A method and system for estimating chromosome arm telomere length

Abstract

The present invention relates to a method and system for estimating chromosome arm telomere length, which comprises: obtaining 10X genome sequencing data; calculating the number of telomeres whose length differences between the entire 10XG molecules in the 10X genome sequencing data do not overlap, or calculating the number of telomeres of mapped DNA fragments that overlap with the 10XG molecules, and probabilizing the number of telomeres to a currently estimated chromosome arm-level telomere length score chArmTL, and repeating the above calculation until the chromosome arm telomere length score chArmTL converges, thereby obtaining the corresponding chromosome arm telomere length TL. Alternatively, each unmapped 10XG molecule is assigned to a mappable chromosome arm selected according to a 10X barcode; reading the 10X barcodes corresponding to all assigned 10XG molecules, reading reads from 10XG molecules with the same 10X barcode, summing the reads, and probabilizing the sums to a currently estimated chromosome arm-level telomere length score chArmTL; and repeating the above calculation until the chromosome arm telomere length score chArmTL converges, thereby obtaining the corresponding chromosome arm telomere length TL.

Description

Technical Field

[0001] The present invention relates to the technical field, and more specifically, to a method and system for estimating chromosome arm telomere length. Background Art

[0002] Telomeres are tandem repeats of multiple guanine residues (TTAGGG) located at the ends of eukaryotic chromosomes. In most normal human cells, telomeres shorten with each cell division, leading to the hypothesis that telomeres represent a marker that limits the number of times a cell can replicate. Telomeres are not linear structures; instead, key proteins maintain telomeric DNA in a circular structure that protects the ends of chromosomes. Using whole-genome sequencing data to estimate telomere length in tumor samples, researchers have found that telomere length is associated with many important tumor phenotypes.

[0003] Telomere length monitoring can help identify individuals at increased risk for diseases associated with short telomeres. Furthermore, effective methods for measuring telomere length have potentially significant implications for aging and cancer. Telomeres shorten with each cycle of cell division, and various experimental and computational methods have been developed to measure telomere length, including terminal restriction fragment analysis (TRF), quantitative PCR (qPCR), quantitative fluorescence in situ hybridization (Q-FISH), and single telomere length analysis (STELA). However, TRF is limited by the large amount of DNA required and the insensitivity to short telomeres due to the inefficient binding of probes to short telomeres. The limitation of qPCR is that it requires the amplification of control genes, and the amplified control genes are unique in the genome. The variation in their copy number and chromosome replication will change the gene copy number, thereby significantly changing the T / S value. Therefore, the qPCR method is only suitable for diploid and karyotype-stable cells and samples, and is not suitable for transformed cell lines and tumor tissue samples. Interphase Q-FISH cannot analyze the telomere length of each chromosome and telomere-free chromosomes, and the result is also the average telomere length. The STELA method requires experience in single-molecule PCR technology, has high technical operation requirements, and the operation process is relatively time-consuming, making it unsuitable for clinical application. Comparative analysis of these telomere length detection methods shows that there is currently a lack of a single method that can accurately, simply and quickly detect telomere length. Therefore, the selection of telomere length detection methods needs to be based on specific scientific problems. Summary of the Invention

[0004] The present invention overcomes the above-mentioned lack of a method for accurately, simply and quickly detecting telomere length in the prior art and provides a chromosome arm telomere length estimation method and system.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A method for estimating chromosome arm telomere length (TeloDiff) comprises the following steps:

[0007] Obtain 10X genome sequencing data; calculate the number of telomeres whose length differences between the entire 10XG molecules do not overlap in the 10X genome sequencing data, or calculate the number of telomeres of mapped DNA fragments that overlap with 10XG molecules, and proportionalize the telomere number probability to its current estimated chromosome arm-level telomere length score chArmTL, and repeat the above calculation until the chromosome arm telomere length score chArmTL converges, and output the chromosome arm telomere length score chArmTL.

[0008] As a preferred embodiment, the step of calculating the fraction of the chromosome arm telomere length TL comprises:

[0009] Calculate the average length La of 10XG molecules in the 10X genome sequencing data;

[0010] For each chromosome arm, calculate the average length Lm of the overlapping portion of the 10XG molecules adjacent to the telomere of the chromosome arm;

[0011] The difference between the average length La and the average length Lm is used as the currently estimated chromosome arm-level telomere length fraction chArmTL.

