Method and apparatus for determining copy number variation profiles by read-depth correction in whole-genome analysis

Abstract

This disclosure provides a method for determining copy number variation profiles, which is performed by at least one processor. [Solution] The method for determining a copy number variation profile includes the steps of: obtaining whole-genome analysis results associated with a target sample taken from a subject; calculating the read depth associated with the target sample for each of a predetermined number of bins on the genome based on the obtained whole-genome analysis results; correcting the read depth associated with the target sample; and determining the copy number variation profile associated with the target sample using the corrected read depth.

Description

Technical Field

[0001] The present disclosure relates to a method and apparatus for determining a copy number variation profile by read depth correction. Specifically, it relates to a method and apparatus for calculating and correcting the read depth related to a target sample based on the whole genome analysis results related to the target sample collected from a subject, and determining a copy number variation profile.

Background Art

[0002] Genetic information analysis technology is widely used in the medical field, such as to determine what characteristics or qualities a living organism has by grasping the genetic information possessed by the living organism. Recently, medical practices for understanding the causes of various diseases such as tumors and treating diseases have evolved from the traditional prescription-centered approach to precision medicine, that is, an order-made treatment form that takes into account the genetic information and health records of individual patients. In the field of precision medicine, the main thing is to obtain a huge amount of personal genetic information and perform related clinical analyses, and such key elements correspond to the core elements that accelerate the development speed of precision medicine technology.

[0003] In particular, when performing whole genome analysis on tissues collected from patients, etc., the so-called "fresh frozen (FF)" treatment method, which immediately freezes the tissue after collection from the human body, is widely used. The FF-treated tissue is known to be the optimal tissue treatment method for whole genome analysis because the DNA damage of the cells in the tissue is less due to being frozen immediately after collection. However, there is a problem that facilities and equipment that are usually not available or difficult to be equipped at the medical site, such as nitrogen tanks, are required to perform FF treatment on the collected tissue or store the FF-treated tissue.

[0004] On the other hand, when removing tumor tissue or performing tissue biopsies for genetic information analysis of patients, it is common practice for medical institutions to treat the tissue (such as tumor tissue) collected from patients with FFPE (Formalin-Fixed, Paraffin-Embedded) technology for long-term storage and utilize the FFPE-treated tissue for subsequent testing or academic research purposes. Processing tissue collected through the FFPE method not only avoids significant costs and labor for processing and storage of the collected tissue, but also has the advantage of allowing the tissue to be stored for a long period with most of its genetic information preserved, making it easier to utilize the collected tissue later (for example, for re-examination or re-analysis of the tissue).

[0005] However, during the process of long-term storage of tissues taken from the human body using FFPE (Fiber-Filtrated Preservative), various types of damage can occur to the DNA within the tissue, including cross-linking (where different parts of the DNA become chemically entangled), fragmentation (where the DNA is cut into smaller pieces), and mutations in DNA bases due to other non-biological causes.

[0006] Due to the DNA damage described above, when performing whole-genome analysis on FFPE-treated tissues, noise may be introduced into the copy number variation (CNV) profile in the analysis data (raw data), potentially leading to inaccurate and distorted analytical results. Such noise is generally not found in whole-genome analysis data from FF-treated tissues. Therefore, to derive undistorted analytical results from FFPE-treated tissues, it is necessary to effectively process or remove noise from the copy number variation profile. [Prior art documents] [Patent Documents]

[0007] [Patent Document 1] Korean Published Patent Publication No. 10-2018-0098438 [Overview of the project] [Problems that the invention aims to solve]

[0008] This disclosure provides a method for determining copy number mutation profiles, a computer program stored on a recording medium, and an apparatus (system) to solve the problems described above. [Means for solving the problem]

[0009] This disclosure can be embodied in various ways, including methods, systems (apparatus), or computer-readable recording media on which instructions are recorded.

[0010] A method for determining a copy number variation profile by read depth correction, performed by at least one processor according to one embodiment of the present disclosure, includes the steps of: obtaining whole-genome analysis results associated with a target sample taken from a subject; calculating the read depth associated with the target sample for each of a predetermined number of bins on the genome based on the obtained whole-genome analysis results; correcting the read depth associated with the target sample; and determining a copy number variation profile associated with the target sample using the corrected read depth.

[0011] A computer-readable non-temporary recording medium is provided, which records instructions for executing a copy number variation profile determination method according to one embodiment of the present disclosure on a computer.

[0012] A computing device according to one embodiment of the present disclosure includes a communication module, memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory, the at least one program including instructions for obtaining whole-genome analysis results associated with a target sample taken from a subject, calculating a read depth associated with the target sample for each of a predetermined number of bins on the genome based on the obtained whole-genome analysis results, correcting the read depth associated with the target sample, and using the corrected read depth to determine a copy number variation profile associated with the target sample. [Effects of the Invention]

[0013] According to various embodiments of this disclosure, grouping genomes in multiple bins can significantly reduce the instability of the read depth of a target sample due to noise (random noise or non-random noise) during read depth calculation.

[0014] According to various embodiments of this disclosure, hierarchical thresholding of detailed wavelet coefficients ensures that higher levels of detailed wavelet coefficients, which contain macroscopic morphological information of the signal, are less likely to be replaced with zero or attenuated, thereby maximizing the preservation of the macroscopic copy number variation profile.

[0015] According to various embodiments of this disclosure, by correcting for noise or errors that may occur when performing whole-genome analysis on FFPE-treated tissue, it is possible to derive analysis results that are not distorted, such as whole-genome analysis data from FF-treated tissue.

[0016] According to various embodiments of this disclosure, whole-genome analysis can be performed with high accuracy on the vast amount of FFPE tissue secured and stored by medical institutions and biobanks, and whole-genome analysis can be carried out on tissue samples from patients and others using only facilities available in normal clinical settings, without any changes to the tissue sample processing procedures of medical institutions.

[0017] The effects of this disclosure are not limited to those mentioned above, and any other effects not mentioned above would be clearly understood by a person with ordinary skill in the art to which this disclosure pertains ("ordinary skill") from the wording of the claims. [Brief explanation of the drawing]

[0018] Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, where similar reference numbers indicate similar elements, but are not limited thereto. [Figure 1] This figure shows the detailed steps of a method for determining a copy number variation profile according to one embodiment of the present disclosure. [Figure 2] This is a schematic diagram showing a configuration in which an information processing system is connected to multiple user terminals in a communicative manner in order to provide a copy number variation profile determination service according to one embodiment of the present disclosure. [Figure 3] This is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure. [Figure 4] This figure shows an example of a corrected dataset according to one embodiment of the present disclosure. [Figure 5] This figure shows the first-order correction process for the target sample and associated read depth for each of the multiple bins according to one embodiment of the present disclosure. [Figure 6] This figure shows the process by which the value of the correction variable that minimizes the result value of the standard deviation function according to one embodiment of the present disclosure is determined. [Figure 7] This figure shows the process by which a secondary correction is performed to remove noise from a primary corrected read depth according to one embodiment of the present disclosure. [Figure 8] A diagram showing an example of a graph and a coefficient set used during secondary correction according to an embodiment of the present disclosure. [Figure 9] A diagram showing an example of a first heatmap related to an FF processing sample and a second heatmap related to an FFPE processing sample according to an embodiment of the present disclosure. [Figure 10] A diagram showing an example of a distribution graph of read depth ratios for each sample before and after correction of read depth related to an FFPE processed sample according to an embodiment of the present disclosure. [Figure 11] A diagram showing an example of the distribution of read depth by chromosome according to an embodiment of the present disclosure. [Figure 12] A diagram showing a SNR graph for evaluating the qualitative improvement degree of a copy number variation profile by read depth correction according to an embodiment of the present disclosure. [Figure 13] A flowchart showing a method for determining a copy number variation profile according to an embodiment of the present disclosure.

