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WO2016068625A1 - Procédé d'élimination du biais dans l'analyse de séquences cibles de nucléotides par nmf - Google Patents

Procédé d'élimination du biais dans l'analyse de séquences cibles de nucléotides par nmf Download PDF

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Publication number
WO2016068625A1
WO2016068625A1 PCT/KR2015/011513 KR2015011513W WO2016068625A1 WO 2016068625 A1 WO2016068625 A1 WO 2016068625A1 KR 2015011513 W KR2015011513 W KR 2015011513W WO 2016068625 A1 WO2016068625 A1 WO 2016068625A1
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bias
region
vector
target
control
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Korean (ko)
Inventor
윤태균
이병철
박동윤
박정선
이정호
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SK Telecom Co Ltd
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SK Telecom Co Ltd
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic 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
    • 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
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • 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
    • G16B99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present invention relates to a method for removing bias in target sequencing, and to a method for providing information to accurately determine somatic cell copy number variation by removing a bias generated in sequence readout for a cancer sample for each region.
  • somatic mutations such as point mutations in many cancers, DNA copy numbers, and chromosomes
  • somatic copy number variation refers to a variation in the number of copies of genes that do not exist in normal cells, and has been highly associated with the onset of cancer.
  • One embodiment produces experimental and control vectors based on a read count calculated by read mapping the test sample sequencing data and the control sample sequencing data to standard reference sequencing data, the regions of which are generated in the experimental and control vectors. 1 , such as through non-negative matrix factorization ("NMF"), removes the bias first, and selects the indifference region to remove the noise second, so that it is possible Provided are a bias removal technique in target sequencing and a target sequencing technique using the same, which can increase sensitivity.
  • NMF non-negative matrix factorization
  • One embodiment provides a method for bias removal in target sequencing using non-negative matrix factorization (NMF).
  • NMF non-negative matrix factorization
  • the bias removal method in the target sequencing (1) Read mapping of test sample sequencing data and control sample sequencing data to standard reference sequencing data for each chromosomal position
  • the bias removal method may be a bias removal method in target sequencing performed in a bias removal device in target sequencing.
  • the test sample sequencing data and the control sample sequencing data of step (1) may each independently or directly indirectly receive sequence data generated by a genome sequencer, or a computer readable data storing stored sequence data. Can be obtained (prepared) through a storage medium.
  • the bias removal method in the target sequencing analysis before step (1), adds a step of preparing experimental sample sequencing data and control sample sequencing data.
  • the test sample sequencing data and the control sample sequencing data may be each independently, directly or indirectly receiving sequence data generated by a genomic sequence analyzer, or stored already stored sequence data. Can be prepared by applying a computer readable storage medium .
  • the first bias removal step may be performed using non-negative matrix factorization (NMF).
  • NMF non-negative matrix factorization
  • the bias removal method in the target sequencing analysis after the first bias removal step, for example, between the steps (3) and (4), the following step (secondary bias removal step) is added
  • the following step (secondary bias removal step) is added
  • Another example provides a computer read method for target sequencing comprising the bias removal method.
  • Another example provides a computer program stored in a computer readable storage medium for carrying out the steps of the bias removal method.
  • Another example provides a system for performing the steps of the bias removal method.
  • Another example provides a computer readable storage medium (or recording medium) containing computer executable instructions for executing the steps of the bias removal method.
  • Another example provides a computer readable storage medium (or recording medium) containing a computer executable instruction for executing a computer read method of a red base sequence including the bias removal method.
  • FIG. 1 is a block diagram illustrating a bias removal system in target sequencing according to an embodiment.
  • FIG. 2 illustrates an apparatus in which a bias removal method is performed according to an embodiment. It is a block diagram for.
  • FIG. 4 is a diagram illustrating a process of generating an experimental group vector based on experimental sample sequence data in a bias removing method according to an embodiment.
  • FIG. 5 is a diagram illustrating a process of generating an experimental group vector and a control vector in a bias removal method according to an exemplary embodiment.
  • FIG. 6 is a diagram illustrating a process of dividing a region-by-test group vector with a control vector in a bias removal method according to an embodiment.
