameter is built；4) Z values are calculated；5) there is the foetal chromosome aneuploidy false positive that micro-deleted or micro- repetition is caused in method reduction parent itself chromosome that there is the foetal chromosome aneuploidy false positive that micro-deleted or micro- repetition is caused according to above-mentioned reduction parent itself chromosome；6) the foetal DNA concentration prediction model built according to the construction method of above-mentioned foetal DNA concentration prediction model predicts foetal DNA concentration；7) the DNA quantity for calculating every chromosome of fetus accounts for the percentage of overall dna：8) judgement of autosome aneuploid：9) sex chromosomal abnormality judges.Apply the technical scheme of the present invention, drastically increase the accuracy of analysis.
Description
Technical field
The present invention relates to field of biomedicine technology, in particular to a kind of analysis side of aneuploidy biological information
Method and analysis system.
Background technology
Pre-natal diagnosis refers to check fetus using Noninvasive or invasive means, can be in pregnant early stage or mid-term
Diagnosis is just made to fetus, to be intervened in advance or to be treated.Wherein, invasive means include：Fine hair biopsy, amnion are worn
Pierce art and trans-abdominal percutaneous umbilical sampling etc..Although result is more accurate, there is at a relatively high risk, pregnant woman is easily caused
Miscarriage or intrauterine infection.Prenatal Screening means (periphery blood biochemistry examination and ultrasonic Nuchal translucency) are although without using intrusive mood side
Method, but verification and measurement ratio and false positive rate can not reach desired level.NIPT, referred to as noninvasive antenatal detection is to be applied to the pregnancy period
A technology of inspection is produced, this technology is fetus dissociative DNA present in the blood plasma based on maternal blood, with very high inspection
Accuracy is surveyed, while it also avoid the brought miscarriage of invasive detection and intrauterine infection risk.
NIPT detections isolate blood plasma firstly the need of maternal blood is extracted, and extract plasma DNA, build for two generations
Sequencing library, the sequence information of pregnant woman blood plasma dissociative DNA is obtained using two generation sequenators.Obtained sequencing data passes through basic
Quality Control and mankind's reference gene group are compared, GC is corrected, calculate the steps such as Z values to obtain the risk that fetus is ill.But, it is existing
NIPT detection techniques have the following disadvantages：1) when foetal DNA concentration is low, it is impossible to accurately judge chromosome abnormality, hold
Easily cause false the moon；2) it can only detect that the autosomal aneuploid such as No. 13, No. 18, No. 21 chromosomes is abnormal, it is difficult to effectively sentence
Disconnected sex chromosome；3) it is applied to single tire, it is impossible to which effective detection is carried out to twins or even polyembryony；4) influenceed larger by sequencing batch,
Easily cause false sun；5.) be not suitable for the foetal DNA of detection parent abnormal (the micro-deleted and micro- repetition of chromosome), easily cause false sun.
The content of the invention
The present invention is intended to provide a kind of analysis method and analysis system of aneuploidy biological information, to improve the standard of analysis
True property.
To achieve these goals, according to an aspect of the invention, there is provided a kind of correction side of sequencing GC Preferences
Method.The bearing calibration comprises the following steps：1) testing sample is sequenced using high-flux sequence platform；2) sequencing is obtained
Base sequence remove reference gene group sequence after joint and low-quality base sequence with the check sample of reference database
It is compared, the sequence bar number of unique comparison on every chromosome of statistics to reference gene group, i.e. Unique Read bar
Number, and calculate the percentage UR ratio that the total amount of Unique Read on every chromosome accounts for total Unique Reads；3) survey
Sequence GC Preferences are corrected：Data prediction, then carries out GC corrections using three sets of different Correction Strategies simultaneously；Wherein, data
Pretreatment includes：Whole chromosome is divided into the window of 100kb clip sizes, overlapping region is 50kb, calculates each in each window
The G/C content of sequencing sequence, while calculate the unique reads numbers in window, ignore be sequenced uncertain base N,
Unique reads are 0 or abnormal those high windows, then, the unique reads numbers in window are normalized, that is, removed
With the unique reads numbers in the corresponding window of the check sample of reference database；Three sets of different Correction Strategies include：First, adopt
GC corrections are carried out with Local Polynomial weighted regression method：After data prediction, the unique reads numbers and window in window are utilized
Interior G/C content makees Local Polynomial weighted regression, recycles normalized unique reads numbers divided by Local Polynomial in window
The estimate that weighted regression is obtained calculates the unique reads of each window numerical value, and then realizes on every chromosome
Unique reads GC corrections；2nd, GC corrections are carried out using intermediate value (rolling median) method of rolling：Data prediction
Afterwards, G/C content fenestrate in all chromosomes is ranked up, with 0.1%GC value differences it is different to institute it is fenestrate be grouped, statistics
Fenestrate Unique reads numbers in each GC value groups, calculate its median as the GC weights of this group of sequencing sequence, then
Using normalized unique reads numbers in window divided by the GC weights of the group, so as to obtain the unique reads after GC corrections
Number；3rd, GC corrections are carried out using linear regression method：After data prediction, every chromosome is estimated using least square method
Normalization unique reads numbers and GC and 1/GC regression equation, unique reads estimate is obtained, using in window
The estimate of normalized unique reads numbers divided by its correspondence window, so as to realize the Unique reads to every chromosome
Several GC corrections.
There is provided a kind of correction system of sequencing GC Preferences according to a further aspect of the present invention.The correction system includes：Survey
Sequence module：For testing sample to be sequenced using high-flux sequence platform；Compare statistical module：For will be sequenced what is obtained
The reference gene group sequence that base sequence removes after joint and low-quality base sequence with the check sample of reference database is entered
Row is compared, the bar number of the Unique Read on every chromosome of statistics, and calculates the total of Unique Read on every chromosome
Amount accounts for total Unique Reads percentage UR ratio；Rectification module：For the correction of GC Preferences to be sequenced；Rectification module bag
Data prediction submodule and correction submodule are included, wherein, data prediction submodule：For whole chromosome to be divided into
The window of 100kb clip sizes, overlapping region is 50kb, calculates the G/C content of each sequencing sequence in each window, while calculating in window
Unique reads numbers, it is 0 or abnormal those high windows to ignore with uncertain base N, unique reads is sequenced,
Then, the unique reads numbers in window are normalized, i.e., divided by reference database check sample corresponding window in
Unique reads numbers；Correcting submodule is used for simultaneously using three sets different Correction Strategies progress GC corrections, and three sets different
Correction Strategies include：First, GC corrections are carried out using Local Polynomial weighted regression method：After data prediction, using in window
Unique reads numbers make Local Polynomial weighted regression with the G/C content in window, recycle normalized unique in window
The estimate that reads numbers divided by Local Polynomial weighted regression are obtained calculates the unique reads of each window numerical value, and then
Realize and the GC of the Unique reads on every chromosome is corrected；2nd, using intermediate value (rolling median) method of rolling
Carry out GC corrections：After data prediction, G/C content fenestrate in all chromosomes is ranked up, it is different with 0.1%GC value differences
It is fenestrate to institute to be grouped, the fenestrate Unique reads numbers in each GC values group are counted, its median is calculated and is used as this
The GC weights of group sequencing sequence, recycle the GC weights of normalized unique reads numbers divided by the group in window, so as to obtain
Unique reads numbers after GC corrections；3rd, GC corrections are carried out using linear regression method：After data prediction, minimum is utilized
Square law estimates the normalization unique reads numbers of every chromosome and GC and 1/GC regression equation, obtains unique
Reads estimate, using the estimate of normalized unique reads numbers in window divided by its correspondence window, so as to realize to every
The GC corrections of the Unique reads numbers of bar chromosome.
There is provided a kind of construction method of foetal DNA concentration prediction model according to a further aspect of the invention.The structure side
Method comprises the following steps：1) selection certain amount karyotyping dye-free body exception and pregnant week nourishing normally more than or equal to 12 weeks
The maternal sample of male tire, a number of healthy male and women sample, base is carried out to the plasma DNA sample of these samples
Because of sequencing, sequencing data is eliminated according to the bearing calibration of above-mentioned sequencing GC Preferences in chromosome and GC preferences are sequenced in interchromosomal
Property, GC corrections are carried out to unique reads numbers, the UR ratio of the Y chromosome of these samples is then calculated, obtains nourishing just
The UR ratio R of the Y chromosome of the maternal sample of normal man's tireSample, women sample Y chromosome UR ratio Rfemale, man
The UR ratio R of the Y chromosome of property samplemale；2) fetal concentrations are calculated according to equation below：Male tire foetal DNA concentration=
(RSample-Rfemale)/(Rmale-Rfemale), then, the fragment length of the sequencing sequence for the maternal sample for nourishing normal male tire is calculated,
And the distribution situation of fragment length is counted, obtain statistics parameter fragment length percentage by calculating the ratio of different fragments length
Than fetal concentrations and fragment length percentage are done into linear regression, linear regression model (LRM) is built, then passes through linear regression model (LRM)
According to the DNA concentration of the fragment length percent prediction female tire fetus for the maternal sample for nourishing female's tire.