Embodiments for Carrying Out the Invention

[0019] <Summary of the Invention> In one embodiment of the present disclosure, the target sample includes a normal cell sample and an abnormal cell sample collected from a subject, and the step of determining a copy number variation profile includes determining a copy number variation profile based on the read depth related to the normal cell sample, the read depth related to the abnormal cell sample, and the allele frequency related to the target sample.

[0020] In one embodiment of the present disclosure, the target sample is a sample processed by FFPE (Formalin-Fixed, Paraffin-Embedded).

[0021] In one embodiment of the present disclosure, the step of correcting the read depth associated with a target sample includes performing a primary correction of the read depth associated with the target sample using a dataset that includes read depth data for each of several bins, each associated with a set of samples taken from multiple subjects different from the subject.

[0022] In one embodiment of the present disclosure, the dataset includes at least one of the following: data associated with normal cell samples taken from each of a group of subjects, or data associated with abnormal cell samples taken from each of a group of subjects.

[0023] In one embodiment of the present disclosure, each of the normal cell samples and abnormal cell samples collected from each of the multiple subjects includes both FFPE-treated and FF (Fresh Frozen) treated samples.

[0024] In one embodiment of the present disclosure, the dataset further includes information related to the read depth correction direction for each of several bins, for use in correcting the read depth of a target sample.

[0025] In one embodiment of the present disclosure, the correction direction and related information are determined for each of the multiple bins based on the distribution of read depths associated with the entire FFPE-processed sample in the dataset, and for each of the multiple bins based on the distribution of read depths associated with the entire FF-processed sample in the dataset.

[0026] In one embodiment of the present disclosure, the dataset further includes the corrected read depth size and related information for each of several bins, for use in correcting the read depth of a target sample.

[0027] In one embodiment of the present disclosure, the correction size and related information are determined for each of the multiple bins based on the average read depth associated with the entire FFPE-processed samples in the dataset, and for each of the multiple bins based on the average read depth associated with the entire FF-processed samples in the dataset.

[0028] In one embodiment of the present disclosure, the step of performing a primary correction includes correcting the read depth associated with the target sample based on information related to the correction direction and information related to the correction size.

[0029] In one embodiment of the present disclosure, the step of correcting the read depth associated with the target sample further includes the step of performing a secondary correction to remove noise from the primary corrected read depth.

[0030] In one embodiment of the present disclosure, the step of performing a secondary correction to remove noise from a primary corrected read depth includes the steps of determining a plurality of wavelet coefficients using a predetermined wavelet and a primary corrected read depth, thresholding the plurality of wavelet coefficients, and determining a secondary corrected read depth based on the thresholded plurality of wavelet coefficients.

[0031] In one embodiment of this disclosure, a predetermined wavelet corresponds to a Haar wavelet.

[0032] In one embodiment of the present disclosure, the step of thresholding a plurality of wavelet coefficients includes the step of replacing a particular wavelet coefficient with 0 if the absolute value of that particular wavelet coefficient is less than a predetermined threshold, and the step of attenuating the particular wavelet coefficient to a higher degree if the absolute value of that particular wavelet coefficient is greater than or equal to the threshold, the smaller the difference between the absolute value and the threshold.

[0033] In one embodiment of this disclosure, the higher the level of a particular wavelet coefficient, the lower the threshold becomes.

[0034] <Details of the invention> The specific details for implementing this disclosure will be described below with reference to the attached drawings. However, in the following description, specific explanations of widely known functions and configurations will be omitted if there is a risk of unnecessarily obscuring the essence of this disclosure.

[0035] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Furthermore, in the following descriptions of embodiments, the description of identical or corresponding components may be omitted. However, the omission of a description of a component does not imply that such a component is not included in any embodiment.

[0036] The advantages and features of the disclosed embodiments, as well as the methods for achieving them, will become clear with reference to the embodiments described below, along with the accompanying drawings. However, this disclosure is not limited to the embodiments disclosed below and may be embodied in a variety of different forms, and these embodiments are provided merely to make the disclosure complete and to fully inform a person of the ordinary skill of the scope of the invention.

[0037] This specification will briefly explain the terminology used herein and then describe the disclosed embodiments in detail. The terminology used herein has been selected as widely used and general terms as possible, taking into account the function of this disclosure, although this may change depending on the intent of the articulates in the relevant field, case law, the emergence of new technologies, etc. In some cases, the applicant has arbitrarily selected terms, in which case their meaning will be described in detail in the description of the relevant invention. Therefore, the terminology used in this disclosure must be defined not simply as a term name, but based on the meaning of the term and the overall content of this disclosure.

[0038] In this specification, singular expressions include plural expressions unless the context clearly identifies them as singular. Conversely, plural expressions include singular expressions unless the context clearly identifies them as plural. Where the entire specification states that a part includes a component, this means, unless otherwise stated, that it may include other components rather than excluding them.

[0039] Furthermore, the terms “module” or “part” as used in this specification mean a software or hardware component, and a “module” or “part” performs a certain role. However, the meaning of “module” or “part” is not limited to software or hardware. A “module” or “part” may be configured to reside on an addressable storage medium, or to regenerate one or more processors. Thus, as an example, a “module” or “part” may include components such as software components, object-oriented software components, class components, and task components, and at least one of the following: processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within a “module” or “part” may be combined into a smaller number of components and a “module” or “part,” or further separated into additional components and “modules” or “parts.”

[0040] According to one embodiment of the present disclosure, “module” or “part” may be embodied in a processor and memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, and the like. In some environments, “processor” may also refer to application-specific semiconductors (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), and the like. “Processor” may also refer to combinations of processing devices such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled with a DSP core, or any other combination of such configurations. “Memory” should also be broadly interpreted to include any electronic component capable of storing electronic information. "Memory" can also refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage devices, and registers. Memory is said to be in electronic communication with the processor when the processor can read information from and / or write information to it. Memory integrated into a processor is in electronic communication with the processor.

[0041] In this disclosure, “Subject” may refer to the patient or subject(s) who will undergo copy number variation profiling in accordance with this disclosure.

[0042] In this disclosure, “target sample” may refer to a sample of a subject whose read depth is calculated and corrected for copy number variation profiling.

[0043] In this disclosure, "bin" may refer to a segment defined on a genome, etc., as a unit used to group or classify at least a portion of the genome for gene / genome analysis. The position and number of base sequences contained in a single bin, and the number of bins, can be determined arbitrarily. For example, for a subject's genome containing approximately 3 billion base pairs, more than 3,000 bins, each capable of grouping 1 million bases, may be determined.

[0044] In this disclosure, “abnormal cells” may refer to non-normal cells that have different size, shape, structure, function, etc., from normal cells. Abnormal cells can arise from a variety of factors, such as genetic mutation, infection, and toxin exposure, and may include various types of non-normal cells, such as cancer cells, tumor cells, necrotic cells, senescent cells, aneuploid cells, hyperplastic cells, and hypertrophic cells.

[0045] In this disclosure, "read depth ratio" may refer to the ratio between the read depth obtained as a result of sequencing a normal cell sample and the read depth obtained as a result of sequencing an abnormal cell sample. Furthermore, since read depth can be calculated from the read depth ratio, "read depth" may refer to the read depth ratio unless it is clearly specified as read depth in the context.