  • FIG. 7 is a graph illustrating a TRR vector for the number of other regions before and after removing a bias in the bias removing method according to an exemplary embodiment.
  • FIG. 9 is a flowchart illustrating a method of removing a bias in target sequencing according to an embodiment of the present invention.
  • a sample containing a nucleotide sequence which may be a cancer sample (ie, a genome (DNA and / or RNA) sample extracted from cancer cells), and the control sample is a normal sample (ie, a genome extracted from normal cells (DNA and / or RNA) RNA) sample).
  • the test sample and control sample may be (isolated) cells, tissues, or tissues obtained from an animal, such as a mammal, including a human.
  • Genomic DNA and / or RNA samples extracted from them.
  • the genome means DNA and / or RNA of all or part of the genome or chromosome.
  • target sequencing is for identifying somatic copy number variation, and may be sequencing of target region for identifying genome copy number variation in a target region.
  • System 1 may include genomic sequence analyzer 100 and bias removal apparatus 300 in target sequencing.
  • the experimental sample bias removal, system 1 in this Figure 1 is limited to the present invention with reference to FIG 1 a block in one embodiment of the present invention and hakkeuk
  • Each component of FIG. 1 may be connected via a network 200.
  • genomic sequence through network 200.
  • the analyzer 100 and the bias removal device 300 in target sequencing can be connected.
  • the control sample base sequence data generated by the genome sequence analyzer 100 And / or only the experimental sample sequencing data need be received by the bias removal device 300 in the target sequencing, thus including both direct or indirect connections.
  • the genomic sequence analyzer 100 and the bias removal apparatus 300 in target sequencing may be directly connected through the network 200 or may be connected through a storage space on the Internet such as Webhard.
  • the control sample sequencing data and / or experimental sample sequencing data generated by the genetic agent reader can be stored in a computer readable storage medium and applied to the bias removal device.
  • the network 200 refers to a connection structure capable of exchanging information between each node, such as terminals and servers
  • an example of such a network 200 is WCDMA, Internet (Internet), LAN (Local) Area Network (WLAN), Wireless Local Area Network (WLAN), Wide Area Network (WAN), Personal Area Network (PAN), El networks using ATM, 3G, 4G, LTE, and Wi-Fi It doesn't work.
  • the genomic sequence analyzer 100 disclosed in FIG. 1 and the bias removal apparatus 300 in target sequencing are not limited to those shown in FIG. 1.
  • the genomic sequence analyzer 100 may refer to any device capable of amplifying DNA sequence, and then photographing fluorescent labels and the like by photographing means and performing image processing to parallelize DNA genetic information.
  • the genomic sequence analyzer may be a device capable of performing massively parallel sequencing techniques such as Next Generation Sequencing (NGS), but is not limited thereto.
  • NGS Next Generation Sequencing
  • sequence information of polynucleotide fragments can be obtained using commercially available sequencing instruments.
  • the genomic sequence analyzer 100 may be applied to the field of identifying genetic variation, DNA copy number, and chromosomal rearrangement.
  • the analyzer 100 may read a single DNA several times.
  • the number of reads may be defined as a read count, and the read count may also be defined as a depth.
  • read refers to the length of a DNA fragment read by a genomic sequence analyzer at a time, about 10 to about 2000 bp, about 10 to about lOOOObp, about 10 to about 500bp, about 10 to about 300bp, about 10 To about 200 bp, about 25 to about 2000 bp, about 25 to about 1000 bp, about 25 to about 500 bp, about 25 to about 300 bp, about 25 to about 200 bp, about 25 to about 100 bp, about 50 to about 2000 bp, About 50 to about 1000 bp, about 50 to about 500 bp, about 50 to about 300 bp, about 50 to about 200 bp, about 50 to about 100 bp, about 100 to about 2000 bp, about 100 to about 1000 bp, about 100 to about 500 bp , About 100 to about 300 bp, about 100 to about 200 bp, about 150 to about 2000 bp, about 150 to about 1000 bp, about 150 to about 500 bp, about 150 to about
  • the bias removal device 300 in order to improve the sensitivity of the somatic cell copy number variation detection, the second bias can be removed, the non-specific region between the experimental group and the control vector is selected and set as an indiscriminate region, The bias can be eliminated based on the indifference region.