There is provided a kind of construction method for the forecast model analyzed for sex chromosome according to a further aspect of the invention.
The construction method comprises the following steps：1) choose certain amount karyotyping dye-free body exception and pregnant week is more than or equal to 12 weeks
Maternal sample as reference database control sample, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without notable
Difference；XO, XXX, XXY, XYY sample verified through karyotyping of identical quantity are selected in addition, and the six classes sample is used as structure
The test set of model；2) gene sequencing is carried out to the DNA sample extracted in the blood plasma of test set, sequencing data is according to above-mentioned sequencing
The bearing calibration of GC Preferences is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC schools are carried out to unique reads numbers
Just, every chromosome UR ratio is then calculated；The construction method of above-mentioned foetal DNA concentration prediction model builds prediction mould simultaneously
Type, fetal concentrations are calculated using fragment length percentage；3) for X chromosome and the UR ratio and fetal concentrations of Y chromosome
Three parameters, using the SVMs Multiclass Classification based on binary tree, the prediction that cross validation is stablized is rolled over using k
Model.
There is micro-deleted or micro- repetition there is provided one kind reduction parent itself chromosome according to a further aspect of the invention to make
Into foetal chromosome aneuploidy false positive method.This method comprises the following steps：1) the blood plasma trip of parent to be measured is extracted
It is sequenced from DNA, sequencing data is eliminated according to the bearing calibration of above-mentioned sequencing GC Preferences in chromosome and interchromosomal is surveyed
Sequence GC Preferences, GC corrections are carried out to unique reads numbers；2) the Z values of test sample data in each window are calculated；Will
The average value of Z values on some chromosome of test sample is compared with corresponding threshold value, so as to whether judge the chromosome
Missing is repeated, and the region for lacking or repeating；3) when foetal chromosome aneuploidy detection Z values are calculated, by test sample
The middle window that there is missing or repeat is filtered out, so that reducing parent itself chromosome has the fetus that micro-deleted or micro- repetition is caused
The false positive of chromosome aneuploid.
There is provided a kind of analysis method of aneuploidy biological information according to a further aspect of the invention.The analysis method
Comprise the following steps：
1) reference database is built：Certain amount pregnant week is chosen to be more than or equal to 12 weeks and karyotyping dye-free body exception
Maternal sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
2) UR ratio are calculated：Bearing calibration according to above-mentioned sequencing GC Preferences is eliminated in chromosome and interchromosomal is surveyed
Sequence GC Preferences, carry out GC corrections to the unique reads numbers of sample in reference database, then calculate every chromosome UR
ratio；
3) reference database statistics parameter is built：According to step 2) the middle UR ratio obtained, calculate control sample storehouse
In every autosome UR ratio average and standard error；
4) Z values are calculated：The plasma DNA of pregnant woman's sample to be measured is sequenced, sequencing data is according to above-mentioned sequencing GC
The bearing calibration of Preference is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC schools are carried out to unique reads numbers
Just, every chromosome UR ratio is then calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
5) there is the non-multiple of fetal chromosomal that micro-deleted or micro- repetition is caused according to above-mentioned reduction parent itself chromosome
The method of body false positive reduces parent itself chromosome and there is the foetal chromosome aneuploidy vacation that micro-deleted or micro- repetition is caused
It is positive；
6) the foetal DNA concentration prediction model built according to the construction method of above-mentioned foetal DNA concentration prediction model is predicted
Foetal DNA concentration；
7) the DNA quantity for calculating every chromosome of fetus accounts for the percentage of overall dna：Calculate every dyeing in sample to be tested
The ratio of the UR ratio of body and the deviation of control sample storehouse check sample, the difference and check sample UR ratio average value
2 times then account for the ratio of overall dna for the DNA of fetus, formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of overall dna；
8) judgement of autosome aneuploid：
According to step 4) correct three sets of different Z that Correction Strategies calculate every chromosome for three sets of different GC
Value, triplicity come the value that synthetically judges UR ratio it is whether statistically significant on exception, if specifically, three sets differences
ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetal concentrations, then judge No. i-th chromosome for aneuploid；
9) sex chromosomal abnormality judges：
Forecast model is built by the construction method of the above-mentioned forecast model analyzed for sex chromosome, to test pregnant woman's sample
The sex chromosomal abnormality situation of product is judged.
Further, step 8) further comprise according to calculating log-likelihood ratio L values, and often dyed according to L values
The judgement of body aneuploid.
There is provided a kind of analysis system of aneuploidy biological information according to a further aspect of the invention.The analysis system
Including：
Reference database builds module：For choosing certain amount pregnant week more than or equal to 12 weeks and karyotyping dye-free body
Abnormal maternal sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
UR ratio computing modules：Eliminated for above-mentioned bearing calibration in chromosome and GC Preferences be sequenced in interchromosomal,
GC corrections are carried out to the unique reads numbers of sample in reference database, every chromosome UR ratio is then calculated；
Reference database statistics parameter builds module：For according to the UR obtained in UR ratio computing modules
Ratio, calculates the average and standard error of every autosome UR ratio in control sample storehouse；
Z value computing modules：It is sequenced for the plasma DNA to pregnant woman's sample to be measured, sequencing data is according to above-mentioned
Bearing calibration is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC corrections, Ran Houji are carried out to unique reads numbers
Every chromosome UR ratio is calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
Parent itself microdeletion micro- computes repeatedly module：Parent itself chromosome is reduced for the above method to deposit
The foetal chromosome aneuploidy false positive caused in micro-deleted or micro- repetition；
Foetal DNA concentration prediction module：Fetus is predicted for the foetal DNA concentration prediction model that above-mentioned construction method is built
DNA concentration；
Foetal DNA number calculating section：The percentage of overall dna is accounted for for calculating the DNA quantity of every chromosome of fetus：
Calculate the deviation of the UR ratio of every chromosome and control sample storehouse check sample in sample to be tested, the difference and check sample
2 times of the ratio of UR ratio average value then account for the ratio of overall dna for the DNA of fetus, and formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of overall dna；
The judge module of autosome aneuploid：For according in Z value computing modules for three sets different GC corrections
Correction Strategies calculate three sets of different Z values of every chromosome, triplicity come synthetically judge UR ratio value whether
Exception on statistically significant, if specifically, three sets of different ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetus
Concentration, then judge No. i-th chromosome for aneuploid；
Sex chromosomal abnormality judge module：For the construction method by the above-mentioned forecast model analyzed for sex chromosome
Forecast model is built, the sex chromosomal abnormality situation for testing maternal sample is judged.
Further, the judge module of autosome aneuploid further comprises according to calculating log-likelihood ratio L values,
And the judgement of autosome aneuploid is carried out according to L values.
Because UR ratio are most basic support data, many other factors in addition to three body signals in inventive algorithm
Its change is also brought along, for example machine batch and sequencing GC-bias etc. all can cause large effect to UR ratio on sample.
In this regard, the algorithm of the present invention employs three sets of different Correction Strategies to eliminate bases G C content and sequence signature etc. simultaneously
The fluctuation for the UR ratio that factor is brought, algorithm applies including local polynomial regression, rolls intermediate value (rolling
Median), a variety of strategies such as weight correction, reduce error interference, finally according to statistical check algorithm, calculate three sets as far as possible
Different Z values, triplicity come the value that synthetically judges UR ratio it is whether statistically significant on exception, be greatly enhanced
The accuracy of analysis.
Embodiment
It should be noted that in the case where not conflicting, the feature in embodiment and embodiment in the application can phase
Mutually combination.The present invention is described in detail below in conjunction with embodiment.