[0012] As a preferred embodiment, the method further comprises the following steps:

[0013] Update the currently estimated chromosome arm telomere length TL as the total telomere reads assigned to each chromosome arm; repeat the above calculation until the fraction chArmTL of the chromosome arm telomere length TL converges, and output the chromosome arm telomere length estimation result.

[0014] As a preferred embodiment, the method further comprises the following steps:

[0015] Constructing a random forest model, wherein the score chArmTL is used as a predicted label of the random forest model, and the frequency X of repeated reads in the whole genome sequencing data is used as a model input;

[0016] The read frequency of the whole-genome sequencing telomeres was multiplied by the ratio of the total telomere reads of 10x Genomics samples to the average of the total telomere reads of each whole-genome sequencing sample, and the scaled whole-genome sequencing telomere frequency was further quantile normalized and converted into a z-score and input into the random forest model for training to obtain a random forest model for predicting the score chArmTL.

[0017] Furthermore, the present invention also proposes a chromosome arm telomere length estimation method (TeloEM), comprising the following steps:

[0018] Obtain 10X genome sequencing data;

[0019] Each unmapped 10XG molecule was assigned to a mappable chromosome arm selected based on the 10X barcode;

[0020] Read the corresponding 10X barcodes of all assigned 10XG molecules, read reads from 10XG molecules with the same 10X barcode, sum the reads and then convert their probabilities into the current estimated chromosome arm-level telomere length fraction chArmTL; repeat the above calculation until the chromosome arm telomere length fraction chArmTL converges to obtain the corresponding chromosome arm telomere length TL.

[0021] As a preferred embodiment, the step of assigning each unmapped 10XG molecule to a mappable chromosome arm selected according to the 10X barcode comprises:

[0022] Calculating the probability of being assigned to any chromosome arm, and taking the corresponding chromosome arm when the probability is greater than a preset threshold as a mappable chromosome arm;

[0023] The probability of being assigned to any chromosome arm includes the estimated telomere length of the chromosome arm accounted for by the sum of the estimated telomere lengths of chromosome arms selected with the same barcode.

[0024] As a preferred embodiment, the method further includes the following steps: updating the currently estimated chromosome arm telomere length TL as the total telomere reads assigned to each chromosome arm; repeating the above calculation until the fraction chArmTL of the chromosome arm telomere length TL converges to obtain the corresponding chromosome arm telomere length TL.

[0025] As a preferred embodiment, the method further comprises the following steps:

[0026] Constructing a random forest model, wherein the score chArmTL is used as a predicted label of the random forest model, and the frequency X of repeated reads in the whole genome sequencing data is used as a model input;

[0027] The read frequency of the whole-genome sequencing telomeres was multiplied by the ratio of the total telomere reads of 10x Genomics samples to the average of the total telomere reads of each whole-genome sequencing sample, and the scaled whole-genome sequencing telomere frequency was further quantile normalized and converted into a z-score and input into the random forest model for training to obtain a random forest model for predicting the score chArmTL.

[0028] Furthermore, the present invention also proposes a chromosome arm telomere length estimation system, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the chromosome arm telomere length estimation method proposed in any of the above technical solutions are implemented.

[0029] Compared with the existing technology, the beneficial effects of the technical solution of the present invention are: the present invention uses high-throughput data to estimate the telomere length of a single chromosome arm, and can estimate the telomere length at the chromosome arm level in 10X genome sequencing data and high-throughput whole-genome sequencing samples, realizing accurate, simple and rapid telomere length detection, and is suitable for the study of chromosome arm-level telomeres in large-scale projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the method for estimating chromosome arm telomere length in Example 1.

[0031] Figure 2 This is a schematic diagram of the principle of the chromosome arm telomere length estimation method of Example 1.

[0032] Figure 3 This is a flow chart of the method for estimating chromosome arm telomere length in Example 2.

[0033] Figure 4 This is a schematic diagram of the chromosome arm telomere length estimation method of Example 2.