[0046] In this disclosure, "wavelet" or "wavelet function" may refer to a one-dimensional function that satisfies certain properties, oscillates at zero in both negative and positive values ​​in a single local region, and whose integral over all real numbers is zero. For example, a "wavelet" or "wavelet function" has a domain of (-∞, ∞) and can have a value of zero in the region extending in both the negative and positive infinity directions outside the local region where the oscillation appears. Wavelets or wavelet functions are classified according to the shape of their oscillation, and various types may exist, such as Haar, Daubechies, Coiflet, Best-localized Daubechies, and Leastasymmetric.

[0047] Figure 1 shows the detailed steps of a copy number variation profiling method according to one embodiment of the present disclosure. In one embodiment, the copy number variation profiling method may include a learning step (110) and an application step (160).

[0048] In the learning step (110), a correction dataset (140) for correcting the target sample data (150) may be determined / learned based on an FFPE sample dataset (120) containing multiple FFPE sample data (120_1~120_n) and an FF sample dataset (130) containing multiple FF sample data (130_1~130_n, where n is a natural number). The FFPE sample dataset (120) and the FF sample dataset (130) may include data calculated from multiple samples taken from multiple subjects different from the subject from which the target sample is taken. Here, "sample data" can be any data associated with the sample, such as a copy number variation (CNV) profile or read depth associated with the sample, and FFPE sample data may refer to data associated with FFPE (Formalin-Fixed, Paraffin-Embedded) processed samples, and FF sample data may refer to data associated with FF (Fresh-Frozen) processed samples.

[0049] At least one of the multiple FFPE sample data (120_1~120_n) and at least one of the multiple FF sample data (130_1~130_n) may be associated with a sample taken from the same subject. For example, the first FFPE sample data (120_1) and the first FF sample data (130_1) may be associated with a sample taken from a specific subject and processed in different ways (FFPE, FF), and the multiple FFPE sample data (120_1~120_n) and the multiple FF sample data (130_1~130_n) may be associated with a sample taken from n subjects and processed in different ways.

[0050] Each of the FFPE sample dataset (120) and FF sample dataset (130) may include data associated with normal cell samples taken from each of multiple subjects and / or data associated with abnormal cell samples taken from each of multiple subjects. For example, the first FFPE sample data (120_1) and the first FF sample data (130_1) may be read depth data for each of multiple bins on the whole genome sequence from which normal cell samples taken from a particular subject were analyzed, and the second FFPE sample data (120_2) and the second FF sample data (130_2) may be read depth data for each of multiple bins on the whole genome sequence from which abnormal cell samples taken from the same subject were analyzed.

[0051] The corrected dataset (140) may include all the data contained in the FFPE sample dataset (120) and the FF sample dataset (130). For example, the corrected dataset (140) may include read depth data associated with normal and abnormal cell samples taken from multiple subjects different from the subject. Alternatively, if the FFPE sample dataset (120) or the FF sample dataset (130) contains copy number variation profiles rather than read depth data for each sample, a separate process may be performed to determine the read depth data from the copy number variation profiles.

[0052] The target sample data (150) may be data associated with the subject's target sample (e.g., copy number variation profile, read depth data, etc.) that is corrected by this disclosure. For example, the target sample data (150) may be read depth associated with the target sample, calculated for each of several bins based on whole-genome analysis results associated with the target sample taken from the subject. In this case, the whole-genome analysis results may include sequence data obtained by mapping and aligning read data obtained using a standard paired-end sequencing technique to a reference genome (hg19, hg38, GRCh37, GRCh38, etc.) using various sequencing data processing tools. The target sample may include normal cell samples and / or abnormal cell samples taken from the subject.

[0053] In one embodiment, the target sample may be an FFPE (Formalin-Fixed, Paraffin-Embedded) treated sample. By treating the target sample with FFPE, noise that occurs in the copy number variation profile (or read depth) during whole-genome analysis is removed by this disclosure, and substantially the same or similar analytical accuracy as that obtained when the target sample is analyzed after FF treatment can be obtained.

[0054] The correction dataset (140) is used to correct the read depth of the target sample and may further include information related to the correction direction and correction size for each of the multiple bins. That is, the correction direction and / or correction size for read depth may differ for each of the multiple bins.

[0055] The correction dataset (140) can be implemented and used in various forms, such as matrices, arrays, and vectors. For example, in a correction dataset (140) implemented in a two-dimensional array, the rows represent each of several bins, and the columns represent the samples for each of several subjects by processing type (e.g., FFPE, FF) and / or the type of sample used (e.g., normal cell sample, abnormal cell sample), or the correction direction and / or correction size of the read depth for each of the several bins. A specific example of the correction dataset (140) will be described in detail in Figure 4.

[0056] In the application step (160), the target sample data (150) may be corrected using the correction dataset (140) and the linear correction function (172). For example, the read depth associated with the target sample may be corrected by the linear correction function (172) based on the correction direction and related information and correction size information contained in the correction dataset (140). This will be described in detail in Figures 5 and 6.

[0057] Subsequently, the noise in the first-order corrected data is removed by the second-order correction function (174), which can generate the corrected target sample data (FFPEX')(180). This will be explained in detail in Figures 7 and 8.

[0058] Subsequently, the corrected target sample data (180) (e.g., corrected read depth) can be used to determine the copy number variation profile (CNVX') (190) associated with the target sample. For example, the copy number variation profile (190) may be determined based on the read depth associated with the normal cell sample of the target sample, the read depth associated with the abnormal cell sample of the target sample, and the allele frequency associated with the target sample. In this case, the allele frequency is influenced by the purity (the proportion of abnormal cells among the cells in the tissue being sequenced) and mean ploidy (the average number of DNA molecules in all regions; for example, normal human cells have a mean ploidy of 2 because DNA or genomes exist in pairs), and is hardly influenced by read depth. Therefore, the noise that occurs when measuring allele frequencies in FFPE-treated samples may not be significant enough to distort the analysis results compared to read depth.

[0059] The corrected read depth or copy number variation profiles determined using the corrected read depth are added to the corrected dataset (140) and can be reused when correcting read depth and determining copy number variation profiles for other subjects.

[0060] Figure 2 is a schematic diagram showing a configuration in which an information processing system (230) is communicatively connected to a plurality of user terminals (210_1, 210_2, 210_3) to provide a copy number variant profiling service according to one embodiment of the present disclosure. The information processing system (230) may include one or more systems or computing devices capable of providing the copy number variant profiling service. In one embodiment, the information processing system (230) may include one or more server devices and / or databases, or one or more cloud computing service-based distributed computing devices and / or distributed databases, capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data related to the copy number variant profiling service.

[0061] The copy number variation profiling service provided by the information processing system (230) may be provided to users through applications installed on each of the multiple user terminals (210_1, 210_2, 210_3).

[0062] Multiple user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) via a network (220). The network (220) can be configured to enable communication between the multiple user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) may consist of wired networks such as Ethernet, Power Line Communication, telephone line communication equipment and RS-serial communication, mobile communication networks, wireless networks such as WLAN (WirelessLAN), Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited and may include not only communication methods that utilize communication networks that the network (220) may include (for example, mobile communication networks, wired internet, wireless internet, broadcasting networks, satellite networks, etc.), but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0063] For example, multiple user terminals (210_1, 210_2, 210_3) may send requests to an information processing system (230) via a network (220), and after receiving these requests, the information processing system (230) may send responses corresponding to the requests to the multiple user terminals (210_1, 210_2, 210_3). For example, if user terminal (210_1) sends a request to the information processing system (230) for whole-genome analysis results and copy number variation profile determination related to a target sample (request), the information processing system (230) may send the copy number variation profile related to the target sample, etc., to user terminal (210_1) (response).