  • the bias removal apparatus 300 in the target sequencing analysis may be implemented by a computer that can be connected to a server or a terminal in a remote place through the network 200.
  • the computer may include, for example, a notebook, a desktop, a laptop, and the like.
  • FIG. 2 is a block diagram illustrating an apparatus (system) in which a bias removal method is performed
  • FIG. 3 is a block diagram illustrating a bias removal method in target sequencing according to an embodiment
  • Fig. 4 is work 5 is a view illustrating a process of generating an experimental group vector based on experimental sample sequence data in a bias removing method according to an embodiment
  • FIG. 5 illustrates a process of generating an experimental group vector and a control vector in a bias removing method according to an exemplary embodiment.
  • FIG. 6 is a diagram for describing a process of dividing an experimental group vector and a control vector for each region in a bias removal method according to an embodiment
  • FIG. 7 illustrates a bias removal method in a bias removal method according to an embodiment.
  • FIG. 8 is a graph showing the TRR vector with respect to the number of target areas before and after the following, and FIG. 8 is a graph showing the TRR with respect to the number of target areas after the bias is removed by various methods.
  • the bias removal apparatus 300 first removes the bias through the NMF and secondly selects the nonspecific region to remove the bias.
  • the bias removal apparatus 300 may include a receiver 310, a generator 330, a first remover 350, and an output 370.
  • the second remover 390 may be further included.
  • test sample sequencing data and control sample sequencing data into standard reference sequencing data per chromosomal position every 3 ⁇ 4 (1 & (1)
  • the bias removal method may be a bias removal method in target sequencing performed in a bias removal device in target sequencing.
  • Experimental sample base data and control sample base sequence of step (1) The data may each independently receive sequence data generated directly or indirectly from a genome sequencer, or may be obtained (prepared) through a computer readable storage medium on which already generated sequence data is stored.
  • the bias removal method in the target sequencing may further comprise preparing (receiving or obtaining) the experimental sample sequencing data and the control sample sequencing data before step (1).
  • the test sample sequencing data and the control sample sequencing data each independently, directly or indirectly receive sequence data generated by a genomic sequence analyzer, or a computer readable storage medium storing the sequence data already generated. We can prepare by application.
  • the first bias removing step may be performed using non-negative matrix factorization (NMF).
  • NMF non-negative matrix factorization
  • the bias removal method in the target sequencing analysis after the first bias removal step, for example, between the steps (3) and (4), the second bias removal step comprising the following step is added Funny to include as:
  • the secondary bias removal step may be performed in the first bias
  • the first bias removal step (corresponding to steps (2) and (3)) may include the following (i) to (V):
  • the secondary bias removing step may include the following (vi) to (viii). have:
  • the step (1) may include the generation unit 330, the step (2), or the steps (i) to (iii), the first removal unit 350, step (a) and (b) or steps (vi) to (viii) may be performed in the second removal section, and steps (3) and (4) or step (iv) may be performed at the output section 370, respectively, optionally step (1) Preparing the previously addable experimental sample sequence data and control sample sequence data may be performed at the receiver 310.
  • Receiving unit 310 is a part for preparing the experimental sample sequencing data and the control sample sequencing data, for example, receiving the experimental sample sequencing data and / or control sample sequencing data generated by the genomic sequence analyzer 100 or Or, read out experimental sample sequence data and / or control sample sequence data stored in a computer readable storage medium.
  • the test sample sequencing data and the control sample sequencing data as shown in Figs. 4 and 5, the test sample and the control sample are read a plurality of times in the genomic sequence analyzer 100 and have a plurality of read counts. Data.
  • the generation unit 330 is based on a read count in which the prepared test sample sequence data and the control 'sample sequence data are read mapped to standard reference sequence data for each chromosomal position. By doing so, the experimental group and the control vector can be generated (S3100, S3200). The read count may be calculated in at least one other third region located in the experimental sample sequence data and the control sample sequence data.
  • standard reference sequencing data refers to genomic sequencing databases representing a species or nucleotide sequence data of a particular chromosome or a specific chromosomal location (or region) constructed from the database.