[0034] Figure 5 This is a data diagram showing the differences in chromosome length between different races in Example 3.

[0035] Figure 6 This is the difference data of chArmTLs between tumor and normal tissues in Example 3. DETAILED DESCRIPTION

[0036] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0037] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0038] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0039] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0040] Example 1

[0041] This example proposes a method for estimating chromosome arm telomere length (TeloDiff), such as Figure 1 FIG. 1 is a flow chart of the method for estimating chromosome arm telomere length according to the present embodiment.

[0042] The method for estimating chromosome arm telomere length proposed in this embodiment includes the following steps:

[0043] S1. Obtain sequencing data for 10X genome samples.

[0044] S2. Calculate the number of telomeres whose length differences between the entire 10XG molecules in the 10X genome sequencing data do not overlap, or calculate the number of telomeres of mapped DNA fragments that overlap with 10XG molecules, and convert the telomere number probability into its current estimated chromosome arm-level telomere length fraction chArmTL;

[0045] The above calculation is repeated until the chromosome arm telomere length fraction chArmTL converges to obtain the corresponding chromosome arm telomere length TL.

[0046] The chromosome arm telomere length estimation method, TeloDiff, proposed in this example, uses 10X genome sequencing data as its primary tool. This is based on the high throughput of 10X genome sequencing and the fact that linked reads can effectively provide information spanning tens of kilobases or even longer. Barcode overlap relationships can accurately distinguish non-adjacent regions in haplotype variants. In theory, 10X genome sequencing can be used to estimate the telomere length of each chromosome arm.

[0047] This embodiment considers that the telomere length is approximately the length composed of the number of non-overlapping telomeres with length differences between the entire 10XG molecules, or the length composed of the number of telomeres of mapped DNA fragments that overlap with 10XG molecules, and the corresponding telomere number probability is proportional to its currently estimated chromosome arm-level telomere length fraction chArmTL to achieve chromosome arm telomere length estimation.

[0048] like Figure 2 FIG. 1 is a diagram showing the principle of estimating the telomere length of chromosome arms according to the present embodiment.

[0049] For each arm, the average length La of 10XG molecules in the 10X genome sequencing data was calculated. For each chromosome arm, the average length Lm of the overlapping portion of the 10XG molecules that overlapped with the telomere-adjacent portion of the chromosome arm was calculated.

[0050] The difference between the average length La and the average length Lm is used as the current estimated chromosome arm-level telomere length fraction chArmTL. The pseudo code is as follows:

[0051]

[0052] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0053] Update the current estimated chromosome arm telomere length TL as the total telomere reads assigned to each chromosome arm; repeat the above calculation until the fraction chArmTL of the chromosome arm telomere length TL converges to obtain the corresponding chromosome arm telomere length TL.

[0054] Further, to speed up the algorithm and to reduce the cost of 10XG data storage, in one embodiment, only 10X reads mapped to the corresponding telomeres within 1 Mb (similar results using values ​​from 1 Mb to 10 Mb with a step size of 1 Mb) were used to calculate the size of each chArmTL estimated background 10XG molecule.

[0055] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0056] S3.1. Construct a random forest model, wherein the score chArmTL is used as the predicted label of the random forest model, and the frequency X of repeated reads in the whole genome sequencing data is used as the model input.

[0057] S3.2. Multiply the reading frequency of the whole-genome sequencing telomeres by the ratio of the total telomere readings of 10x Genomics samples to the average of the total telomere readings of each whole-genome sequencing sample, and then further quantile normalize the scaled whole-genome sequencing telomere frequency and convert it into a z-score before inputting it into the random forest model for training to obtain a random forest model for predicting the score chArmTL.

[0058] Specifically, this example uses the frequency X of repeated reads in the whole genome sequencing data (TTAGGG)n as the input to the random forest model. In one specific embodiment, the frequency X of repeated reads from 52 cell line samples is used as the input to the random forest model, where each sample has 92 variables. The output of the random forest model is chArmTL for the 52 cell line samples. Its pseudo code is as follows:

[0059]

[0060] In this embodiment, the telomere length of a single chromosome arm is estimated using high-throughput data, which can be applied to study chromosome arm (ChArm)-level telomere length estimation in large-scale projects.