[0064] In Figure 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are shown as examples of user terminals, but are not limited to these. User terminals (210_1, 210_2, 210_3) can be any computing device capable of wired and / or wireless communication, and on which copy number variation profiling applications and the like can be installed and run. For example, user terminals may include medical devices, smartphones, mobile phones, navigation systems, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (virtual reality) devices, AR (augmented reality) devices, and the like. Furthermore, although Figure 2 shows three user terminals (210_1, 210_2, 210_3) communicating with the information processing system (230) via the network (220), the system is not limited to this configuration, and it is also possible to configure the system to communicate with a different number of user terminals via the network (220) with the information processing system (230).

[0065] Figure 2 shows that a user terminal provides a copy number variation profile via network communication with an information processing system, but is not limited to this. The user may also request whole-genome analysis results and copy number variation profile determination related to a target sample via an input device connected to the information processing system, and in response, the whole-genome analysis results and copy number variation profile related to the target sample may be provided via an output device connected to the information processing system.

[0066] Figure 3 is a block diagram showing the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure. The user terminal (210) can refer to any computing device capable of running copy number variation profiling applications and the like, and capable of wired / wireless communication, and may include, for example, the mobile phone terminal (210_1), tablet terminal (210_2), and PC terminal (210_3) shown in Figure 2. As shown, the user terminal (210) may include memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include memory (332), a processor (334), a communication module (336), and an input / output interface (338). As shown in Figure 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data over a network (220) using their respective communication modules (316, 336). Furthermore, the input / output device (320) may be configured to input information and / or data to a user terminal (210) via an input / output interface (318), or to output information and / or data generated from the user terminal (210).

[0067] The memory (312, 332) may include any computer-readable recording medium. According to one embodiment, the memory (312, 332) may be any non-temporary computer-readable recording medium, and may include, for example, a permanent mass storage device such as a ROM (read-only memory), disk drive, SSD (solid-state drive), or flash memory. As another example, a permanent mass storage device such as a ROM, SSD, flash memory, or disk drive may be included in a user terminal (210) or information processing system (230) as a separate permanent storage device distinct from the memory. The memory (312, 332) may also store an operating system and at least one program code (for example, code for a copy number variation profiling application).

[0068] These software components may be loaded from a computer-readable recording medium separate from memory (312, 332). Such separate computer-readable recording mediums may include recording media that can be directly connected to these user terminals (210) and information processing systems (230), and may include computer-readable recording media such as floppy drives, disks, tapes, DVD / CD-ROM drives, and memory cards. As another example, software components may also be loaded into memory (312, 332) via a communication module (316, 336) that is not a computer-readable recording medium. For example, at least one program may be loaded into memory (312, 332) based on a computer program (e.g., a copy number variation profiling application) that is installed by a file provided over a network (220) by a developer or a file distribution system that distributes application installation files.

[0069] The processor (314, 334) may be configured to process instructions for a computer program by performing basic arithmetic, logical, and input / output operations. Instructions may be provided to the processor (314, 334) by memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a recording device such as memory (312, 332).

[0070] The communication modules (316, 336) may provide configurations or functions for a user terminal (210) and an information processing system (230) to communicate with each other via a network (220), and may provide configurations or functions for a user terminal (210) and / or an information processing system (230) to communicate with other user terminals or other systems (for example, a separate cloud system). For example, requests or data generated by the processor (314) of the user terminal (210) according to program code stored in a recording device such as memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, control signals or commands provided under the control of the processor (334) of the information processing system (230) may be received by the user terminal (210) via the communication module (336) and the network (220) through the communication module (316) of the user terminal (210).

[0071] The input / output interface (318) may be a means for interface with an input / output device (320). For example, an input device may include a camera with audio sensors and / or image sensors, a keyboard, a microphone, a mouse, etc., and an output device may include a display, a speaker, a haptic feedback device, etc. As another example, the input / output interface (318) may be a means for interface with a device in which the configuration or function for performing input and output is integrated into one, such as a touchscreen. In Figure 3, the input / output device (320) is shown not to be included in the user terminal (210), but is not limited to this, and may consist of the user terminal (210) and a single device. Furthermore, the input / output interface (338) of the information processing system (230) may be connected to the information processing system (230) or a means for interface with input or output devices (not shown) that the information processing system (230) may include. In Figure 3, the input / output interfaces (318, 338) are shown as elements configured separately from the processors (314, 334). However, the diagram is not limited to this, and the input / output interfaces (318, 338) can be configured to be included in the processors (314, 334).

[0072] The user terminal (210) and the information processing system (230) may include more components than those shown in Figure 3. However, it is not necessary to clearly illustrate most of the conventional components. In one embodiment, the user terminal (210) may be embodied to include at least some of the input / output devices (320) described above. The user terminal (210) may also further include other components such as a transceiver, a GPS (Global Positioning system) module, a camera, various sensors, and a database.

[0073] In one embodiment, the processor (314) of a user terminal (210) may be configured to run an application that provides a copy number variation profiling service or a web browser application. In this case, the application and associated program code may be loaded into the memory (312) of the user terminal (210). While the application is running, the processor (314) of the user terminal (210) can receive information and / or data provided by an input / output device (320) via an input / output interface (318) or receive information and / or data from an information processing system (230) via a communication module (316), process the received information and / or data, and store it in memory (312). This information and / or data may also be provided to the information processing system (230) via the communication module (316).

[0074] While the application is running, the processor (314) can receive audio data, text, images, video, etc., inputted or selected via input devices such as a touchscreen, keyboard, camera including audio sensors and / or image sensors, and microphone, which are connected to the input / output interface (318). The received audio data, text, images, and / or video, etc., can be stored in memory (312) or provided to the information processing system (230) via the communication module (316) and network (220).

[0075] The processor (314) of the user terminal (210) may transmit information and / or data to an input / output device (320) via an input / output interface (318) for output. For example, the processor (314) of the user terminal (210) may output processed information and / or data via an output device (320) such as a display output device (e.g., touchscreen, display, etc.) or an audio output device (e.g., speaker).

[0076] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from multiple user terminals (210) and / or multiple external systems. The information and / or data processed by the processor (334) may be provided to the user terminals (210) via a communication module (336) and a network (220).

[0077] In one embodiment, the processor (334) may calculate the read depth associated with the target sample for each of a predetermined number of bins on the genome based on the whole-genome analysis results associated with the target sample taken from a subject, correct the read depth associated with the target sample, and use the corrected read depth to determine the copy number variation profile associated with the target sample. The determined copy number variation profile may be provided to a user terminal (210) via a communication module (336) and a network (220).

[0078] Figure 4 shows an example of a correction dataset (400) according to one embodiment of the present disclosure. In Figure 4, the correction dataset (400) is shown in table format, but this is for illustrative purposes only and is not limited to this format. The correction dataset (400) can be embodied and used in various formats, such as matrices, arrays, and vectors.

[0079] The corrected dataset (400) may be divided into a first region (410) containing data associated with each of several samples taken from multiple subjects different from the subject, and a second region (420) containing data for correcting the read depth associated with the target sample based on this. In contrast, the data in the first region (410) may not be included in the corrected dataset (400) or may be removed from the corrected dataset (400) after being used to calculate the data in the second region (420).