  • Human standard reference sequence data may be constructed based on published (eg, UCSC, NCBI, etc.) reference genomic sequences such as build 37 (GRCh37), hgl 8, hgl 9, hg38.
  • the target region of the experimental sample sequence data and the control sample sequence data is read while reading the sequence data of the 250 test sample and the control sample, respectively. Stars lead
  • the number of counts can be calculated.
  • the read count may be calculated in at least one target region located in the experimental sample base data and the control sample base data.
  • the control sample sequence data i.e., if there is already generated (prepared) standard control vector, it can be calculated in at least one target region located in the experimental sample sequence data and the standard control vector.
  • the experimental group vector and the control group vector are as shown in Equation 1 below.
  • N (ni, n 2 , n ... n k .i, n ⁇
  • the first remover 350 performs a first bias removal step, and generates a binding matrix combining the generated experimental group vector and the control vector, and divides the generated binding matrix by region to remove bias.
  • the bias is, NMF (Non- negative Matrix Factorization) 3 ⁇ 4 ⁇ may be removed through.
  • Equation 2 Equation 2
  • Equation 2 may be divided into regions and expanded as shown in Equation 3 as shown in FIG. 6 (S3400):
  • 1 is the number of regions
  • k is the number of target regions
  • b is a boundary.
  • step (S3500) of performing the NMF for each of the plurality of regions may be performed.
  • Non-negative matrix factorization refers to a method of factoring a matrix into two matrices of non-negative (positive + zero), that is, W (specific element matrix) and H (weighted matrix). Used to extract independent features in the data.
  • Equation 3 can be summarized as Equation 4 below. That is, when NMF is applied to the matrix Vb of Equation 3 divided by regions (S3500).
  • Equation 5 it can be seen that T b is composed of W, lX H u + W, 2 x H 2 , ⁇ l, and Nb is composed of WjxH + W xH.
  • the vector is a common element vector of the experimental group and the control vector, and the W, 2 vectors are the experimental group or the control specific element vector.
  • ! and 3 ⁇ 4 , 2 are the augmented values multiplied only in the experimental vector and the control vector, respectively.
  • most of U and H U have a value larger than 0, but in some cases, ⁇ and U U can be close to zero.
  • the bias elements can be selected by comparing the values of the decomposed! ⁇ And 3 ⁇ 4, 2 values. Therefore, Equation 5 may be used to illustrate the bias element selection step (S3600) when the rank is fixed to 2 in the NMF.
  • TRR target region ratio
  • Steps (3) and (4) may be performed in the output unit 370.
  • H 2 ⁇ H 2 greater than 2 for example, in the case 2 is close to 0, W, a divalent group Since it means that it is a special element vector, it is possible to recombine the matrix except H 2 and 2 (S3610).
  • Tb W ,, x H u + W, 2 x ⁇ 2 , ⁇
  • the bias removal method according to an embodiment of the present invention, by dividing the coupling vector matrix for the experimental group and the control vector by region, by removing the noise bias by region, and then re-aggregate this collectively in all areas
  • it is possible to eliminate the problem of desensitization caused by removing the bias and to increase the accuracy in identifying the somatic cell copy number variation by removing the bias that may occur specifically in the region.
  • the bias is first removed by using the NMF, and the second step of removing the bias by selecting the non-specific region (secondary bias removal step) It can be done further.
  • second bias removal step a bias removal method for selecting non-specific regions will be described.
  • the secondary bias removal step may be performed in the second removal unit 390.
  • step (a) of removing non-specific areas between the experimental group and the control vector as a non-discriminatory region after screening (1) S3700, S3710
  • the experimental group and the control vector remaining non-specific region are described below. Selection may be made through Equations 8 and 9.
  • step 1 of calculating the weight for each region of the experimental group and the control vector the bias is removed to the set indiscriminate region, to the set indiscriminate region
  • the target region ratio (TRR) vector may be generated based on the number of the target sample sequencing data or the experimental group vector, and at least one target located in the control sequencing data or the control vector.
  • the calculated TRR vector for each region may be collected for each region and generated as shown in Equation 13 (S3800) (In Equations 12 and 13, b denotes a boundary and 1 denotes the number of regions.