[0061] Example 2

[0062] This example proposes a chromosome arm telomere length estimation method (TeloEM), such as Figure 3 FIG. 1 is a flow chart of the method for estimating chromosome arm telomere length according to the present embodiment.

[0063] The method for estimating chromosome arm telomere length proposed in this embodiment includes the following steps:

[0064] S1. Obtain 10X genome sequencing data.

[0065] S2. Assign each unmapped 10XG molecule to a mappable chromosome arm selected based on the 10X barcode.

[0066] S3. Read the corresponding 10X barcodes of all assigned 10XG molecules, read reads from 10XG molecules with the same 10X barcode, sum the reads and then divide their probabilities into the current estimated chromosome arm-level telomere length fraction chArmTL; repeat the above calculation until the chromosome arm telomere length fraction chArmTL converges to obtain the corresponding chromosome arm telomere length TL.

[0067] In the chromosome arm telomere length estimation method TeloEM proposed in this example, the unmapped telomeres in each 10X barcode (Bx) are assigned to mappable chromosome arms in molecules with the same barcode Bx. The corresponding 10X barcodes of all assigned 10XG molecules are then read, and reads are generated from 10XG molecules with the same 10X barcode. The sum of the reads of the 10X molecules assigned to the telomere portion of each chromosome arm is used as the new length estimate. Furthermore, an expectation maximization (EM) iterative strategy is optionally used for updating to obtain the corresponding chromosome arm telomere length TL.

[0068] like Figure 4 , which is a schematic diagram of the principle of the chromosome arm telomere length estimation method of this embodiment.

[0069] In an optional embodiment, the step of assigning each unmapped 10XG molecule to a mappable chromosome arm selected according to the 10X barcode comprises:

[0070] The probability of being assigned to any chromosome arm is calculated, and the corresponding chromosome arm is considered to be a mappable chromosome arm when the probability is greater than a preset threshold. The probability of being assigned to any chromosome arm includes the sum of the estimated telomere length of the chromosome arm and the estimated telomere length of the chromosome arms selected with the same barcode.

[0071] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0072] Update the current estimated chromosome arm telomere length TL as the total telomere reads assigned to each chromosome arm; repeat the above calculation until the fraction chArmTL of the chromosome arm telomere length TL converges to obtain the corresponding chromosome arm telomere length TL.

[0073] This embodiment is constructed using an expectation maximization (EM) iterative strategy, in which all chArmTL are initialized to be equal. During each iteration, the unmapped telomeres in each 10X barcode (Bx) are assigned to the mappable chromosome arms in the molecules with the same barcode Bx, with a probability proportional to the current estimated chArmTL divided by the sum of chArmTL. Then, each chArmTL is updated to the total assigned telomere reads for each arm. These two steps are repeated until all chArmTL converge to obtain the corresponding chromosome arm telomere length TL. Its pseudo code is as follows:

[0074]

[0075]

[0076] Furthermore, in an optional embodiment, the method further comprises the following steps:

[0077] A random forest model is constructed, wherein the score chArmTL is used as the predicted label of the random forest model, and the frequency X of repeated reads in the whole genome sequencing data is used as the model input.

[0078] The read frequency of the whole-genome sequencing telomeres was multiplied by the ratio of the total telomere reads of 10x Genomics samples to the average of the total telomere reads of each whole-genome sequencing sample, and the scaled whole-genome sequencing telomere frequency was further quantile normalized and converted into a z-score and input into the random forest model for training to obtain a random forest model for predicting the score chArmTL.

[0079] Example 3

[0080] This example applies the chromosome arm telomere length estimation methods TeloDiff and TeloEM proposed in Examples 1 and 2 to human samples.

[0081] A recent study found that African Americans have longer average telomere lengths than European Americans. From the estimated chArmTLs from 10 XG samples, we observed that Europeans (EUR) had longer chArmTLs overall than Africans (AFR). Furthermore, EUR, 278 East Asians (EAS), and Ad-admixed Americans (AMR) exhibited similar chArmTLs, while AFR and South Asians (SAS) exhibited similar chArmTLs. We then compared EUR and AFR within each group and found that, although EUR tended to show longer chromosomal arms than AFR for most, only seven arms showed general significance (P values ​​< 0.05 and > 0.01), with some chromosomal arms tending to be longer for AFR than for EUR. Therefore, we hypothesized that expanding the population sample would provide further insights into population differences in chromosome length.