The main analysis of biological information flow of the present invention is as follows：The sequencing of Illumina platforms is obtained after DNA sequence dna information, first
Joint and low-quality reads are first removed to initial data, core algorithm of the invention is to compare DNA sequence dna to arrive first
On human genome reference sequences, distribution of the sample DNA sequence on every chromosome is then counted according to the result of comparison
The bar number of Unique Read on situation, every chromosome of statistics.Unique Read total amount accounts for total on every chromosome
Unique Reads percentage (UR ratio) is exactly one of significant data that we study.For in theory, certain dyeing
The too high or too low exception of body UR ratio value is derived from this abnormal chromosome nucleic acid molecular weight in the sample
It is abnormal.Whether it is ortholoidy that the core of the algorithm of the present invention is namely based on this principle to carry out the chromosome of diagnosing fetal.
According to a kind of typical embodiment of the present invention, there is provided a kind of bearing calibration of sequencing GC Preferences.The correction side
Method comprises the following steps：1) testing sample is sequenced using high-flux sequence platform；2) obtained base sequence will be sequenced
Remove the reference gene group sequence after joint and low-quality base sequence with the check sample of reference database to be compared, unite
The bar number of the Unique Read on every chromosome is counted, and calculates the total amount of Unique Read on every chromosome and accounts for total
Unique Reads percentage UR ratio；3) sequencing GC Preferences correction：Data prediction, then uses three sets not simultaneously
Same Correction Strategies carry out GC corrections；Wherein, data prediction includes：Whole chromosome is divided into 100kb clip sizes
Window, overlapping region is 50kb, calculates the G/C content of each sequencing sequence in each window, while calculating the unique reads in window
Number, it is 0 or abnormal those high windows to ignore with N, unique reads, and then, the unique reads numbers in window are entered
Row normalization, i.e., divided by reference database check sample unique reads numbers；Three sets of different Correction Strategies include：
First, GC corrections are carried out using Local Polynomial weighted regression method：After data prediction, the unique reads numbers in window are utilized
Make Local Polynomial weighted regression with the G/C content in window, recycle normalized unique reads numbers divided by part in window many
The weights that item formula weighted regression is obtained calculate the unique reads of each window numerical value, and then realize on every chromosome
Unique reads GC corrections；2nd, GC corrections are carried out using intermediate value (rolling median) method of rolling：Data prediction
Afterwards, G/C content fenestrate in all chromosomes is ranked up, with 0.1%GC value differences it is different to institute it is fenestrate be grouped, statistics
Fenestrate Unique reads numbers in each GC value groups, calculate its median as the GC weights of this group of sequencing sequence, then
Using normalized unique reads numbers in window divided by the GC weights of the group, so as to obtain the unique reads after GC corrections
Number；3rd, GC corrections are carried out using linear regression method：After data prediction, every chromosome is estimated using least square method
Normalization unique reads numbers and GC and 1/GC regression equation, unique reads estimate is obtained, using in window
The estimate of normalized unique reads numbers divided by its correspondence window, so as to realize the Unique reads to every chromosome
Several GC corrections.
Because UR ratio are most basic support data, many other factors in addition to three body signals in inventive algorithm
Its change is also brought along, for example machine batch and sequencing GC-bias etc. all can cause large effect to UR ratio on sample.
In this regard, the algorithm of the present invention employs three sets of different Correction Strategies to eliminate bases G C content and sequence signature etc. simultaneously
The fluctuation for the UR ratio that factor is brought, algorithm applies including local polynomial regression, rolls intermediate value (rolling
Median), a variety of strategies such as weight correction, reduce error interference as far as possible.
According to a kind of typical embodiment of the present invention, there is provided a kind of correction system of sequencing GC Preferences.The correction system
System includes：Sequencer module：For testing sample to be sequenced using high-flux sequence platform；Compare statistical module：For inciting somebody to action
The reference base with the check sample of reference database after obtained base sequence removal joint and low-quality base sequence is sequenced
Because a group sequence is compared, the bar number of the Unique Read on every chromosome of statistics, and calculate Unique on every chromosome
Read total amount accounts for total Unique Reads percentage UR ratio；Rectification module：For the correction of GC Preferences to be sequenced；Rectify
Positive module includes data prediction submodule and correction submodule, wherein, data prediction submodule：For by whole chromosome
The window of 100kb clip sizes is divided into, overlapping region is 50kb, calculates the G/C content of each sequencing sequence in each window, counted simultaneously
The unique reads numbers in window are calculated, it is 0 or abnormal high that to ignore with uncertain base N, the unique reads of sequencing
A little windows, then, the unique reads numbers in window are normalized, i.e., divided by reference database check sample unique
Reads numbers；Correcting submodule is used to carry out GC corrections, three sets of different Correction Strategies using three sets of different Correction Strategies simultaneously
Including：First, GC corrections are carried out using Local Polynomial weighted regression method：After data prediction, the unique in window is utilized
Reads numbers make Local Polynomial weighted regression with the G/C content in window, recycle normalized unique reads numbers in window to remove
The weights obtained with Local Polynomial weighted regression calculate the unique reads of each window numerical value, and then realize to every dye
The GC corrections of Unique reads on colour solid；2nd, GC corrections are carried out using intermediate value (rolling median) method of rolling：Number
After Data preprocess, G/C content fenestrate in all chromosomes is ranked up, institute fenestrate divided so that 0.1%GC value differences are different
Group, counts the fenestrate Unique reads numbers in each GC values group, calculates its median as the GC of this group of sequencing sequence
Weight, recycles the GC weights of normalized unique reads numbers divided by the group in window, so as to obtain after GC corrections
Unique reads numbers；3rd, GC corrections are carried out using linear regression method：After data prediction, estimated using least square method
Go out the normalization unique reads numbers of every chromosome and GC and 1/GC regression equation, obtain unique reads estimation
Value, using the estimate of normalized unique reads numbers in window divided by its correspondence window, so as to realize to every chromosome
The GC corrections of Unique reads numbers.
In addition, the factor such as foetal DNA concentration can also be impacted to the accuracy of detection, too low fetal concentrations can be made
Into the result of false negative.We can be slightly shorter than mother cfDNA characteristic using fetal DNA fragments in blood plasma, according to sequencing piece
The length information of section calculates fragment length percentage, makees training set by certain maternal sample for nourishing normal male tire, infers
Go out the linear relationship of fragment length percentage and fetal concentrations, so as to calculate fetal concentrations.
According to a kind of typical embodiment of the present invention, there is provided a kind of construction method of foetal DNA concentration prediction model.Should
Construction method comprises the following steps：1) bosom of selection certain amount karyotyping dye-free body exception and pregnant week more than or equal to 12 weeks
There are maternal sample, a number of healthy male and the women sample of normal male tire, to the plasma DNA sample of these samples
Gene sequencing is carried out, sequencing data is eliminated according to the bearing calibration of above-mentioned sequencing GC Preferences in chromosome and interchromosomal is sequenced
GC Preferences, carry out GC corrections to unique reads numbers, then calculate the UR ratio of the Y chromosome of these samples, obtain
Nourish the UR ratio R of the Y chromosome of the maternal sample of normal male tireSample, women sample Y chromosome UR ratio
Rfemale, male's sample Y chromosome UR ratio Rmale；2) fetal concentrations are calculated according to equation below：Male tire foetal DNA
Concentration=(RSample-Rfemale)/(Rmale-Rfemale), then, calculate the piece of the sequencing sequence for the maternal sample for nourishing normal male tire
Segment length, and the distribution situation of fragment length is counted, obtain statistics parameter fragment by calculating the ratio of different fragments length
Fetal concentrations and fragment length percentage are done linear regression by length percent, linear regression model (LRM) are built, then by described
Linear regression model (LRM) is according to the DNA concentration of the fragment length percent prediction female tire fetus for the maternal sample for nourishing female's tire.
In terms of sex of foetus, due to the DNA sequence dna of masculinity and femininity in blood plasma exist on X, Y chromosome it is obvious poor
Not, sex can be judged by the Joint Distribution (making hypothesis testing with Joint Distribution) of both.However, schools many before this
In correction method, the detection of sex chromosomal abnormality is always larger difficult point.The algorithm of the present invention is by means of advanced pattern-recognition
Algorithm, can effectively catch corresponding characteristic value, and accurate judgement is made to the sample of sex chromosomal abnormality.