[0080] In the corrected dataset (400), a row may represent each of several bins. Each of the several bins (Bin1 to Binn) may be a unit used to group or classify at least a portion of the genome for gene / genome analysis. In one embodiment, the size of each of the several bins may be selected in the range of 100 kbps (kilobasepairs) to 1 Mbps (megabasepairs).

[0081] Each of the multiple bins can be partitioned so that they do not share the same portion of the genome with each other. For example, if each of the multiple bins is 1 Mbps in size, Bin1 may group bases 1-1,000,000 within a particular chromosome, and Bin2 may group bases 1,000,001-2,000,000 within the same chromosome. Alternatively, each of the multiple bins can be partitioned so that they partially share the same portion of the genome with each other. For example, Bin1 may group bases 1-1,000,000 within a particular chromosome, and Bin2 may group bases 500,001-1,500,000 within the same chromosome. Through such a configuration, grouping the genome across multiple bins can significantly reduce the instability of the target sample's read depth due to noise (random noise or non-random noise) during read depth calculation.

[0082] The columns in the first region (410) show samples from multiple subjects by processing type (FFPE, FF), and the columns in the second region (420) show the correction direction and / or correction size of the target object. The values ​​in the correction dataset (400) may show the read depth for each of the multiple bins in the first region (410), and the correction coefficients applied to the correction direction, correction size, and read depth for each of the multiple bins in the second region (420). For example, "Depth_111" shown in Figure 4 may show the read depth for the first bin calculated from a sample (normal cell sample or abnormal cell sample) of a specific subject processed with FFPE. In this case, the read depth of each of the multiple bins (one of Depth_111 to Depth_2nn) can be a representative value among the multiple read depths for each of the multiple bins (i.e., the read depths for the multiple reads classified in each of the multiple bins), and may correspond to the arithmetic mean or median of the multiple read depths.

[0083] In one embodiment, the correction direction and related information can be determined for each of the multiple bins based on the distribution p of read depth associated with the entire FFPE-processed sample in the dataset, and the distribution q of read depth associated with the entire FF-processed sample in the dataset for each of the multiple bins. For example, the correction direction and related information for Bin1 can be determined based on the distributions of Depth_111~Depth_11n and Depth_211~Depth_21n.

[0084] For example, if a test to determine whether the distributions p and q for a given bin are statistically significantly different (e.g., Student's t-test with a p-value of 0.05) shows that the distributions p and q are significantly different (i.e., the mean and median of the two distributions differ even after considering randomness), then the bin is determined to be a noisy region, and the correction direction and related information for that bin may be set to "1" or "-1". Conversely, if the distributions p and q are not significantly different, the correction direction and related information for that bin may be set to "0".

[0085] Based on the distributions p and q, if the read depth associated with the entire FFPE-treated sample (or the mean or median of distribution p, etc.) is determined to be statistically larger than the read depth associated with the entire FF-treated sample (or the mean or median of distribution q, etc.), the correction direction and related information for that bin may be set to "-1". If it is determined to be smaller, it may be set to "1".

[0086] The information associated with the correction direction for a particular bin may indicate that the read depth for that bin should be corrected in the (-) direction if it is "-1", in the (+) direction if it is "1", and in the case of "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0" if it is "0"

[0087] In one embodiment, the correction size and related information may be determined for each of the multiple bins based on the average read depth associated with the entire FFPE-processed samples in the dataset, and for each of the multiple bins based on the average read depth associated with the entire FF-processed samples in the dataset. For example, the correction size and related information for Bin1 may be the absolute difference between the average of Depth_111~Depth_11n and the average of Depth_211~Depth_21n.

[0088] In one embodiment, the adjustment factor applied to the lead depth may be determined by the product of the adjustment direction and the adjustment size. The adjustment factor determined for each of the multiple bins can be used to adjust the lead depth associated with the target sample for each of the multiple bins. This will be described in detail in Figures 5 and 6.

[0089] Figure 5 shows the process of first-order correction of the target sample and associated read depth for each of several bins according to one embodiment of the present disclosure, and Figure 6 shows the process of determining the value of the correction variable (532) that minimizes the result of the standard deviation function according to one embodiment of the present disclosure. For first-order correction, several bins (primary bins) may be reclassified into several secondary bins (bin-of-bin, sub-bins). For example, for a subject's genome containing approximately 3 billion base pairs, more than 30,000 primary bins, each capable of grouping 100,000 bases, may be determined, and these more than 30,000 bins may be classified into more than 100 secondary bins, each capable of grouping more than 300 primary bins. In one embodiment, the number of secondary bins may be determined to be between 50 and 150, but is not limited thereto.

[0090] As shown in Figure 5, the read depth data (510) for a secondary bin may include the read depth for each of the multiple primary bins (represented by bars arranged vertically around the first baseline (512)). The first baseline (512) shows the ground truth or FF-processed read depth for each of the primary bins associated with the target sample, and the degree to which the read depth for each of the multiple primary bins deviates from the first baseline (512) may indicate the difference from the read depth calculated from the ground truth or FF-processed target sample.

[0091] The correction factor data (520) may include multiple correction factors for correcting the read depth for the primary bin. The second baseline (522) indicates a correction factor of 0, and the arrows indicating the correction factors may indicate the correction direction and correction size for the read depth for each of the multiple primary bins. In one embodiment, the correction factor may correspond to a fixed value regardless of the type of target sample, the object to be extracted, etc.

[0092] The target sample and associated read depth data for each of the multiple bins (primary bins) can be first-order corrected using the following equations 1 and 2.

number

number

[0093] Here, X'iRDR is the linearly corrected read depth data for the i-th secondary bin (540), XiRDR is the read depth data for the secondary bin before linear correction (510), POFi,adjust is the correction coefficient, α is the correction variable (532), and s() is the standard deviation function (530). That is, the correction variable (α)(532) can be determined as the arbitrary variable value (α') multiplied by the correction coefficient that minimizes the standard deviation when the correction coefficient is multiplied by an arbitrary variable value (α') and added to the read depth data for each of the primary bins included in the secondary bin. Subsequently, the read depth data for each of the primary bins included in the secondary bin can be linearly corrected by adding a correction constant, which is the product of the value of the correction variable (α)(532) and the correction coefficient. That is, the correction constant for linearly correcting the read depth data for each of the primary bins can be calculated by the product of a fixed correction coefficient and a correction variable specific to the target sample.

[0094] In one embodiment, a single correction variable (α)(532) is determined for multiple primary bins contained in a secondary bin, and the determined correction variable (α)(532) can be used for the read depth data for each of the multiple primary bins. That is, each secondary bin may not contain the same primary bin as the primary bins contained in other secondary bins, and one correction constant can be determined for each primary bin. Alternatively, each secondary bin may overlap with other secondary bins and contain the primary bins contained in other secondary bins, so one or more correction constants can be determined for a single primary bin. In this case, the mean, median, etc. of the one or more determined correction constants can be used as the final correction constant.

[0095] The standard deviation function (530) is a convex function with respect to the correction variable (α'), and therefore there can always be one minimum value. Consequently, the correction variable (α)(532) can be determined as a single variable value using methods such as grid search or line search. For example, as shown in Figure 6, the standard deviation can be calculated by repeatedly adding d to the read depth for each of the primary bins. In Figure 6, the standard deviation appears to decrease and then increase, and it can be seen that the standard deviation reaches its minimum value when d is added for the third time. Thus, the value of the correction variable (α)(532) can be determined to be α = 3d.