  • TRR b * means the region-specific TRR vector after the bias is removed first and second.
  • FIG. 7 is a graph depicting TRR vectors for the number of target regions before and after bias removal in human genome chromosomes (test sample: HCC1143 Cell line (ATCC), control sample: HCC1143 BL (ATCC)). Shows the TRR vector for the target region number before removing the bias, and (b) shows the TRR vector for the target region number after performing the primary bias removal and the secondary bias removal.
  • (a) and (b) it can be seen that (b) of the present invention is better classified TRR vectors for each region. That is, it is difficult to identify somatic cell copy number variation because ( a ) has a lot of bias or noise, but it is understood that somatic cell copy number variation is easier to identify because (b) is a state where bias and noise are removed.
  • FIG. 8 is a graph depicting TRR versus number of target regions after debiasing in various methods for human genome chromosomes (test sample: HCC1143 Cell line (ATCC), control sample: HCC1143 BL (ATCC)).
  • step (c) removes the bias at the same time for the entire region by the NMF method (i.e., except for dividing the coupling matrix of step (2) by region in the above-described method (step (ii) (S3400)). And the TRR vector for the number of target regions of the secondary bias removal step (S3700),
  • (d) shows the TRR vectors for the number of target regions after removing the primary bias (S3600) and the secondary bias (S3700) for each region by the NMF method as described above.
  • Equation 1-8 The SVD of FIG. 8B was performed by the following Equations 1-8:
  • ni (fi, ⁇ 2, ⁇ ⁇ ; nik)
  • ri is the lead correction sheet at position i of the standard control vector (rule 91) ISA / KR Number of counts, and k in Equation 4-8 is the number of targets)
  • T can be factored as 1; ⁇ ⁇ and 1 is defined as Singular Value Cutoff, so the cutoff is determined by Ref. 7 below)
  • FIG. 8 shows a large number of TRR values beyond the baseline in addition to the region indicated by T, so that it is difficult to identify variation in somatic cell copy number due to bias.
  • (b) increases the TRR value in the area indicated by T, which increases the sensitivity of excavation variation.
  • the bias removal effect is not significant because more areas are beyond the baseline.
  • FIG. 9 is a flowchart illustrating an example of a method of removing a bias in target sequencing according to an exemplary embodiment.
  • the bias removal apparatus receives experimental sample sequence data and control sample sequence data generated by a genome sequence analyzer (S8100). Then, the bias removal apparatus reads the received test sample sequencing data and the control sample sequencing data to a read count of read mapping to standard reference sequencing data for each chromosomal position. Based on the experimental group and the control vector is generated (S8200).
  • the bias removal apparatus generates a binding matrix combining the generated experimental group vector and the control vector, divides the generated binding matrix by region to remove bias (S8300), and recombines the coupling matrix from which the bias is removed.
  • a TRR vector for each region from which the bias is removed is collected and collected for each region.
  • the bias removal method or computer readable method including the same is a computer executable instruction, which may be implemented and / or processed in whole or in part on a known computer readable medium.
  • the methods described herein may be implemented in combination with hardware.
  • the hardware may mean specially designed hardware or firmware, such as a computer, a standard multipurpose ( ⁇ - ⁇ ) CPU, an application-specific integrated circuit or other hard-wired device,
  • ⁇ - ⁇ standard multipurpose
  • ⁇ - ⁇ application-specific integrated circuit
  • the term 'computer' used may be used to generically refer to them.
  • Another example is a computer stored in a computer readable storage medium for carrying out the steps of the bias removal method or a computer readable method comprising the same.
  • the computer program stored in the computer readable storage medium may be combined with hardware.
  • the computer program stored in the computer readable storage medium is a program for executing each step of the above-described bias removing method or a computer reading method including the same in a computer, wherein all the above steps are executed by one program. Or by two or more programs executing one or more steps.
  • a processor may have one or more of the following features: It may be implemented in software stored in (eg, memory, etc.) and implemented on one or more processors. As is generally known, a processor may have one or more of the following features: a processor may have one or more of the following features: a processor may have one or more of the following features: a processor may have one or more of the following features: a processor may have one or more of the following features: a processors, etc.