According to a kind of typical embodiment of the present invention, there is provided a kind of structure for the forecast model analyzed for sex chromosome
Method.The construction method comprises the following steps：1) choose certain amount karyotyping dye-free body exception and pregnant week is more than or equal to
The maternal sample of 12 weeks as reference database control sample, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire
Without significant difference；XO, XXX, XXY, XYY sample verified through karyotyping of identical quantity are selected in addition, and the six classes sample is made
To build the test set of model；2) gene sequencing is carried out to the DNA sample extracted in the plasma free of test set, sequencing data is pressed
Bearing calibration according to above-mentioned sequencing GC Preferences is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, to unique reads
Number carries out GC corrections, then calculates every chromosome UR ratio；Simultaneously according to the structure of above-mentioned foetal DNA concentration prediction model
Method builds forecast model, and fetal concentrations are calculated using fragment length percentage；3) for X chromosome and the UR of Y chromosome
Three parameters of ratio and fetal concentrations, using the SVMs Multiclass Classification based on binary tree, are tested using k folding intersections
Demonstrate,prove the forecast model stablized.
The core algorithm of the present invention contemplates the fetus that situations such as there is micro- repeat by mother itself chromosome is caused
The situation of the false positive of chromosomal aneuploidy.Therefore some small windows can be all divided every item chromosome of positive sample to carry out
Unique Reads are counted, carries out after corresponding GC correcting algorithms, makes the chromosome overburden depth figure in units of window, and
Statistical check between sample is carried out in units of window.
Existed according to a kind of typical embodiment of the present invention there is provided one kind reduction parent itself chromosome micro-deleted or micro-
The method for the foetal chromosome aneuploidy false positive that repetition is caused.This method comprises the following steps：1) parent to be measured is extracted
Plasma DNA is sequenced, and sequencing data is eliminated in chromosome and dyed according to the bearing calibration of above-mentioned sequencing GC Preferences
GC Preferences are sequenced between body, GC corrections are carried out to unique reads numbers；2) Z of test sample data in each window is calculated
Value；The average value of Z values on some chromosome of test sample is compared with corresponding threshold value, so as to judge the chromosome
Whether lack or repeat, and the region for lacking or repeating；3) when foetal chromosome aneuploidy detection Z values are calculated, it will test
The window that there is missing in sample or repeat is filtered out, so that reducing parent itself chromosome has what micro-deleted or micro- repetition was caused
The false positive of foetal chromosome aneuploidy.
According to a kind of typical embodiment of the present invention, there is provided a kind of analysis method of aneuploidy biological information.This point
Analysis method comprises the following steps：
1) reference database is built：Certain amount pregnant week is chosen to be more than or equal to 12 weeks and karyotyping dye-free body exception
Maternal sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
2) UR ratio are calculated：Bearing calibration according to above-mentioned sequencing GC Preferences is eliminated in chromosome and interchromosomal is surveyed
Sequence GC Preferences, carry out GC corrections to the unique reads numbers of sample in reference database, then calculate every chromosome UR
ratio；
3) reference database statistics parameter is built：According to step 2) the middle UR ratio obtained, calculate control sample storehouse
In every autosome UR ratio average and standard error；
4) Z values are calculated：The plasma DNA of pregnant woman's sample to be measured is sequenced, sequencing data is according to above-mentioned sequencing GC
The bearing calibration of Preference is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC schools are carried out to unique reads numbers
Just, every chromosome UR ratio is then calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
5) there is the non-multiple of fetal chromosomal that micro-deleted or micro- repetition is caused according to above-mentioned reduction parent itself chromosome
The method of body false positive reduces parent itself chromosome and there is the foetal chromosome aneuploidy vacation that micro-deleted or micro- repetition is caused
It is positive；
6) the foetal DNA concentration prediction model built according to the construction method of above-mentioned foetal DNA concentration prediction model is predicted
Foetal DNA concentration；
7) the DNA quantity for calculating every chromosome of fetus accounts for the percentage of overall dna：Calculate every dyeing in sample to be tested
The ratio of the UR ratio of body and the deviation of control sample storehouse check sample, the difference and check sample UR ratio average value
2 times then account for the ratio of overall dna for the DNA of fetus, formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of overall dna；
8) judgement of autosome aneuploid：
According to step 4) correct three sets of different Z that Correction Strategies calculate every chromosome for three sets of different GC
Value, triplicity come the value that synthetically judges UR ratio it is whether statistically significant on exception, if specifically, three sets differences
ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetal concentrations, then judge No. i-th chromosome for aneuploid；
9) sex chromosomal abnormality judges：
Forecast model is built by the construction method of the above-mentioned forecast model analyzed for sex chromosome, to test pregnant woman's sample
The sex chromosomal abnormality situation of product is judged.
In the typical embodiment of the present invention, step 8) in calculate three sets of different Z values, triplicity is integrated
Ground judge UR ratio value it is whether statistically significant on exception, what three kinds of parallel detection strategies considerably increased detection can
Reliability.Generally, Z is more than 3 or is the exception on statistical significance less than -3, and only three values are feminine gender in the present invention,
Feminine gender is judged to, otherwise can pointedly depth analysis.
It is preferred that, step 8) further comprise carrying out autosome according to calculating log-likelihood ratio L values, and according to L values
The judgement of aneuploid.
Later stage saves bit by bit with positive sample, on the basis of Z values, can also further calculate log-likelihood ratio L values.L
Value has bigger advantage compared with Z values on the abnormal Sensitivity and Specificity of detection fetal chromosomal aneuploidy.This be because
For Z only represents the degree that the sample deviates negative reference sample, and Z is more big more illustrates that the sample is not negative；L is the sample
Deviate the ratio of the degree of negative reference sample and the degree of sample deviation positive reference sample, L increasingly illustrates the sample
It is not negative it is more likely that positive.
In addition, with the accumulation of Massive Sample, the present invention can utilize the advantage of machine learning, by Z values, maternal age,
A variety of variables such as pregnant week, fetal concentrations, using random forests algorithm, further do mode decision as the input of model, improve
The reliability of fetus aneuploidy detection.
According to a kind of typical embodiment of the present invention, there is provided a kind of analysis system of aneuploidy biological information.This point
Analysis system includes：
Reference database builds module：For choosing certain amount pregnant week more than or equal to 12 weeks and karyotyping dye-free body
Abnormal maternal sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
UR ratio computing modules：For according to above-mentioned bearing calibration eliminate chromosome in and interchromosomal be sequenced GC preferences
Property, GC corrections are carried out to the unique reads numbers of sample in reference database, every chromosome UR ratio is then calculated；
Reference database statistics parameter builds module：For according to the UR obtained in UR ratio computing modules
Ratio, calculates the average and standard error of every autosome UR ratio in control sample storehouse；
Z value computing modules：It is sequenced for the plasma DNA to pregnant woman's sample to be measured, sequencing data is according to above-mentioned
Bearing calibration is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC corrections, Ran Houji are carried out to unique reads numbers
Every chromosome UR ratio is calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
Parent itself microdeletion micro- computes repeatedly module：For reducing parent itself dyeing according to the method described above
There is the foetal chromosome aneuploidy false positive that micro-deleted or micro- repetition is caused in body；
Foetal DNA concentration prediction module：It is pre- for the foetal DNA concentration prediction model according to above-mentioned construction method structure
Survey foetal DNA concentration；
Foetal DNA number calculating section：The percentage of overall dna is accounted for for calculating the DNA quantity of every chromosome of fetus：
Calculate the deviation of the long check sample of UR ratio and control sample storehouse of every chromosome in sample to be tested, the difference and control sample
2 times of the ratio of this UR ratio average value then account for the ratio of overall dna for the DNA of fetus, and formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of overall dna；
The judge module of autosome aneuploid：For according in Z value computing modules for three sets of every chromosome
Different GC corrections Correction Strategies calculate three sets of different Z values, triplicity come synthetically judge UR ratio value whether
Exception on statistically significant, if specifically, three sets of different ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetus
Concentration, then judge No. i-th chromosome for aneuploid；
Sex chromosomal abnormality judge module：For the construction method by the above-mentioned forecast model analyzed for sex chromosome
Forecast model is built, the sex chromosomal abnormality situation for testing maternal sample is judged.
It is preferred that, the judge module of autosome aneuploid further comprises according to calculating log-likelihood ratio L values, and
The judgement of autosome aneuploid is carried out according to L values.
Beneficial effects of the present invention are further illustrated below in conjunction with embodiment.
Embodiment 1
1) reference database is built：Choose 1500 pregnant weeks be more than or equal to 12 weeks and karyotyping dye-free body exception it is pregnant
Woman's sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference.