[0096] In summary, primary correction can be described as a method of correcting read depth by utilizing patterns of similar increases and decreases in read depth across multiple samples, and then decreasing the calculated read depth if it is above the baseline, and increasing it if it is below it. There is a great deal of direct and indirect evidence that such regions where read depth commonly increases or decreases are not limited to specific samples (one or more), but are universally defined. For example, the GC-richness of a sequence can affect the strength of DNA double bonds and the distribution of charge, which can cause DNA to exhibit different characteristics in terms of how it tries to maintain its structure. Furthermore, DNA actually has a 3D structure and exists wound like a spool in nucleosomes, and because the 3D structure determined by epigenetic modifications of DNA or nucleosomes differs, each DNA sequence has different internal properties and resistance to external changes. In FFPE-treated samples, DNA crosslinking is known to occur due to the solutions used during tissue preservation, but the regions where crosslinking is more or less likely to occur can be significantly affected by the factors mentioned above.

[0097] Contrary to what is shown and explained in Figures 5 and 6, read depth data can be corrected in a way that removes read depth data in regions where the read depth distribution increases or decreases excessively over a specific narrow area (i.e., the change in the region-relative read depth distribution is greater than or equal to a threshold).

[0098] Figure 7 shows the process of performing secondary correction to remove noise from primary-corrected read depth according to one embodiment of the present disclosure, and Figure 8 shows examples of graphs (810, 820) and coefficient sets (830, 840) used during secondary correction according to one embodiment of the present disclosure. Secondary correction is a process of removing noise from the data and may correspond to the process of removing random read depth changes occurring at a regional level after macroscopic read depth changes that appear in large units in FFPE-treated samples have been corrected through primary correction. Secondary correction may be performed through a decomposing step (710), a thresholding step (720), and a reconstruction step (730), and may be performed on the whole genome or on a portion of the genome (e.g., a chromosome), respectively.

[0099] In the decomposition step (710), a level L may be set at which the coefficient set is calculated. The maximum value of L, Lmax, can be determined by log2(N) depending on the length of the input data or the number of quadratic bins N, and the level can be a natural number between 1 and Lmax.

[0100] In the decomposition step (710), the linearly corrected data may be processed in one-dimensional vector form. For example, the linearly corrected read depths for multiple bins may be represented and processed as one-dimensional vectors.

[0101] In one embodiment, if the data length N is not a multiple of 2, zero padding may be added to the data so that the data length N is greater than N and the smallest multiple of 2. For example, for read depth data for each of 3,000 bins, 1096 zero paddings may be added, and Lmax may be determined to be log2(4096)=12. Through such a configuration, the data can be sequentially downsampled to 1 / 2 in the decomposition step (710).

[0102] Subsequently, by applying the wavelet function (wavelet function (810) in Figure 8) to the data at each level i=1, ..., Lmax, a coefficient set containing the detailed wavelet coefficient Wi (first coefficient set (830) in Figure 8) and a coefficient set containing the approximate wavelet coefficient Vi (second coefficient set (840) in Figure 8) can be determined.

[0103] The process by which the detailed wavelet coefficients and approximate wavelet coefficients are determined is expressed by the following equation 3.

number

[0104] Here, n represents the index of the data and can have an integer value from 0 to N-1. When i=0, y0 (V0[n] in Figure 7) can be the input data for the decomposition step (710) (e.g., first-order corrected read depth data for each of the multiple bins). f[n] can be selected and determined from a variety of wavelets, such as Haar, Daubechies, Coiflet, Best-localized Daubechies, or Least asymmetric wavelet. In one embodiment, f[n] may be selected as the Haar wavelet function (810) in Figure 8, which is best suited for application to step function data with discrete values ​​(copy number variation (or read depth) profiles). The Haar wavelet function (810) can be transformed and expressed as a high-pass filter (HPF) h[n] and a low-pass filter (LPF) g[n] according to Equation 4 below. In other words, f[n] in equation 3 can be h[n] when a high-pass filter is used, and g[n] when a low-pass filter is used.

number

[0105] Here, 1x(n) is the indicator function, which has a value of 1 only at n=x and is 0 in the remaining region. A high-pass filter captures local data features by allowing frequencies above a specific cutoff frequency to pass through and attenuating frequencies below it, while a low-pass filter can capture macroscopic data features by attenuating frequencies above a specific cutoff frequency and allowing frequencies below it to pass through.

[0106] Again in Equation 3, the input yi can be V0[n] in Figure 7 or Vi[n] (where i=1,2,...,L-1) that has passed through the low-pass filter g(n). yi*f can represent a function where Vi[n] (where i=1,2,...,L-1) that has passed through the low-pass filter and been downsampled by half, and the result of the convolution of the low-pass or high-pass filter is downsampled by half. Subsequently, the output value yi+1[n] corresponds to the approximate wavelet coefficients Vi (where i=1,2,...,L) in Figure 7 if it has passed through the low-pass filter, and to the detailed wavelet coefficients Wi (where i=1,2,...,L) in Figure 7 if it has passed through the high-pass filter.

[0107] In the thresholding step (720), the detailed wavelet coefficients determined in the decomposition step (710) may be thresholded.

[0108] In one embodiment, detailed wavelet coefficients can be soft-thresholded. For example, detailed wavelet coefficients whose absolute value is below the threshold are replaced with 0, and even if their absolute value exceeds the threshold, the closer they are to the threshold, the greater the attenuation of the detailed wavelet coefficients. That is, even if the absolute value of the detailed wavelet coefficient exceeds the threshold, the closer it is to the threshold, the more noise it contains, and at least some of this noise can be removed through soft-thresholding. During soft-thresholding, the soft-thresholding function (820) in Figure 8 replaces Wi where |Wi| < threshold with 0, and the remaining region can be linearly scaled.

[0109] Additionally or alternatively, detailed wavelet coefficients can be subjected to hierarchical thresholding. That is, the higher the level i=1, ..., Lmax, the lower the value of the threshold λ may be. Through such a configuration, higher levels of detailed wavelet coefficients, which contain macroscopic morphological information of the signal, are less likely to be replaced with zero or attenuated, and the macroscopic copy number variation profile can be preserved to the greatest extent possible. In other words, high-frequency noise can be selectively removed through hierarchical thresholding. For example, the threshold λ can be weighted with respect to level i by a value of 1-α*i, as shown in Equation 5 below.

number

[0110] Here, N is the data length or the total number of secondary bins, and α is a predetermined arbitrary constant whose value can be changed later. For example, if, after read depth correction, it is determined that noise has not been sufficiently removed, the threshold can be increased by decreasing the α value, thereby further removing noise. σ can be estimated using the mean absolute deviation (MAD) in Equation 6 below.

number

[0111] Subsequently, in the reconstructing step (730), the decomposition step (710) may be performed in reverse order. For example, the thresholded detailed wavelet coefficients and their corresponding approximate wavelet coefficients may be alternately upsampled, and the convolutions of g[-n] and h[-n] may be performed sequentially for each level (see Equation 4 for functions g and h).

[0112] Figure 9 shows examples of a first heatmap (910) associated with an FF-treated sample and a second heatmap (920) associated with an FFPE-treated sample according to one embodiment of the present disclosure. The indices along the vertical axis in Figure 9 indicate samples taken from different subjects, and samples assigned the same number may be taken from the same subject. For example, "FF1" in the first heatmap (910) and "FFPE1" in the second heatmap (920) may be samples taken from the same subject but treated with different methods.

[0113] A striped pattern can be observed in the second heatmap (920), but it can be confirmed that no striped pattern is present in the first heatmap (910). The striped pattern in the second heatmap (920) indicates specific noise that occurs in FFPE-treated samples, caused by a similar change in read depth (i.e., increase or decrease) at each position of multiple FFPE-treated samples.

[0114] Figure 10 shows an example of a distribution graph (1010, 1020) of sample-specific read depth ratios before and after correction of the read depth associated with FFPE-treated samples according to one embodiment of the present disclosure. The first graph (1010) shows the mean distribution of sample-specific read depth ratios before correction of the read depth associated with FFPE-treated samples, and the second graph (1020) shows the mean distribution of sample-specific read depth ratios after correction according to the present disclosure for the read depth associated with FFPE-treated samples. The indices indicated along the vertical axis of graphs (1010, 1020) indicate specific locations on the genome sequence.

[0115] In the first graph (1010), it can be seen that the read depth ratio follows different distributions depending on the sample processing method (FFPE, FF). On the other hand, in the second graph (1020), it can be seen that the read depth ratios depending on the sample processing method follow similar distributions after the read depth associated with the FFPE-processed sample is corrected. In other words, even if FFPE-processed samples are used when determining the copy number variation profile (or read depth), similar data can be obtained when correcting the read depth according to this disclosure, similar to that obtained when using FF-processed samples.

[0116] Figure 11 shows an example of chromosome-specific read depth distribution according to one embodiment of the present disclosure. The indices displayed along the horizontal axis of each graph (1110, 1120, 1130, 1140) represent autosomal chromosome numbers and sex chromosomes, while the indices displayed along the vertical axis represent read depth ratios. That is, the black dots in each graph (1110, 1120, 1130, 1140) may represent, in bin units, the ratio between the read depth obtained as a result of sequencing a normal cell sample and the read depth obtained as a result of sequencing an abnormal cell sample at a specific base sequence location.

[0117] Graph 1 (1110) is a graph associated with FF-treated samples, Graph 2 (1120) is a graph associated with FFPE-treated samples from the same subjects, Graph 3 (1130) is a graph associated with the data in Graph 2 (1120) as first corrected by this disclosure, and Graph 4 (1140) is a graph associated with the data in Graph 2 (1120) as first corrected and second corrected by this disclosure.

[0118] The distributions in the first graph (1110) and the second graph (1120) differ slightly, and it can be seen that noise occurred at a higher frequency in the second graph (1120). On the other hand, in the third graph (1130), it can be seen that the data has been corrected to be similar to the distribution of the first graph (1110). Furthermore, in the fourth graph (1140), it can be seen that the distribution is very similar to the distribution of the first graph (1110) because noise has been additionally removed from the data of the third graph (1130).

[0119] Figure 12 shows an SNR (signal-to-noiseratio) graph (1200) for evaluating the degree of qualitative improvement in the copy number variation profile by read depth correction according to one embodiment of the present disclosure. The SNR value may be calculated for each sample (shown as a dot), and the SNR graph (1200) shown in Figure 12 includes a first box plot (1210) showing the SNR value calculated for each sample before read depth correction and a second box plot (1220) showing the SNR value calculated after read depth correction for FFPE-treated samples. The SNR value may be calculated by the following formula 7.

number

[0120] Here, DFF is an interpretation of the read depth of the FF-processed sample as a random variable, and DFFPE is an interpretation of the read depth of the FFPE-processed sample as a random variable.

[0121] When correction is performed only on the read depth of FFPE-treated samples according to this disclosure, the DFF remains the same before and after read depth correction, while the DFFPE may change before and after read depth correction. As a result, it can be confirmed that the SNR value in the first box plot (1210) generally increases in the second box plot (1220) after read depth correction. In other words, it can be confirmed that the quality of the copy number variation profile is improved by correcting the read depth.

[0122] Figure 13 is a flowchart of a copy number variation profiling method (1300) according to one embodiment of the present disclosure. The method (1300) may be performed by at least one processor (e.g., a processor in a user terminal, a processor in an information processing system, or a processor in a copy number variation profiling device). The method (1300) may be initiated by the processor obtaining whole-genome analysis results associated with a target sample taken from a subject (S1310). In one embodiment, the target sample may be an FFPE (Formalin-Fixed, Paraffin-Embedded) treated sample.

[0123] Subsequently, the processor can calculate the read depth associated with the target sample for each of several predetermined bins on the genome, based on the obtained whole-genome analysis results (S1320).

[0124] Subsequently, the processor may correct the read depth associated with the target sample (S1330).

[0125] In one embodiment, the processor may perform a first-order correction of the read depth associated with a target sample using a dataset containing read depth data for each of several bins associated with each of several samples taken from multiple subjects different from the subject. In this case, the dataset may include at least one of the following: data associated with normal cell samples taken from each of the multiple subjects, or data associated with abnormal cell samples taken from each of the multiple subjects, and each of the normal cell samples and abnormal cell samples may include FFPE-treated samples and FF (Fresh Frozen) samples.

[0126] In one embodiment, the dataset may further include information related to the read depth correction direction for each of the multiple bins, for use in correcting the read depth of the target sample. The information related to the read depth correction direction for each of the multiple bins may be determined for each of the multiple bins based on the distribution of read depths related to the entire FFPE-treated sample in the dataset, and for each of the multiple bins based on the distribution of read depths related to the entire FF-treated sample in the dataset.

[0127] In one embodiment, the dataset may further include information related to the read depth correction size for each of the multiple bins, for use in correcting the read depth of the target sample. The correction size and related information may be determined for each of the multiple bins based on the average read depth associated with all FFPE-treated samples in the dataset, and for each of the multiple bins based on the average read depth associated with all FF-treated samples in the dataset.

[0128] The processor can perform primary correction by correcting the target sample and associated read depth based on information related to the correction direction and information related to the correction size.

[0129] The processor may perform further secondary correction to remove noise from the first-order corrected read depth. In one embodiment, the processor may determine a plurality of wavelet coefficients using a predetermined wavelet and the first-order corrected read depth, threshold the plurality of wavelet coefficients, and determine a secondary corrected read depth based on the thresholded plurality of wavelet coefficients. In this case, the predetermined wavelet may be a Haar wavelet.

[0130] The processor can threshold multiple wavelet coefficients by substituting a specific wavelet coefficient to zero if its absolute value is below a predetermined threshold, and by attenuating the specific wavelet coefficient to a higher degree if its absolute value is greater than or equal to the threshold, with the smaller the difference between the absolute value and the threshold. The higher the level of the specific wavelet coefficient, the lower the threshold can be.

[0131] Subsequently, the processor may use the corrected read depth to determine a copy number variation profile associated with the target sample (S1340). In one embodiment, the target sample includes normal and abnormal cell samples taken from a subject, and the processor may determine the copy number variation profile based on the read depth associated with the normal cell sample, the read depth associated with the abnormal cell sample, and the allele frequency associated with the target sample.

[0132] The flowchart shown in Figure 13 and the above description are merely examples and may be implemented differently in some embodiments. For example, one or more steps may be omitted, the order of each step may be changed, one or more steps may be repeated, or one or more steps may be repeated multiple times.

[0133] The methods described above may be provided as computer programs stored on a computer-readable recording medium for execution on a computer. The medium may continuously store computer-executable programs or temporarily store them for execution or download. The medium may also be various recording or storage means in the form of a combination of one or more hardware components, and is not limited to a medium directly connected to a computer system, but may be distributed over a network. Examples of mediums may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and devices configured to store program instructions, including ROM, RAM, and flash memory. Other examples of mediums include recording or storage media managed by app stores that distribute applications and various other software supply or distribution sites and servers.

[0134] The methods, operations, or techniques described herein can be embodied by a variety of means. For example, these techniques can be embodied in hardware, firmware, software, or a combination thereof. A person of ordinary skill will understand that the various exemplary logic blocks, modules, circuits, and algorithmic steps described in conjunction with the disclosure may be embodied in electronic hardware, computer software, or a combination thereof. To illustrate such interchangeability of hardware and software, the various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functionality is embodied as hardware or as software depends on the design requirements imposed on the particular application and the overall system. A person of ordinary skill may embodied the functionality described in a variety of ways for each specific application, but such embodiments should not be construed as exceeding the scope of this disclosure.

[0135] In hardware implementation, the processing units used to perform the techniques may be embodied in one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, computers, or combinations thereof.

[0136] Accordingly, the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure may be embodied or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate and transistor logic, discrete hardware components, or any combination of those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, a processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be embodied as a combination of computing devices, e.g., a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other combination of configurations.

[0137] In firmware and / or software embodiments, the technique may be embodied as instructions stored on a computer-readable medium such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), or magnetic or optical data storage devices. The instructions may be executable by one or more processors, causing one or more processors to perform certain aspects of the functions described herein.

[0138] Although the embodiments described above are described as utilizing aspects of the subject matter currently disclosed in one or more standalone computer systems, the disclosure is not limited to and can be embodied in any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the disclosure can be embodied in multiple processing chips or devices, and storage can be similarly affected across multiple devices. These devices may include PCs, network servers, and portable devices.

[0139] While this disclosure has been described in relation to some embodiments, various modifications and alterations can be made without departing from the scope of this disclosure as understood by a person of the ordinary skill in the art to which the invention of this disclosure pertains. Such modifications and alterations should be considered to fall within the scope of the claims appended to this specification.

Claims

1. A method for determining copy number variation profiles by read depth correction, which is performed by at least one processor, The steps include obtaining whole-genome analysis results associated with target samples taken from subjects, Based on the obtained whole-genome analysis results, the step of calculating the read depth associated with the target sample for each of the predetermined bins on the genome, The steps include correcting the read depth associated with the target sample, Using the corrected read depth, the copy number variant (Cop) associated with the target sample is used. This includes the step of determining the y Number Variation profile, The step of correcting the read depth associated with the target sample includes the step of performing a primary correction of the read depth associated with the target sample based on the corrected read depth and related information, The correction and related information are determined based on a comparison of a first dataset, which includes read depth data associated with samples taken from multiple subjects different from the subject and processed using FFPE (Formalin-Fixed, Paraffin-Embedded), and a second dataset, which includes read depth data associated with samples taken from the multiple subjects and processed using FF (Fresh Frozen). The information related to the correction includes information related to the correction direction and correction size of the lead depth for each of the plurality of bins. The information related to the correction direction is determined for each of the plurality of bins based on the difference between the distribution of read depth in the first dataset and the distribution of read depth in the second dataset. The correction size and related information are determined for each of the plurality of bins based on the difference between the average read depth in the first dataset and the average read depth in the second dataset. A method for determining a copy number variant profile, wherein the step of performing the primary correction includes correcting the read depth associated with the target sample based on information related to the correction direction and information related to the correction size.

2. The method for determining a copy number variation profile according to claim 1, wherein the target sample includes a normal cell sample and an abnormal cell sample taken from the subject, and the step of determining the copy number variation profile includes determining the copy number variation profile based on read depth associated with the normal cell sample, read depth associated with the abnormal cell sample, and allele frequency associated with the target sample.

3. The method for determining a copy number variation profile according to claim 1, wherein the target sample is a sample treated with FFPE (Formalin-Fixed, Paraffin-Embedded).

4. The copy number variation profiling method according to claim 1, wherein the read depth associated with the target sample includes read depth for each of a plurality of predetermined bins on the genome of the target sample, and each of the read depth data associated with the FFPE-treated sample and the read depth data associated with the FF-treated sample includes read depth data for each of the plurality of bins.

5. The copy number variation profile determination method according to claim 1, wherein the FFPE-treated sample or the FF-treated sample comprises at least one of the normal cell samples or abnormal cell samples taken from each of the plurality of subjects.

6. The copy number variation profile determination method according to claim 1, wherein the read depth associated with the target sample includes read depth for each of a plurality of predetermined bins on the genome of the target sample, and the information associated with the correction further includes information associated with the correction direction of the read depth for each of the plurality of bins.

7. The method for determining a copy number variation profile according to claim 6, wherein the information related to the correction direction is determined by comparing the distribution of read depth in the first dataset with the distribution of read depth in the second dataset for each of the plurality of bins.

8. The copy number variant profile determination method according to claim 6, wherein the information related to the correction further includes information related to the correction size of the read depth for each of the plurality of bins.

9. The method for determining a copy number variant profile according to claim 8, wherein the information related to the correction size is determined by comparing the average read depth in the first dataset with the average read depth in the second dataset for each of the plurality of bins.

10. The copy number variation profile determination method according to claim 8, wherein the step of performing the primary correction includes correcting the read depth associated with the target sample based on information related to the correction direction and information related to the correction size.

11. The method for determining a copy number variant profile according to claim 1, wherein the step of correcting the read depth associated with the target sample further includes the step of performing a secondary correction to remove noise from the primary corrected read depth.

12. The copy number variation profile determination method according to claim 11, wherein the step of performing a secondary correction to remove noise from the primary corrected read depth includes the steps of determining a plurality of wavelet coefficients using a predetermined wavelet and the primary corrected read depth, thresholding the plurality of wavelet coefficients, and determining a secondary corrected read depth based on the thresholded plurality of wavelet coefficients.

13. The copy number variation profile determination method according to claim 12, wherein the predetermined wavelets correspond to Haar wavelets.

14. The method for determining a copy number variant profile according to claim 12, wherein the step of thresholding the plurality of wavelet coefficients includes the step of replacing a specific wavelet coefficient with 0 if the absolute value of the specific wavelet coefficient is less than a predetermined threshold, and the step of attenuating the specific wavelet coefficient to a higher degree if the absolute value of the specific wavelet coefficient is greater than or equal to the threshold, the smaller the difference between the absolute value and the threshold.

15. The method for determining a copy number mutation profile according to claim 14, wherein the threshold becomes lower as the specific wavelet coefficient reaches a higher level.

16. A computer-readable recording medium for performing the method according to any one of claims 1 to 15 on a computer.

17. A computing device, The system includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory. The at least one program obtains whole-genome analysis results associated with a target sample taken from a subject, calculates the read depth associated with the target sample based on the obtained whole-genome analysis results, corrects the read depth associated with the target sample, and uses the corrected read depth to calculate the copy number variation (Cop) associated with the target sample. Includes instructions for determining the y Number Variation profile, Correcting the read depth associated with the target sample includes performing a primary correction of the read depth associated with the target sample based on the corrected read depth and related information. The correction and related information are determined based on a comparison of a first dataset, which includes read depth data associated with samples taken from multiple subjects different from the subject and processed using FFPE (Formalin-Fixed, Paraffin-Embedded), and a second dataset, which includes read depth data associated with samples taken from the multiple subjects and processed using FF (Fresh Frozen). The information related to the correction includes information related to the correction direction and correction size of the lead depth for each of the plurality of bins. The information related to the correction direction is determined for each of the plurality of bins based on the difference between the distribution of read depth in the first dataset and the distribution of read depth in the second dataset. The correction size and related information are determined for each of the plurality of bins based on the difference between the average read depth in the first dataset and the average read depth in the second dataset. A computing device comprising the step of performing the first correction, which includes correcting the read depth associated with the target sample based on information related to the correction direction and information related to the correction size.

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