[0082] In this example, TeloDiff and TeloEM were applied to the charmtl estimation of 1000 GP samples (n=150) selected from the Illumina project. Figure 5 Figure 2 shows the data for chromosome length differences between different ethnic groups in this example. The experimental data showed that 15 chArmTLs in African 287 (AFR) were longer than those in European (EUR), while 23 chArmTLs were shorter in AFR. Furthermore, in AFR 28, three chromosome arms (17p, 19p, and 8p) tended to be longer (although not significantly so).

[0083] This example also applies TeloDiff and TeloEM to a large number of cancer samples to detect average telomere length. To study chArmTLs in cancer, we also attempted to predict chArmTLs in liver cancer WGS samples (54 tumors and 54 matched normal samples) from The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC) project.

[0084] like Figure 6 Figure 2 shows the difference in chArmTLs between tumor and normal tissue in this example. Experimental data show that the charmTLs in tumors are shorter (19 short arms and 7 long arms), which is consistent with previous reports that liver tumors show an average TL size 40 shorter than normal tissue. The predicted charmTLs are negatively correlated with tumor purity.

[0085] It can be seen that the chromosome arm telomere length estimation method proposed in the present invention can effectively, accurately, simply and quickly detect telomere length.

[0086] Example 4

[0087] This embodiment proposes a chromosome arm telomere length estimation system, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the chromosome arm telomere length estimation method proposed in Example 1.

[0088] Specifically, when the processor executes the computer program, the processor performs the following steps:

[0089] S1. Obtain 10X genome sequencing data.

[0090] S2. Calculate the number of telomeres whose length differences between the entire 10XG molecules in the 10X genome sequencing data do not overlap, or calculate the number of telomeres of mapped DNA fragments that overlap with 10XG molecules, and convert the telomere number probability into its current estimated chromosome arm-level telomere length fraction chArmTL;

[0091] The above calculation is repeated until the chromosome arm telomere length fraction chArmTL converges to obtain the corresponding chromosome arm telomere length TL.

[0092] For each arm, the average length La of 10XG molecules in the 10X genome sequencing data was calculated. For each chromosome arm, the average length Lm of the overlapping portion of the 10XG molecules that overlapped with the telomere-adjacent portion of the chromosome arm was calculated.

[0093] The difference between the average length La and the average length Lm is used as the currently estimated chromosome arm-level telomere length fraction chArmTL.

[0094] Furthermore, the currently estimated chromosome arm telomere length TL is updated as the total telomere reads assigned to each chromosome arm; the above calculation is repeated until the fraction chArmTL of the chromosome arm telomere length TL converges to obtain the corresponding chromosome arm telomere length TL.

[0095] Example 5

[0096] This embodiment proposes a chromosome arm telomere length estimation system, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the chromosome arm telomere length estimation method proposed in Example 2.

[0097] Specifically, when the processor executes the computer program, the processor performs the following steps:

[0098] S1. Obtain 10X genome sequencing data.

[0099] S2. Assign each unmapped 10XG molecule to a mappable chromosome arm selected based on the 10X barcode.

[0100] S3. Read the corresponding 10X barcodes of all assigned 10XG molecules, read reads from 10XG molecules with the same 10X barcode, sum the reads and then divide their probabilities into the current estimated chromosome arm-level telomere length fraction chArmTL; repeat the above calculation until the chromosome arm telomere length fraction chArmTL converges to obtain the corresponding chromosome arm telomere length TL.

[0101] The step of assigning each unmapped 10XG molecule to a mappable chromosome arm selected according to the 10X barcode comprises:

[0102] The probability of being assigned to any chromosome arm is calculated, and the corresponding chromosome arm is considered to be a mappable chromosome arm when the probability is greater than a preset threshold. The probability of being assigned to any chromosome arm includes the sum of the estimated telomere length of the chromosome arm and the estimated telomere length of the chromosome arms selected with the same barcode.