Using Bei Rui and the foetal chromosome aneuploidy of health (T13/T18/T21) detection kit, (reversible end is terminated
PCR sequencing PCR) build NIPT libraries, comprise the following steps that：
(1) 7 pregnant woman blood plasma dissociative DNAs are taken, numbering is Y1-Y7, and karyotyping result is shown, 2 are negative sample
(Y6, Y7), 1 is No. 21 chromosome trisomies (Y1), and 1 is No. 18 chromosome trisomies (Y2), and 1 is No. 13 chromosome trisomies
(Y3), 1 is the body of X chromosome three (Y5), and 1 is No. 15 micro- repetitions of chromosome parent (Y4), and reaction system group is added according to table 1
Point：
Table 1
| The plasma DNA of purifying | 40.5μl |
| Buffer solution 1 | 7μl |
| Enzyme 1 | 1.5μl |
| Cumulative volume | 49μl |
It is soft to mix.Reacted in brief centrifugation, PCR instrument according to the program of table 2：
Table 2
| 37℃ | 20min |
| 72℃ | 20min |
| 4℃ | Hold |
Place at once on ice afterwards, immediately enter next step joint coupled reaction.
(2) system component is added into above-mentioned product according to table 3：
Table 3
Reacted in soft mixing, brief centrifugation, PCR instrument according to the program of table 4：
Table 4
| 20℃ | 15min |
| 65℃ | 10min |
| 4℃ | Hold |
(3) purified product：
A. the magnetic bead 28ul of mixing is added into above-mentioned product, is well mixed；Room temperature places 5min；
B. of short duration centrifugation, sample is placed on magnetic frame and stands 5min；
C. after solution clarification, supernatant is abandoned；
D. 200ul lavation buffer solutions are added, 30s is placed, discarding supernatant is carefully drawn with pipette tips；
E. 200ul lavation buffer solutions are added, 30s is placed, discarding supernatant is carefully drawn with pipette tips；
F. brief centrifugation, draws remaining waste liquid with 10ul pipette tips and abandons；
G. room temperature is placed 3min and dried；
H. 22ul buffer solution T are added, are well mixed, room temperature places 5min；
I. of short duration centrifugation, sample is placed on magnetic frame and stands 2min；
J. 20ul supernatants are transferred in clean collecting pipe after solution clarification, carry out next step operation.
(4) sequencing data is obtained using SE75 sequencings on NextSeq CN500 behind the quantitative libraries of qPCR.
2) UR ratio are calculated：Bearing calibration according to the sequencing GC Preferences of the present invention is eliminated in chromosome and chromosome
Between be sequenced GC Preferences, in reference database sample unique reads numbers carry out GC corrections, then calculate every dyeing
Body UR ratio；
Table 1 is 7 sample correction prochromosomes 21,18,13, X, 15 UR ratio
3) reference database statistics parameter is built：According to step 2) the middle UR ratio obtained, calculate control sample storehouse
In every autosome UR ratio average and standard error；
4) Z values are calculated：The plasma DNA of pregnant woman's sample to be measured is sequenced, sequencing data is according to above-mentioned sequencing GC
The bearing calibration of Preference is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC schools are carried out to unique reads numbers
Just, every chromosome UR ratio is then calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
Table 3 is the corresponding z values of 7 sample portion chromosomes
5) there is the non-multiple of fetal chromosomal that micro-deleted or micro- repetition is caused according to above-mentioned reduction parent itself chromosome
The method of body false positive reduces parent itself chromosome and there is the foetal chromosome aneuploidy vacation that micro-deleted or micro- repetition is caused
It is positive；
6) the foetal DNA concentration prediction model built according to the construction method of above-mentioned foetal DNA concentration prediction model is predicted
Foetal DNA concentration；
| Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Y7 | |
| Foetal DNA concentration | 0.1084 | 0.1311 | 0.1643 | 0.1881 | 0.1770 | 0.1934 | 0.01542 |
7) the DNA quantity for calculating every chromosome of fetus accounts for the percentage of overall dna：Calculate every dyeing in sample to be tested
The ratio of the UR ratio of body and the deviation of control sample storehouse check sample, the difference and check sample UR ratio average value
2 times then account for the ratio of overall dna for the DNA of fetus, formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of overall dna；
| fr21 | fr18 | fr13 | frX | fr15 | |
| Y1 | 0.1036 | 0.0217 | 0.0081 | 0.0711 | 0.0378 |
| Y2 | 0.0365 | 0.1206 | 0.0177 | 0.0315 | 0.0554 |
| Y3 | 0.0434 | 0.0545 | 0.1869 | 0.0503 | 0.0383 |
| Y4 | 0.0409 | 0.0157 | 0.0095 | 0.0860 | 0.4662 |
| Y5 | 0.0363 | 0.0008 | 0.0372 | 0.2091 | 0.0203 |
| Y6 | 0.0297 | 0.0412 | 0.0413 | 0.0552 | 0.0546 |
| Y7 | 0.0145 | 0.0479 | 0.0446 | 0.0474 | 0.0591 |
8) judgement of autosome aneuploid：
According to step 4) correct three sets of different Z that Correction Strategies calculate every chromosome for three sets of different GC
Value, triplicity come the value that synthetically judges UR ratio it is whether statistically significant on exception, if specifically, three sets differences
ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetal concentrations, then judge No. i-th chromosome for aneuploid；
Sample Y1-Y3 Z21, Z18, Z13 is all higher than 3, and corresponding fr values are approached with fetal concentrations, therefore is judged as respectively
T21、T18、T13.
Because Y4 samples fr15 is much larger than fetal concentrations, by examining parent leucocyte may determine that, parent chr15 is present
Micro- repetition, therefore it is the vacation sun caused by the micro- repetition of parent that Z15 is bigger than normal.
The z values of all chromosomes of sample Y6-Y7 are respectively less than 3, belong to normal range (NR), therefore, are judged as feminine gender, chromosome is not
In the presence of exception.
9) sex chromosomal abnormality judges：
According to above-mentioned steps obtain maternal sample Y5 to be tested X chromosome and the UR ratio of Y chromosome and fetus it is dense
Three parameters are spent, the forecast model that sex chromosome is analyzed that is used for described by claim 4 are substituted into, you can judge Y5 for XXY.
In step 8) in calculate three sets of different Z values, triplicity synthetically judges whether UR ratio value has
Exception on statistical significance, three kinds of parallel detection strategies considerably increase the confidence level of detection.Generally, Z is more than 3 or small
In -3 be statistical significance on exception, the present invention in only three values be feminine gender, be just judged to feminine gender, otherwise can be pointedly
Depth analysis.
In addition, in addition to the above described embodiments, wearing pregnant to 100 reservation sheep of 301 Hospital using technical scheme
Woman is analyzed, find in 100 NIPT and sheep wear results of comparison only have 1 it is inconsistent, sequence is resurveyed 6 times in sample reconstruction storehouse,
It is notable T13；But it is feminine gender that sheep, which wears result,.It is still T13 through the auspicious checking of shellfish after sample, doubtful placenta is chimeric to cause T13 false
Sun.More than 10,000 example samples are also have detected, total positives result is worn the Given informations such as result with sheep and compared, and accuracy reaches
100%, wherein, successfully eliminate No. 7 chromosomes of a mother and there are caused No. 7 chromosome trisomies of fetus of large fragment repetition
False positive, No. 10 chromosomes of a mother exist large fragment repeat caused by No. 7 chromosome trisomies of fetus false positive；Sheep
Wear the result and quote the micro- repetition of parent with NIPT results.
In summary, embodiments of the invention have been at least up to following technique effect.