  • the program RAM Random Access Memory
  • ROM Read Only Memory
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • Flash Memory eg, Universal Serial Bus (USB) Memory, Secure Digital (SD) Memory) , Soli State Drive (SSD), Compact Flash (CF) memory, xD memory, etc.
  • USB Universal Serial Bus
  • SD Secure Digital
  • CF Compact Flash
  • magnetic disks laser disks, or other storage media.
  • Programs or software stored on the computer readable storage medium may be any, including, for example, on a communication channel such as a telephone line, the Internet, a wireless connection, or the like, or on a portable medium such as a computer readable disk, a flash drive, or the like. It can be delivered to a computer device through known delivery methods.
  • some or all of the blocks, tasks, techniques, etc. may be, for example, custom ICs, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), programmable logic arrays (PLAs). ) May be implemented.
  • ASICs application specific integrated circuits
  • FPGAs field programmable logic arrays
  • PDAs programmable logic arrays
  • the software may be a known computer readable medium, such as a magnetic disk, optical disk, or other storage medium, RAM or ROM or flash memory of a computer, processor, hard disk drive, optical disk drive, tape drive, or the like. Can be stored.
  • the software may be delivered to a user or computer system via known delivery methods, including, for example, computer readable disks or other portable computer storage mechanisms.
  • bias removal method computer readable method, program, and storage medium may be embodied in many other general purpose or special purpose computing system environments or
  • Computing systems, environments, and / or structures suitable for implementing the bias removal method, computer readable method, program, and storage medium may be, for example, a personal computer (PC), server computer, portable or laptop device, multiprocessor Remote processing including systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and / or the systems or devices described above and connected via a communications network Distributed computing environment performed by the devices, and the like, but is not limited thereto.
  • program modules may be located in both local and remote computer storage media, including memory storage devices.
  • Computers may typically include a variety of computer readable media.
  • Computer-readable media can be media that are accessible and available by a computer and can include volatile and nonvolatile media, removable media, and non-removable media.
  • Computer readable media may include computer storage media and / or communication media.
  • the computer readable storage medium can be any available medium that can be accessed by the computer and includes all conventional media such as volatile and nonvolatile media, removable media non-removable media, removable media and / or non-removable media. It may mean a medium.
  • a computer-readable storage media may include both computer storage media and communication media.
  • Computer storage media include RAM, ROM, EEPROM, flash memory (eg, USB memory, SD memory, SSD, CF memory, xD memory, etc.), magnetic disks, laser disks, or other memory, CD-ROM, DVD (digital versatile disk). ) Or other optical disc, magnetic
  • One or more of a cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any medium that can be used to store desired information and accessible by a computer can be selected, but is not limited thereto.
  • the communication medium is typically an information transfer medium that implements data transmission or other transport mechanisms among modulated data signals, such as computer readable instructions, data structures, program modules, or carrier waves. (information delivery media).
  • modulated data signals such as computer readable instructions, data structures, program modules, or carrier waves.
  • Modulated data signal means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.
  • the communication medium may be wired. Wired media such as network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of one or more of the above may also be included within the scope of computer readable media.

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Abstract

L'invention concerne un procédé d'élimination du biais dans l'analyse d'une séquence cible de nucléotides, le procédé d'élimination du biais comportant les étapes consistant à: générer un vecteur de groupe expérimental et un vecteur de groupe témoin sur la base d'un comptage de lectures dans lequel des données de séquences de nucléotides d'échantillons expérimentaux et des données de séquences de nucléotides d'échantillons témoins lues sont transcrites sur des données de séquences de nucléotides de référence standard par position chromosomique; générer une matrice de couplage couplant le vecteur de groupe expérimental et le vecteur de groupe témoin générés, et éliminer le biais en divisant la matrice de couplage générée par région; et coupler à nouveau la matrice de couplage issue de l'élimination du biais, et collecter et délivrer, par région, des vecteurs de rapport de région visée (TRR) issus de l'élimination du biais par région.
PCT/KR2015/011513 2014-10-29 2015-10-29 Procédé d'élimination du biais dans l'analyse de séquences cibles de nucléotides par nmf Ceased WO2016068625A1 (fr)

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