[0103] Furthermore, the currently estimated chromosome arm telomere length TL is updated as the total telomere reads assigned to each chromosome arm; the above calculation is repeated until the fraction chArmTL of the chromosome arm telomere length TL converges to obtain the corresponding chromosome arm telomere length TL.

[0104] The same or similar reference numerals correspond to the same or similar components;

[0105] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0106] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for estimating chromosome arm telomere length, characterized in that: The following steps are involved: Obtain 10X genome sequencing data; for each branch, calculate the number of telomeres whose length differences between the entire 10XG molecules in the 10X genome sequencing data do not overlap, or calculate the number of telomeres of mapped DNA fragments that overlap with 10XG molecules, and proportionalize the telomere number probability to its current estimated chromosome arm-level telomere length score chArmTL, and repeat the calculation until the chromosome arm telomere length score chArmTL converges to obtain the corresponding chromosome arm telomere length TL; wherein, Calculate the chromosome arm-level telomere length fraction chArmTL The steps include: Calculate the average length of 10XG molecules in the 10X genome sequencing data La ; For each chromosome arm, calculate the average length of the overlapping portion of the 10XG molecules adjacent to the telomere of the chromosome arm. Lm ; The average length La With the average length Lm The difference between the two is taken as the current estimated chromosome arm-level telomere length fraction chArmTL ; The method further comprises the following steps: Update of current estimates of chromosome arm telomere lengths TL As the total telomere reads assigned to each chromosome arm; the calculation is repeated until the telomere length of the chromosome arm TL Score chArmTL Converge to get the corresponding chromosome arm telomere length TL ;as well as, Construct a random forest model, where the score chArmTL As predicted labels for random forest models to include the frequency of duplicate reads in whole-genome sequencing data X As model input; The frequency of telomere readings of whole genome sequencing is multiplied by the ratio of the total telomere readings of 10X genome samples to the average value of the total telomere readings of each whole genome sequencing sample. The scaled whole genome sequencing telomere frequency is further quantile normalized and converted into a z score and then input into the random forest model for training to obtain the prediction score. chArmTL Random forest model.

2. A chromosome arm telomere length estimation system, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the chromosome arm telomere length estimation method according to claim 1 are implemented.

3. A method for estimating chromosome arm telomere length, characterized in that: The following steps are involved: Obtain 10X genome sequencing data; Each unmapped 10XG molecule was assigned to a mappable chromosome arm selected based on the 10X barcode; Read the corresponding 10X barcodes of all assigned 10XG molecules, read reads from 10XG molecules with the same 10X barcode, sum the reads and then divide their probabilities proportionally to the current estimated chromosome arm-level telomere length fraction chArmTL ; Repeat the calculation until the chromosome arm telomere length fraction chArmTL Converge to get the corresponding chromosome arm telomere length TL ;in, The step of assigning each unmapped 10XG molecule to a mappable chromosome arm selected according to the 10X barcode comprises: Calculate the probability of being assigned to any chromosome arm, and take the corresponding chromosome arm when the probability is greater than a preset threshold as a mappable chromosome arm; Among them, the probability of being assigned to any chromosome arm includes the estimated telomere length of the chromosome arm accounted for the sum of the estimated telomere lengths of chromosome arms selected by the same barcode; The method further comprises the following steps: Update of current estimates of chromosome arm telomere lengths TL As the total telomere reads assigned to each chromosome arm; the calculation is repeated until the telomere length of the chromosome arm TL Score chArmTL Converge to get the corresponding chromosome arm telomere length TL ;as well as, Construct a random forest model, where the score chArmTL As predicted labels for random forest models to include the frequency of duplicate reads in whole-genome sequencing data X As model input; The frequency of telomere readings of whole genome sequencing is multiplied by the ratio of the total telomere readings of 10X genome samples to the average value of the total telomere readings of each whole genome sequencing sample. The scaled whole genome sequencing telomere frequency is further quantile normalized and converted into a z score and then input into the random forest model for training to obtain the prediction score. chArmTL Random forest model.

4. A chromosome arm telomere length estimation system, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the chromosome arm telomere length estimation method according to claim 3 are implemented.

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