1) fetal concentrations can be effectively assessed, decision model is set up, can guarantee that testing result is accurate when fetal concentrations are low；
2) fetus all 23 pairs of chromosome aneuploid exceptions can be detected；
3) it can be used for polyembryony detection；
4) fluctuation between batch can effectively be corrected；
5) it is applied to the abnormal situation of maternal DNA, can effectively rejects the abnormal influence of parent.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (9)
1. a kind of bearing calibration of sequencing GC Preferences, it is characterised in that comprise the following steps：
1) testing sample is sequenced using high-flux sequence platform；
2) check sample after obtained base sequence removal joint and low-quality base sequence with reference database will be sequenced
Reference gene group sequence be compared, statistics every chromosome on unique comparison to reference gene group sequence bar number, i.e.,
Unique Read bar number, and calculate the percentage that the total amount of Unique Read on every chromosome accounts for total Unique Reads
Than i.e. UR ratio；
3) sequencing GC Preferences correction：Data prediction, then carries out GC corrections using three sets of different Correction Strategies simultaneously；
Wherein, data prediction includes：Whole chromosome is divided into the window of 100kb clip sizes, overlapping region is 50kb, counted
The G/C content of each sequencing sequence in each window is calculated, while calculating the unique reads numbers in window, ignores uncertain with sequencing
Base N, unique reads be 0 or abnormal those high windows, then, normalizing is carried out to the unique reads numbers in window
Change, i.e., divided by reference database check sample corresponding window in unique reads numbers；
Three sets of different Correction Strategies include：
First, GC corrections are carried out using Local Polynomial weighted regression method：After data prediction, the unique in window is utilized
Reads numbers make Local Polynomial weighted regression with the G/C content in window, recycle normalized unique reads numbers in window to remove
The estimate obtained with Local Polynomial weighted regression calculates the unique reads of each window numerical value, and then realizes to every
The GC corrections of Unique reads on chromosome；
2nd, GC corrections are carried out using rolling median method：After data prediction, G/C content fenestrate in all chromosomes is entered
Row sequence, with 0.1%GC value differences it is different it is fenestrate to institute be grouped, count the fenestrate Unique reads in each GC values group
Number, calculates its median as the GC weights of this group of sequencing sequence, recycle in window normalized unique reads numbers divided by
The GC weights of the group, so as to obtain the unique reads numbers after GC corrections；
3rd, GC corrections are carried out using linear regression method：After data prediction, every chromosome is estimated using least square method
Normalization unique reads numbers and GC and 1/GC regression equation, unique reads estimate is obtained, using in window
The estimate of normalized unique reads numbers divided by its correspondence window, so as to realize the Unique reads to every chromosome
Several GC corrections.
2. a kind of correction system of sequencing GC Preferences, it is characterised in that including：
Sequencer module：For testing sample to be sequenced using high-flux sequence platform；
Compare statistical module：For that will be sequenced after obtained base sequence removal joint and low-quality base sequence and reference number
It is compared according to the reference gene group sequence of the check sample in storehouse, the bar number of the Unique Read on every chromosome of statistics, and
The total amount for calculating Unique Read on every chromosome accounts for total Unique Reads percentage UR ratio；
Rectification module：For the correction of GC Preferences to be sequenced；
The rectification module includes data prediction submodule and correction submodule,
Wherein, the data prediction submodule：Window for whole chromosome to be divided into 100kb clip sizes, overlay region
Domain is 50kb, calculates the G/C content of each sequencing sequence in each window, while calculating the unique reads numbers in window, ignores and carries
It is 0 or abnormal those high windows that uncertain base N, unique reads, which is sequenced, then, to the unique reads in window
Number be normalized, i.e., divided by reference database check sample corresponding window in unique reads numbers；
The correction submodule is used to carry out GC corrections, three sets of different corrections using three sets of different Correction Strategies simultaneously
Strategy includes：
First, GC corrections are carried out using Local Polynomial weighted regression method：After data prediction, the unique in window is utilized
Reads numbers make Local Polynomial weighted regression with the G/C content in window, recycle normalized unique reads numbers in window to remove
The estimate obtained with Local Polynomial weighted regression calculates the unique reads of each window numerical value, and then realizes to every
The GC corrections of Unique reads on chromosome；
2nd, GC corrections are carried out using rolling median method：After data prediction, G/C content fenestrate in all chromosomes is entered
Row sequence, with 0.1%GC value differences it is different it is fenestrate to institute be grouped, count the fenestrate Unique reads in each GC values group
Number, calculates its median as the GC weights of this group of sequencing sequence, recycle in window normalized unique reads numbers divided by
The GC weights of the group, so as to obtain the unique reads numbers after GC corrections；
3rd, GC corrections are carried out using linear regression method：After data prediction, every chromosome is estimated using least square method
Normalization unique reads numbers and GC and 1/GC regression equation, unique reads estimate is obtained, using in window
The estimate of normalized unique reads numbers divided by its correspondence window, so as to realize the Unique reads to every chromosome
Several GC corrections.
3. a kind of construction method of foetal DNA concentration prediction model, it is characterised in that comprise the following steps：
1) pregnant woman for nourishing normal male tire of selection certain amount karyotyping dye-free body exception and pregnant week more than or equal to 12 weeks
Sample, a number of healthy male and women sample, carry out gene sequencing to the plasma DNA sample of these samples, survey
Ordinal number is right according to according to GC Preferences are sequenced with interchromosomal in bearing calibration as claimed in claim 1 elimination chromosome
Unique reads numbers carry out GC corrections, then calculate the UR ratio of the Y chromosome of these samples, obtain nourishing normal male tire
Maternal sample Y chromosome UR ratio RSample, women sample Y chromosome UR ratio Rfemale, male's sample
Y chromosome UR ratio Rmale；
2) fetal concentrations are calculated according to equation below：Male tire foetal DNA concentration=(RSample-Rfemale)/(Rmale-Rfemale), then,
The fragment length of the sequencing sequence of the maternal sample for nourishing normal man's tire is calculated, and counts the distribution situation of fragment length,
Statistics parameter fragment length percentage is obtained by calculating the percentage of different fragments length, by fetal concentrations and fragment length
Percentage does linear regression, linear regression model (LRM) is built, then by the linear regression model (LRM) according to the pregnant woman for nourishing female's tire
The DNA concentration of the fragment length percent prediction female tire fetus of sample.
4. a kind of construction method for the forecast model analyzed for sex chromosome, it is characterised in that comprise the following steps：
1) choose certain amount karyotyping dye-free body exception and maternal sample of the pregnant week more than or equal to 12 weeks is used as reference number
According to the control sample in storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；Identical number is selected in addition
XO, XXX, XXY, XYY sample verified through karyotyping of amount, the six classes sample are used as the test set for building model；
2) gene sequencing is carried out to the DNA sample extracted in the blood plasma of the test set, sequencing data is according to as claimed in claim 1
Bearing calibration is eliminated in chromosome and GC Preferences are sequenced in interchromosomal, and GC corrections, Ran Houji are carried out to unique reads numbers
Calculate every chromosome UR ratio；Simultaneously according to the construction method structure of foetal DNA concentration prediction model as claimed in claim 3
Forecast model is built, fetal concentrations are calculated using fragment length percentage；
3) for three parameters of UR ratio and fetal concentrations of X chromosome and Y chromosome, using the support based on binary tree to
Amount machine Multiclass Classification, the forecast model that cross validation is stablized is rolled over using k.
5. there is the foetal chromosome aneuploidy false positive that micro-deleted or micro- repetition is caused in a kind of reduction parent itself chromosome
Method, it is characterised in that comprise the following steps：
1) plasma DNA for extracting parent to be measured is sequenced, and sequencing data is according to bearing calibration as claimed in claim 1
Eliminate in chromosome and GC Preferences are sequenced in interchromosomal, GC corrections are carried out to unique reads numbers；
2) the Z values of test sample data in each window are calculated；By the average value of the Z values on some chromosome of test sample
It is compared with corresponding threshold value, so that judge whether the chromosome lacks or repeat, and the region for lacking or repeating；
3) when foetal chromosome aneuploidy detection Z values are calculated, the window that there is missing in test sample or repeat is filtered out,
So as to reduce the false positive that parent itself chromosome has the foetal chromosome aneuploidy that micro-deleted or micro- repetition is caused.
6. a kind of analysis method of aneuploidy biological information, it is characterised in that comprise the following steps：
1) reference database is built：Certain amount pregnant week is chosen to be more than or equal to 12 weeks and the abnormal pregnant woman of karyotyping dye-free body
Sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
2) UR ratio are calculated：Eliminated according to bearing calibration as claimed in claim 1 in chromosome and GC is sequenced in interchromosomal
Preference, carries out GC corrections to the unique reads numbers of sample in reference database, then calculates every chromosome UR
ratio；
3) reference database statistics parameter is built：According to step 2) the middle UR ratio obtained, calculate every in control sample storehouse
Bar autosome UR ratio average and standard error；
4) Z values are calculated：The plasma DNA of pregnant woman's sample to be measured is sequenced, sequencing data is according to as claimed in claim 1
Bearing calibration eliminate chromosome in and interchromosomal be sequenced GC Preferences, to unique reads numbers carry out GC corrections, then
Every chromosome UR ratio is calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
5) reduce parent itself chromosome according to method as claimed in claim 5 and there is the fetus that micro-deleted or micro- repetition is caused
Chromosome aneuploid false positive；
6) the foetal DNA concentration prediction model built according to construction method as claimed in claim 3 predicts foetal DNA concentration；
7) the DNA quantity for calculating every chromosome of fetus accounts for the percentage of overall dna：Calculate every chromosome in sample to be tested
The 2 of the ratio of UR ratio and control sample storehouse check sample deviation, the difference and check sample UR ratio average value
The ratio of overall dna then is accounted for for the DNA of fetus again, formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of No. i-th chromosome STb gene of fetus and parent；
8) judgement of autosome aneuploid：
According to the step 4) correct three sets of different Z that Correction Strategies calculate every chromosome for three sets of different GC
Value, triplicity come the value that synthetically judges UR ratio it is whether statistically significant on exception, if specifically, three sets differences
ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetal concentrations, then judge No. i-th chromosome for aneuploid；
9) sex chromosomal abnormality judges：
Forecast model is built by being used for the construction method for the forecast model that sex chromosome is analyzed as claimed in claim 4, it is right
The sex chromosomal abnormality situation of test maternal sample is judged.
7. analysis method according to claim 6, it is characterised in that the step 8) further comprise according to calculating pair
Number likelihood ratio L values, and according to the judgement of L values progress autosome aneuploid.
8. a kind of analysis system of aneuploidy biological information, it is characterised in that including：
Reference database builds module：For choosing, certain amount pregnant week is more than or equal to 12 weeks and karyotyping dye-free body is abnormal
Maternal sample as control sample storehouse, wherein, it is desirable to nourish the maternal sample quantity of male tire and female's tire without significant difference；
UR ratio computing modules：For being eliminated according to bearing calibration as claimed in claim 1 in chromosome and interchromosomal
GC Preferences are sequenced, GC corrections are carried out to the unique reads numbers of sample in reference database, every chromosome is then calculated
UR ratio；
Reference database statistics parameter builds module：For according to the UR ratio obtained in UR ratio computing modules, meter
Calculate the average and standard error of every autosome UR ratio in control sample storehouse；
Z value computing modules：It is sequenced for the plasma DNA to pregnant woman's sample to be measured, sequencing data will according to such as right
Ask the bearing calibration described in 1 to eliminate in chromosome and interchromosomal sequencing GC Preferences, GC schools are carried out to unique reads numbers
Just, every chromosome UR ratio is then calculated, its autosomal Z value is calculated：
Zi=(xi-μi)/σi
i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
σi：The UR ratio of No. i-th chromosome standard error in control sample storehouse；
Parent itself microdeletion micro- computes repeatedly module：It is female for being reduced according to method as claimed in claim 5
There is the foetal chromosome aneuploidy false positive that micro-deleted or micro- repetition is caused in body itself chromosome；
Foetal DNA concentration prediction module：Foetal DNA concentration for being built according to construction method as claimed in claim 3 is pre-
Survey model prediction foetal DNA concentration；
Foetal DNA number calculating section：The percentage of overall dna is accounted for for calculating the DNA quantity of every chromosome of fetus：Calculate
The deviation of the long check sample of UR ratio and control sample storehouse of every chromosome in sample to be tested, the difference and check sample UR
2 times of the ratio of ratio average value then account for the ratio of overall dna for the DNA of fetus, and formula is：
fri=(xi-μi)/μi*2
Wherein, i：Chromosome numbers；
xi：The UR ratio of No. i-th chromosome in analyze data；
μi：The UR ratio of No. i-th chromosome average value in control sample storehouse；
fri：The DNA quantity of No. i-th chromosome of fetus accounts for the percentage of No. i-th chromosome STb gene of fetus and parent；
The judge module of autosome aneuploid：For according in the Z values computing module for three sets different GC corrections
Correction Strategies calculate three sets of different Z values, and whether triplicity is statistically significant come the value for synthetically judging UR ratio
On exception, if specifically, three sets of different ZiEqual Zi＞ 3 (i=1,2 ..., 22), and friClose to fetal concentrations, then is judged
I chromosomes are aneuploid；
Sex chromosomal abnormality judge module：For by being used for the forecast model that sex chromosome is analyzed as claimed in claim 4
Construction method build forecast model, to test maternal sample sex chromosomal abnormality situation judge.
9. analysis system according to claim 8, it is characterised in that in the judge module of the autosome aneuploid
Further comprise according to calculating log-likelihood ratio L values, and according to the judgement of L values progress autosome aneuploid.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710310451.9A CN107133495B (en) | 2017-05-04 | 2017-05-04 | A kind of analysis method and analysis system of aneuploidy biological information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710310451.9A CN107133495B (en) | 2017-05-04 | 2017-05-04 | A kind of analysis method and analysis system of aneuploidy biological information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107133495A true CN107133495A (en) | 2017-09-05 |
| CN107133495B CN107133495B (en) | 2018-07-13 |
Family
ID=59731394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710310451.9A Active CN107133495B (en) | 2017-05-04 | 2017-05-04 | A kind of analysis method and analysis system of aneuploidy biological information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107133495B (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108256296A (en) * | 2017-12-29 | 2018-07-06 | 北京科迅生物技术有限公司 | Data processing method and device |
| CN108388770A (en) * | 2018-03-01 | 2018-08-10 | 北京乐普基因科技股份有限公司 | The noninvasive antenatal bioinformatics detecting system of one kind and its methods and applications |
| CN108595912A (en) * | 2018-05-07 | 2018-09-28 | 深圳市瀚海基因生物科技有限公司 | Detect the method, apparatus and system of chromosomal aneuploidy |
| CN109616154A (en) * | 2018-12-27 | 2019-04-12 | 北京优迅医学检验实验室有限公司 | The antidote and device of depth is sequenced |
| CN110211654A (en) * | 2019-05-30 | 2019-09-06 | 湖南自兴智慧医疗科技有限公司 | A kind of the caryogram detection system and method for automatic hiding gender information |
| WO2019213810A1 (en) * | 2018-05-07 | 2019-11-14 | 深圳市真迈生物科技有限公司 | Method, apparatus, and system for detecting chromosome aneuploidy |
| WO2019213811A1 (en) * | 2018-05-07 | 2019-11-14 | 深圳市真迈生物科技有限公司 | Method, apparatus, and system for detecting chromosomal aneuploidy |
| CN110580934A (en) * | 2019-07-19 | 2019-12-17 | 南方医科大学 | method for predicting pregnancy-related diseases based on peripheral blood free DNA high-throughput sequencing |
| CN110970089A (en) * | 2019-11-29 | 2020-04-07 | 北京优迅医疗器械有限公司 | Preprocessing method and preprocessing device for fetal concentration calculation and application of preprocessing method and device |
| CN110993029A (en) * | 2019-12-26 | 2020-04-10 | 北京优迅医学检验实验室有限公司 | Method and system for detecting chromosome abnormality |
| CN111175480A (en) * | 2020-01-13 | 2020-05-19 | 北京奇云诺德信息科技有限公司 | Method for calculating gender and age by blood biochemical indexes |
| CN112037846A (en) * | 2020-07-14 | 2020-12-04 | 广州市达瑞生物技术股份有限公司 | cffDNA aneuploidy detection method, system, storage medium and detection equipment |
| CN112652359A (en) * | 2020-12-30 | 2021-04-13 | 安诺优达基因科技(北京)有限公司 | Chromosome abnormality detection device |
| CN112712853A (en) * | 2020-12-31 | 2021-04-27 | 北京优迅医学检验实验室有限公司 | Noninvasive prenatal detection device |
| CN113270138A (en) * | 2021-04-13 | 2021-08-17 | 杭州博圣医学检验实验室有限公司 | Method for enriching fetal free DNA for analyzing copy number variation based on bioinformatics |
| WO2021243650A1 (en) * | 2020-06-04 | 2021-12-09 | 深圳华大基因股份有限公司 | Method for determining pregnancy status of pregnant woman |
| WO2024011929A1 (en) * | 2022-07-13 | 2024-01-18 | 深圳华大基因股份有限公司 | Method and apparatus for detecting fetal chromosomal aneuploidy, and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103525939A (en) * | 2013-10-28 | 2014-01-22 | 广州爱健生物技术有限公司 | Method and system for noninvasive detection of fetus chromosome aneuploid |
| CN104156631A (en) * | 2014-07-14 | 2014-11-19 | 天津华大基因科技有限公司 | Triploid testing method for chromosomes |
| CN105825076A (en) * | 2015-01-08 | 2016-08-03 | 北京圣庭生物技术有限公司 | Method for removing GC preferences in euchromosomes and between chromosomes as well as detection system |
| CN105844116A (en) * | 2016-03-18 | 2016-08-10 | 广州市锐博生物科技有限公司 | Processing method and processing apparatus for sequencing data |
-
2017
- 2017-05-04 CN CN201710310451.9A patent/CN107133495B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103525939A (en) * | 2013-10-28 | 2014-01-22 | 广州爱健生物技术有限公司 | Method and system for noninvasive detection of fetus chromosome aneuploid |
| CN104156631A (en) * | 2014-07-14 | 2014-11-19 | 天津华大基因科技有限公司 | Triploid testing method for chromosomes |
| CN105825076A (en) * | 2015-01-08 | 2016-08-03 | 北京圣庭生物技术有限公司 | Method for removing GC preferences in euchromosomes and between chromosomes as well as detection system |
| CN105844116A (en) * | 2016-03-18 | 2016-08-10 | 广州市锐博生物科技有限公司 | Processing method and processing apparatus for sequencing data |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108256296A (en) * | 2017-12-29 | 2018-07-06 | 北京科迅生物技术有限公司 | Data processing method and device |
| CN108256296B (en) * | 2017-12-29 | 2021-05-25 | 北京科迅生物技术有限公司 | Data processing apparatus |
| CN108388770A (en) * | 2018-03-01 | 2018-08-10 | 北京乐普基因科技股份有限公司 | The noninvasive antenatal bioinformatics detecting system of one kind and its methods and applications |
| CN111919256A (en) * | 2018-05-07 | 2020-11-10 | 深圳市真迈生物科技有限公司 | Method, device and system for detecting chromosome aneuploidy |
| CN108595912A (en) * | 2018-05-07 | 2018-09-28 | 深圳市瀚海基因生物科技有限公司 | Detect the method, apparatus and system of chromosomal aneuploidy |
| WO2019213810A1 (en) * | 2018-05-07 | 2019-11-14 | 深圳市真迈生物科技有限公司 | Method, apparatus, and system for detecting chromosome aneuploidy |
| WO2019213811A1 (en) * | 2018-05-07 | 2019-11-14 | 深圳市真迈生物科技有限公司 | Method, apparatus, and system for detecting chromosomal aneuploidy |
| CN108595912B (en) * | 2018-05-07 | 2023-12-19 | 深圳市真迈生物科技有限公司 | Method, device and system for detecting chromosome aneuploidy |
| CN109616154A (en) * | 2018-12-27 | 2019-04-12 | 北京优迅医学检验实验室有限公司 | The antidote and device of depth is sequenced |
| CN110211654A (en) * | 2019-05-30 | 2019-09-06 | 湖南自兴智慧医疗科技有限公司 | A kind of the caryogram detection system and method for automatic hiding gender information |
| CN110580934B (en) * | 2019-07-19 | 2022-05-10 | 南方医科大学 | Pregnancy related disease prediction method based on peripheral blood free DNA high-throughput sequencing |
| CN110580934A (en) * | 2019-07-19 | 2019-12-17 | 南方医科大学 | method for predicting pregnancy-related diseases based on peripheral blood free DNA high-throughput sequencing |
| CN110970089B (en) * | 2019-11-29 | 2023-05-23 | 北京优迅医疗器械有限公司 | Pretreatment method and pretreatment device for fetal concentration calculation and application of pretreatment device |
| CN110970089A (en) * | 2019-11-29 | 2020-04-07 | 北京优迅医疗器械有限公司 | Preprocessing method and preprocessing device for fetal concentration calculation and application of preprocessing method and device |
| CN110993029B (en) * | 2019-12-26 | 2023-09-05 | 北京优迅医学检验实验室有限公司 | Method and system for detecting chromosome abnormality |
| CN110993029A (en) * | 2019-12-26 | 2020-04-10 | 北京优迅医学检验实验室有限公司 | Method and system for detecting chromosome abnormality |
| CN111175480A (en) * | 2020-01-13 | 2020-05-19 | 北京奇云诺德信息科技有限公司 | Method for calculating gender and age by blood biochemical indexes |
| WO2021243650A1 (en) * | 2020-06-04 | 2021-12-09 | 深圳华大基因股份有限公司 | Method for determining pregnancy status of pregnant woman |
| CN112037846A (en) * | 2020-07-14 | 2020-12-04 | 广州市达瑞生物技术股份有限公司 | cffDNA aneuploidy detection method, system, storage medium and detection equipment |
| CN112652359A (en) * | 2020-12-30 | 2021-04-13 | 安诺优达基因科技(北京)有限公司 | Chromosome abnormality detection device |
| CN112652359B (en) * | 2020-12-30 | 2024-05-28 | 安诺优达基因科技(北京)有限公司 | Chromosome abnormality detection device |
| CN112712853A (en) * | 2020-12-31 | 2021-04-27 | 北京优迅医学检验实验室有限公司 | Noninvasive prenatal detection device |
| CN112712853B (en) * | 2020-12-31 | 2023-11-21 | 北京优迅医学检验实验室有限公司 | Noninvasive prenatal detection device |
| CN113270138A (en) * | 2021-04-13 | 2021-08-17 | 杭州博圣医学检验实验室有限公司 | Method for enriching fetal free DNA for analyzing copy number variation based on bioinformatics |
| CN113270138B (en) * | 2021-04-13 | 2023-09-22 | 杭州博圣医学检验实验室有限公司 | Analysis method for enriching fetal free DNA (deoxyribonucleic acid) for copy number variation based on bioinformatics |
| WO2024011929A1 (en) * | 2022-07-13 | 2024-01-18 | 深圳华大基因股份有限公司 | Method and apparatus for detecting fetal chromosomal aneuploidy, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107133495B (en) | 2018-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107133495B (en) | A kind of analysis method and analysis system of aneuploidy biological information | |
| CN103525939B (en) | The method and system of Non-invasive detection foetal chromosome aneuploidy | |
| CN104169929B (en) | For determining system and the device of fetus whether existence numerical abnormalities of chromosomes | |
| CN108778287B (en) | Methods and systems for early risk assessment of preterm birth outcomes | |
| CN106096330B (en) | A kind of noninvasive antenatal biological information determination method | |
| CN104968800A (en) | Method of detecting chromosomal abnormalities | |
| CN104951671B (en) | The device of fetal chromosomal aneuploidy is detected based on single sample peripheral blood | |
| EP3226163A1 (en) | Trait prediction model creation method and trait prediction method | |
| CN104156631A (en) | Triploid testing method for chromosomes | |
| CN106537401A (en) | Method for expecting fetal single nucleotide polymorphisms using maternal serum DNA | |
| CN108604258B (en) | Chromosome abnormality determination method | |
| EP4086356A1 (en) | Methods for determining chromosome aneuploidy and constructing classification model, and device | |
| CN107463797B (en) | Biological information analysis method and device for high-throughput sequencing, equipment and storage medium | |
| CN106778069A (en) | Determine the method and apparatus of micro-deleted micro- repetition in fetal chromosomal | |
| CN106591451A (en) | Method for detecting content of fetal-free DNA, and apparatus for enforcing method | |
| US20210366569A1 (en) | Limit of detection based quality control metric | |
| CN110191964B (en) | Method and device for determining proportion of free nucleic acid of predetermined source in biological sample | |
| US20230115196A1 (en) | Method for determining pregnancy status of pregnant woman | |
| JP6564053B2 (en) | A method for determining whether cells or cell groups are the same person, whether they are others, whether they are parents and children, or whether they are related | |
| CN108229099A (en) | Data processing method, device, storage medium and processor | |
| CN108588218A (en) | A kind of minimally invasive detection kit of serum miRNA combination | |
| CN109321641B (en) | A kind of antenatal noninvasive fetal chromosomal detection system based on DNA fragmentation enrichment and sequencing technologies | |
| Mahdi et al. | The discriminant analysis in the evaluation of cancers diseases in Iraq | |
| CN117095745A (en) | Method and device for detecting fetal aneuploidy and copy number variation in maternal plasma free DNA and application thereof | |
| KR20210130680A (en) | Non-invasive prenatal testing method and devices based on double Z-score |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |