Probe design method and apparatus therefor

Abstract

The present disclosure provides a method for designing probes and an apparatus thereof. The method for designing probes of the present disclosure comprises: arranging one or more target regions on a genome into a bed file; obtaining a genome sequence corresponding to each target region; obtaining a k-mer sequence set based on the sequence of the genome and the target region respectively; obtaining a probe data set based on the sequence of the target region; calculating the Ud value of each probe in the probe data set, and determining the target probe for the target region according to the Ud value; and screening the homology of the probe and the genome, and further screening the probe data set.

Description

Technical Field

[0001] This invention relates to the field of molecular biology detection, and more specifically to a device and method for probe design based on dynamic selection. Background Technology

[0002] Nucleic acid hybridization is one of the fundamental techniques commonly used in molecular biology. Under ideal experimental conditions, nucleic acid hybridization can be modified into a highly efficient and sensitive method for detecting target molecules. For example, the classic PCR method can amplify a single copy of a target molecule sequence from a background of millions of non-target sequences.

[0003] DNA microarrays are another nucleic acid hybridization technique. Because they allow for high-throughput monitoring of the expression of all genes, DNA microarrays have rapidly gained popularity in biology and medicine. The specificity of nucleic acid hybridization depends on the hybridization of primers (or probes) with the target fragment. Non-specific hybridization wastes valuable data, increasing experimental costs, and can also lead researchers to incorrect conclusions.

[0004] Therefore, avoiding non-specific hybridization is crucial and also the most challenging aspect of developing chips based on nucleic acid molecular hybridization. To achieve this, some reference rules for nucleotide probe design already exist, such as probe length, GC content, probe-target fragment binding stability, and probe secondary structure.

[0005] The human genome is extremely complex, with coding regions comprising less than 2% of the genome. The remaining 98% consists of numerous repetitive sequences. These non-coding regions have simple sequence structures and multiple copies throughout the genome. If the selected target region includes these non-coding regions, the designed probe will struggle to avoid non-specific hybridization. Therefore, a novel probe design method is urgently needed. Summary of the Invention

[0006] To address one of the aforementioned technical problems in the prior art, this disclosure provides a novel probe design method and an apparatus for implementing the method.

[0007] According to one aspect of this disclosure, a method for designing probes is provided, the method comprising the following steps:

[0008] (1) One or more target regions on the genome will be organized into bed files. Each target region's bed file includes the chromosome number where the target region is located, the start position of the target region, and the end position of the target region.

[0009] (2) Obtain the genome sequence corresponding to each target region;

[0010] (3) Obtain a first k1-mer sequence set based on the sequence of the genome and count the number of the first k1-mer sequences; obtain a second k1-mer sequence set based on the sequence of one or more of the target regions and count the frequency of each sequence in the second k1-mer sequence set on the genome, wherein k1 is selected from an integer from 10 to 20;

[0011] (4) Obtain a set of k2-mer probes based on the sequences of multiple target regions, where k2 is the length of the probe;

[0012] (5) Calculate the Ud value of each probe in the k2-mer probe set, and sort the probes in each target region according to the Ud value from largest to smallest, and determine the probe with the largest Ud value as the target probe for that target region;

[0013] (6) Repeat step (5) n times to select n probes for each target area;

[0014] (7) Calculate the homology between the obtained probe and the genome sequence, obtain the number of times that the homology between each probe and the genome sequence is ≥75%, and select probes with the number of homology ≤5 to generate a probe data set.

[0015] The method disclosed herein allows for the design of an optimal probe dataset capable of capturing the target region, maximizing the specificity of probe capture. The method described above can be used to design probes for a given target region of arbitrary size.

[0016] The probes obtained by the methods disclosed herein can be used for probe design in DNA microarrays (e.g., gene chips).

[0017] According to another aspect of this disclosure, a DNA microarray is provided having probes obtained by the methods of this disclosure.

[0018] According to another aspect of this disclosure, an apparatus for designing probes is provided, the apparatus comprising: a BED file generation unit for organizing one or more target regions on a genome into BED files, each target region's BED file including the chromosome number where the target region is located, the start position of the target region, and the end position of the target region; a genome sequence acquisition unit for acquiring the genome sequence corresponding to each target region; and a first k-mer sequence generation unit for obtaining a first k1-mer sequence set based on the sequences of multiple genomes and counting the number of k1-mer sequences in the first k1-mer sequence set, obtaining a second k1-mer sequence set based on the sequences of multiple target regions and counting the number of k1-mer sequences in the second k1-mer sequence set. The system comprises: a frequency of occurrence on the genome, where k1 is an integer from 10 to 20; a second k-mer sequence generation unit, used to obtain a k2-mer probe dataset based on sequences of multiple target regions, where k2 is the probe length; a probe selection unit, used to calculate the Ud value of each probe in the k2-mer probe dataset, and sort the probes for each target region in the k2-mer probe dataset according to the Ud value from largest to smallest, and determine the probe with the largest Ud value as the target probe for that target region, repeating n times to select n probes for each target region; and a homology calculation unit, used to calculate the homology between the obtained probes and the genome sequence, obtain the number of times each probe has a homology ≥75% with the genome sequence, and select probes with a homology ≤5 times to generate a probe dataset.

[0019] According to another aspect of the invention, an apparatus is provided, comprising a memory for storing a program; and a processor for implementing the method of the present disclosure by executing the program stored in the memory.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a program is stored, the program being executable to implement the method of the present disclosure. Attached Figure Description

[0021] Figure 1 A flowchart of a probe design method according to one embodiment of the present disclosure is shown.

[0022] Figure 2 A schematic diagram of a probe design apparatus according to one embodiment of the present disclosure is shown.

[0023] Figure 3 The results of the capture efficiency of a probe designed according to one embodiment of the present disclosure are shown.

[0024] Figure 4The results show the coverage uniformity of the target region of a probe designed according to one embodiment of the present disclosure. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention in any way. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of this disclosure. Such structures and techniques have also been described in many publications.

[0026] Figure 1 A flowchart of the probe design method of this disclosure is shown.

[0027] 101. One or more target regions on the genome will be organized into BED files. Each BED file for a target region includes the chromosome number where the target region is located, the start position of the target region, and the end position of the target region.

[0028] The method disclosed herein allows for the design of an optimal probe dataset capable of capturing the target region, maximizing the specificity of probe capture. The method described above can be used to design probes for a given target region of arbitrary size.

[0029] In some implementations, in step (1), if the distance between two target regions is ≤50bp, the two target regions can be merged into one target region.

[0030] In some implementations, a line in the BED file represents a target region. In the methods of this disclosure, there is no upper limit on the number of target regions.

[0031] 102. Obtain the genome sequence corresponding to each target region.

[0032] In some embodiments, in step (2), the genome sequence corresponding to an extension of 10 to 30 bp on each side of each target region can be obtained from the genome file. To better capture the target region, the probe coverage area is generally larger than the target region. In the method of this disclosure, the extended genome sequence is obtained by extending a certain distance (i.e., the start and end positions) on each side of each target region, thereby covering a larger area than the target region. In some embodiments, the genome sequence corresponding to an extension of 15 to 25 bp on each side of each target region, such as 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 bp, can be obtained. In a preferred embodiment, k1 and k2 are both 15. The genome sequence corresponding to an extension of 20 bp on each side of each target region can be obtained.

[0033] 103. Obtain a first k1-mer sequence set based on the genome sequence and count the number of the first k1-mer sequences; obtain a second k1-mer sequence set based on the sequences of one or more of the target regions and count the frequency of each sequence in the second k1-mer sequence set on the genome, wherein k1 is selected from an integer from 10 to 20.

[0034] In some embodiments, in step (3), the information of the first k1-mer sequence set and the second k1-mer sequence set can be saved to a dictionary. In some embodiments, in step (3), the information of the first k1-mer sequence set and the second k1-mer sequence set can be saved to a binary file.

[0035] In some implementations, k1 is selected from an integer from 10 to 20. For example, k1 can be 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20. In a preferred implementation, k1 can be 15.

[0036] In some embodiments, in step (3), the genome sequence is traversed to obtain the first k1-mer sequence set with a step size of 1 bp. In some embodiments, in step (3), the sequences of one or more target regions are traversed to obtain the second k1-mer sequence set with a step size of 1 bp.

[0037] In some implementations, the first k1-mer sequence set and the second k1-mer sequence set are obtained using Jellyfish software.

[0038] In some implementations, in step (3), the frequency of each sequence in the second k1-mer sequence set appearing on the genome is counted. That is, each sequence in the second k1-mer sequence set is compared with the string in the first k1-mer sequence set, and the frequency is incremented by 1 for each instance of complete agreement. When the frequency of a sequence appearing on the genome is 1, it means that the sequence appears only once in the genome, that is, the sequence is specifically present in the genome.

[0039] The method disclosed herein can significantly shorten probe design time by saving all first k1-mer and second k1-mer sequences of the genome and target region at once.

[0040] 104. Obtain a set of k2-mer probes based on sequences of multiple target regions, where k2 is the length of the probe.

[0041] In some embodiments, in step (4), sequences of multiple target regions are traversed to obtain a probe data set of a specified length (k2) with a step size of 1 bp (nt). In some embodiments, the probe length (k2) can be in the range of 25 to 200 nt. In some embodiments, the probe length (k2) can be in the range of 25 to 150 nt. In a specific embodiment, the probe length (k2) can be 120 nt.

[0042] In some implementations, the value of k1 is less than that of k2.

[0043] In some implementations, in step (4), each target region may obtain a dataset containing multiple probes.

[0044] 105. Screening target probes. Specifically, the Ud value of each probe in the k2-mer probe set is calculated, and for each target region, the probes are sorted from largest to smallest according to their Ud values. The probe with the largest Ud value is determined as the target probe for that target region.

[0045] In some implementations, in step (5), the Ud value is equal to the number of probe-specific k1-mer sequences divided by the probe length (k2). A larger Ud value indicates better specificity of the probe.

[0046] In some implementations, the number of probe-specific k1-mer sequences can be obtained by the following steps: traversing the probes in the k2-mer probe dataset and obtaining a third k1-mer sequence set with a step size of 1 bp; aligning the sequences in the third k1-mer sequence set with the sequences in the second k1-mer sequence set; and incrementing the number of probe-specific k1-mer sequences by 1 when the alignment result is 100% consistent and the frequency of the corresponding sequence in the second k1-mer sequence set is 1.

[0047] Taking one probe from the k2-mer probe dataset as an example, the calculation process of the probe's Ud value is further described. First, the k1-mer sequence set of the probe is obtained with a step size of 1 bp, which includes multiple k1-mer sequences. Then, each sequence in the probe's k1-mer sequence set is compared with the second k1-mer sequence set. When a sequence in the probe's k1-mer sequence set is completely identical (i.e., 100% identical) to a sequence in the second k1-mer sequence set and the frequency of that sequence is 1, the numerator of the probe's Ud value is incremented by 1 (i.e., the number of probe-specific k1-mer sequences + 1). When all k1-mer sequences in the probe's k1-mer sequence set have been aligned, the probe's Ud value is obtained. In other words, the numerator of the probe's Ud value is the number of sequence-specific k1-mer sequences in the probe's k1-mer sequence set divided by the probe's length.

[0048] In some implementations, step (5) may also include calculating the values ​​of Ua, Hairpin, Dimer, and / or GC55 for each probe.

[0049] In some implementations, a mean absolute error (MAE) model is used to calculate the Ua value for each probe. In a specific implementation, the Ua value for each probe can be obtained through the following steps: a) calculating the theoretical spacing distance when the specific k1-mer sequence is uniformly distributed on the probe; b) calculating the actual spacing distance of the k1-mer sequence on the probe; c) calculating the mean absolute error between the actual spacing distance and the theoretical spacing distance; d) dividing the mean absolute error obtained in step c) by the probe length to obtain the Ua value. In some implementations, the closer the Ua value is to 0, the more uniform the distribution of the specific k1-mer sequence on the probe, and the better the specificity of the probe.

[0050] In some implementations, the hairpin and dimer values ​​for each probe are calculated using methods conventional in the art. For example, software such as Primer3 or Lasergene can be used to calculate the hairpin and dimer values ​​for each probe.

[0051] In some implementations, the GC55 value of each probe represents the absolute difference between the probe's GC content and 55%. The smaller the GC55 value, the closer the probe's GC content is to 55%. Probes with a GC content of 55% are generally considered in the art to have the best hybridization performance.

[0052] In some implementations, in step (5), multiple probes for each target region are sorted according to their Ud values ​​(from largest to smallest), Ua values ​​(from smallest to largest), Hairpin values ​​(from smallest to largest), and / or Dimer values ​​(from smallest to largest), and the probe ranked first is selected as the optimal probe. In some implementations, in step (5), each probe is sorted according to its Ud value (from largest to smallest), Ua value (from smallest to largest), Hairpin value (from smallest to largest), and / or Dimer value (from smallest to largest), and the probe ranked first is selected as the optimal probe. Through step (5), the probes obtained after screening using these parameters have high specificity and fewer secondary structure pairings, thereby minimizing binding to non-target regions.

[0053] In some implementations, in step (5), each probe is sorted in descending order of priority: Ud value, Ua value, Hairpin value, and Dimer value. For example, if two probes have the same Ud value, their Ua values ​​are compared; if their Ua values ​​are still the same, their Hairpin values ​​are compared; if they are still the same, their Dimer values ​​are compared, and so on.

[0054] 106. Repeat step 105 n times to select n probes for each target region.

[0055] In some implementations, in step (6), the screening process of step (5) is repeated until a set of n probes covering the entire target area is obtained. In this set, the n probes overlap at their ends in pairs, with an overlap length between 5 and 20 bp, for example, an overlap of 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 bp.

[0056] Taking the 3' end position of the first probe, overlapping by 10 bp as an example, the steps to obtain the second probe are described. Based on the 3' end of the first probe, traverse 10 probes within a range of 10 bp (-10 bp) upstream of the 3' end of the first probe with a step size of 1 bp. Repeat the screening process of step (5) to obtain the optimal second probe from these 10 probes. Repeat step (5) in this way to obtain n probes covering the target area.

[0057] The conventional planar probe strategy involves directly connecting each probe to the previous one. A significant drawback of this design is that once the position of the first probe is determined, the position of all subsequent probes is also fixed, leaving no room for selection. In reality, probes differing by only a few base pairs may not show significant differences in thermodynamic and kinetic hybridization performance. The method disclosed in this paper allows for selection within a certain overlap range. This slightly improves the performance of each probe, resulting in a substantial overall improvement in hybridization capture performance for probe arrays that often contain hundreds, thousands, or even tens of thousands of probes.

[0058] 107. Calculate the homology between the obtained probe and the genome sequence, obtain the number of times each probe has a homology ≥75% with the genome sequence, and select probes with a homology ≤5 to generate a probe dataset.

[0059] In some embodiments, in step 107, the homology between the probes obtained in steps 105 and 106 and the genome sequence is calculated, and the number of times each probe has a homology ≥75% with the genome sequence is obtained. Probes with a homology ≤5 are selected to generate a probe dataset. In some embodiments, in step 107, probes with a homology ≤4 are selected to generate a probe dataset. In some embodiments, in step 107, probes with a homology ≤3 are selected to generate a probe dataset. In some embodiments, in step 107, probes with a homology ≤2 are selected to generate a probe dataset. In some embodiments, in step 107, probes with a homology ≤1 are selected to generate a probe dataset.

[0060] In some implementations, homology can be described as percentage identity (% identity) between two sequences.

[0061] In a preferred embodiment, before or after step 107, the method may further include the following steps: comparing the sequence of the probe obtained in steps 105 and 106 with the sequence of a non-target region on the genome; if the sequence of the probe does not have 15 or more consecutive identical nucleotides with the sequence of the non-target region, the probe is retained.

[0062] The selected target region (especially regions containing introns) may contain homologous regions on the genome. Therefore, probes obtained through steps 105 and 106 may exhibit homology with the genome, necessitating probe removal based on a homology threshold. In some implementations, the homology threshold can be determined according to specific circumstances. If the region is important, all homologous probes can be retained, but this carries the risk of reduced capture efficiency. If the region is relatively dispensable, it is recommended to remove all homologous probes to ensure a high proportion of effective data.

[0063] Figure 2 An exemplary device 201 for a design probe of this disclosure is shown.

[0064] The bed file generation unit 202 is used to organize one or more target regions on the genome into bed files. Each target region's bed file includes the chromosome number where the target region is located, the start position of the target region, and the end position of the target region.

[0065] In some implementations, in the bed file generation unit 202, if the distance between two target regions is less than 50bp, the two target regions can be merged into one target region.

[0066] In some implementations, one line in the bed file represents one target region. In the methods of this disclosure, there is no upper limit to the number of target regions in the bed file.

[0067] The genome sequence acquisition unit 203 is used to acquire the genome sequence corresponding to each target region.

[0068] In some embodiments, the genome sequence acquisition unit 203 can acquire the genome sequence corresponding to 10 to 30 bp extensions on both sides of each target region. To better capture the target region, the probe coverage area is generally larger than the target region. In a preferred embodiment, the BED file generation unit 203 can acquire the genome sequence corresponding to 20 bp extensions on both sides of each target region.

[0069] The first k-mer sequence generation unit 204 is used to obtain a first k1-mer sequence set based on sequences of multiple genomes and count the number of k1-mer sequences in the first k1-mer sequence set, and to obtain a second k1-mer sequence set based on sequences of multiple target regions and count the frequency of each k1-mer sequence in the second k1-mer sequence set on the genome, wherein k1 is an integer from 10 to 20.

[0070] In some embodiments, the first k-mer sequence generation unit 204 can save the information of the first k1-mer sequence set and the second k1-mer sequence set into a dictionary. In some embodiments, the first k-mer sequence generation unit 204 can save the information of the first k1-mer sequence set and the second k1-mer sequence set into a binary file.

[0071] In some embodiments, k1 is selected from integers from 10 to 30. In some embodiments, k1 is selected from integers from 10 to 20. In a preferred embodiment, k1 can be 15.

[0072] The second k-mer sequence generation unit 205 is used to obtain a k2-mer probe dataset based on sequences from multiple target regions, where k2 is the length of the probe.

[0073] In some embodiments, the second k-mer sequence generation unit 205 can generate sequences from one or more of the target regions into a specified probe length (k2) with a step size of 1 bp. In some embodiments, the probe length (k2) can be in the range of 25 to 200 nt. In a preferred embodiment, the probe length (k2) can be in the range of 25 to 150 nt. In a specific embodiment, the probe length (k2) can be 120 nt.

[0074] The probe filtering unit 206 is used to calculate the Ud value of each probe in the k2-mer probe dataset, and sort the probes in the k2-mer probe dataset for each target region according to the Ud value from largest to smallest. The probe with the largest Ud value is determined as the target probe for that target region. This process is repeated n times to filter out n probes for each target region.

[0075] In some embodiments, the probe screening unit 206 may include a Ud value calculation module. In some embodiments, the probe screening unit may also include a Ua value calculation module, a Hairpin value calculation module, a Dimer value calculation module, and / or a GC55 value calculation module.

[0076] In some embodiments, the probe selection unit 206 may further sort each probe according to its Ud value (from largest to smallest), Ua value (from smallest to largest), Hairpin value (from smallest to largest), and / or Dimer value (from smallest to largest), and select the probe ranked first as the optimal probe.

[0077] In some embodiments, when repeating the screening step, the probe screening unit 206 traverses the k2-mer probe data set with a step size of 1 bp based on a determined end position of the target probe, calculates the Ud value of each probe in the probe data set that overlaps with the end position of the determined target probe by 5 to 20 bp, and determines the probe with the largest Ud value as the target probe.

[0078] In some embodiments, n is selected from an integer from 1 to 200. In a preferred embodiment, n is selected from an integer from 25 to 200. In a specific embodiment, n is the same as the length of the probe.

[0079] The homology calculation unit 207 is used to calculate the homology between the obtained probe and the genome sequence, obtain the number of times that the homology between each probe and the genome sequence is ≥75%, and select probes with the number of homology ≤5 to generate a probe data set.

[0080] In some implementations, the homology calculation unit can calculate the homology between the probes obtained by the probe screening unit and the genome sequence, obtain the number of times each probe has a homology of ≥75% with the genome sequence, and select probes with a homology of ≤5 times to generate a probe data set.

[0081] In some embodiments, the apparatus may further include a third k-mer sequence generation unit for obtaining a third k1-mer sequence set by taking probes from the k2-mer probe data set in a step size of 1 bp.

[0082] In some implementations, the Ud value is equal to the number of probe-specific k1-mers divided by the probe length. A higher Ud value indicates better probe specificity.

[0083] In a preferred embodiment, the method may further include an alignment unit for aligning the sequence of the probe obtained by the probe screening unit with the sequence of a non-target region on the genome. If the sequence of the probe does not have 15 or more consecutive identical nucleotides with the sequence of the non-target region, the probe is retained.

[0084] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly used in the field to which this invention pertains. For the purposes of interpreting this specification, the following definitions will apply, and where appropriate, terms used in the singular will also include the plural forms, and vice versa.

[0085] Unless the context clearly indicates otherwise, the terms “a” and “an” as used herein include plural references. For example, reference to “a cell” includes multiple such cells and equivalents known to those skilled in the art, etc.

[0086] As used herein, the term "about" indicates a range of ±20% of the following value. In some embodiments, the term "about" indicates a range of ±10% of the following value. In some embodiments, the term "about" indicates a range of ±5% of the following value.

[0087] The term "k-mer" used in this article refers to a nucleic acid fragment of length k. For example, if a nucleic acid sequence is broken into 15 bp fragments, the resulting 15 bp fragment is called a 15-mer; if a nucleic acid sequence is broken into 120 bp fragments, the resulting fragment is called a 120-mer.

[0088] As used in this article, the term "nt" refers to the basic unit of nucleic acids, the nucleotide. For example, 120nt refers to a single-stranded nucleic acid with 120 nucleotides. As used in this article, the term "bp" refers to a pair of nucleotides, that is, a base pair in which one nucleotide is non-covalently bonded to the other nucleotide (e.g., via hydrogen bonds).

[0089] The "homology" or "percentage identity (% identity)" between two sequences can be determined using techniques known in the art. Some methods for determining identity are coded in publicly available computer programs. Sequence alignment and percentage identity calculations can be performed using, for example... The AlignX program of the software package (Invitrogen, Carlsbad, CA) or the MegAlign of the LASERGENE bioinformatics computing software package (DNASTAR Inc., Madison, WI) TM The program proceeds.

[0090] Furthermore, the determination of the percentage identity between two sequences can also be made using mathematical algorithms known to those skilled in the art. A non-limiting example of a mathematical algorithm for comparing two sequences is the algorithm of Karlin and Altschul (1990), Proceedings of the National Academy of Sciences (Proc. Natl. Acad. Sci. USA, 87: 2264-2268), modified by Karlin and Altschul (1993), Proceedings of the National Academy of Sciences (90: 5873-5877). Such an algorithm has been incorporated into the NBLAST and XBLAST procedures of Altschul et al. (1990), Journal of Molecular Biology (J. Mol. Biol.) 215: 403-410. BLAST nucleotide searches can be performed using the NBLAST procedure, for example, score = 100, word length = 12, to obtain nucleotide sequences homologous to the nucleotide sequences of this disclosure. To obtain gap alignments for comparative purposes, Gapped BLAST can be used as described in Altschul et al. (1997) Nucleic Acids Res. 25:3389-3402. Alternatively, PSI-Blast can be used to perform iterative searches that detect distance relationships between molecules. When using the BLAST, GappedBLAST, and PSI-Blast procedures, the default parameters of the respective procedures (e.g., XBLAST and NBLAST) can be used. See http: / / www.ncbi.nlm.nih.gov. As used herein, the term “genome” refers to a complete set of genetic material (DNA or RNA) within a cell of a particular species or within a viral particle. A genome includes genes (coding regions) and non-coding regions (ncDNA or ncRNA), and may also include mitochondrial DNA and / or chloroplast DNA. In some embodiments, the genome specifically refers to the whole human genome sequence.

[0091] As used in this article, the term "probe" refers to a nucleotide sequence that can selectively hybridize with a corresponding nucleotide sequence present in a genomic DNA or cDNA fragment (e.g., a genome or cDNA library) from a selected sample.

[0092] The terms “GC” or “%GC” used in this article are used interchangeably and refer to the percentage of guanine and cytosine in a DNA sequence out of all nucleotides in that DNA sequence.

[0093] The term "hairpin structure" as used in this article refers to a nucleic acid having a double-stranded "stem" region and a single-stranded "loop" region, which is formed by a single-stranded nucleic acid having two essentially complementary regions that form the stem region; and a third region between the two regions that forms the loop.

[0094] The following embodiments and accompanying drawings are provided to aid in understanding the present invention. However, it should be understood that these embodiments and drawings are for illustrative purposes only and do not constitute any limitation. The actual scope of protection of the present invention is set forth in the claims. It should be understood that any modifications and changes can be made without departing from the spirit of the present invention.

[0095] To verify the performance of the probe design method of this disclosure, probes targeting 22 genes (including 3 full-length CDS and partial exons) were designed according to the following steps.

[0096] The nucleic acid sequences of these 22 genes were compiled into a BED file. Each line in the BED file displays one gene. The BED file includes the chromosome number of each gene, the start position of the target region, and the end position of the target region.

[0097] Based on the above information for the 22 genes, the genome sequence corresponding to each gene was obtained.

[0098] Using Jellyfish software, the genome sequence was traversed, and a first set of 15-mer sequences was obtained with a step size of 1 bp, and the number of 15-mer sequences was counted. Additionally, using Jellyfish software, the sequences of 22 genes were traversed, and a second set of 15-mer sequences was obtained with a step size of 1 bp, and the frequency of each 15-mer sequence in this set on the genome was counted.

[0099] Using Jellyfish software, a 120-mer probe dataset was obtained by iterating through the sequences of 22 genes. For each probe in the dataset, a 15-mer sequence set was generated using a 1bp step size. For each probe, its 15-mer sequence was compared with strings in the second 15-mer sequence set. When a string with a completely identical sequence was found, if the frequency of that sequence was 1, the numerator of the probe's Ud value was incremented by 1. This method yielded the Ud value (Ud value = number of specific 120-mers for the probe / 120) for each probe in the 120-mer probe dataset of these 22 genes.

[0100] In addition, Ua was calculated using the mean absolute error model. First, the theoretical distance for uniform distribution of the specific 15-mer sequence on the probe was calculated. Then, the actual spacing distance was calculated. Next, the mean absolute error between the actual and theoretical distances was calculated, and finally, this was divided by the probe length to obtain Ua. A Ua value closer to 0 indicates a more uniform distribution of the specific 15-mer sequence on the probe, representing better probe specificity. The hairpin and dimer values ​​of the probe were calculated using Primer3 software. Furthermore, the GC55 of each probe was calculated using conventional methods.

[0101] The probes are sorted by Ud value from largest to smallest, Ua value from smallest to largest, Hairpin value from smallest to largest, and / or Dimer value from smallest to largest, and the probe ranked first is selected as the target probe for each gene.

[0102] Determine the range of available probes. For example, the available range for the first probe is 20bp, and the available range for the 1X probe is 10bp. Sort the probes according to their various metrics, from largest to smallest Ud, from smallest to largest Ua, from smallest to largest Hairpin, from smallest to largest Dimer, and from smallest to largest GC55. The probe ranked first is the one with the best overall performance.

[0103] For the designed probes, the BLAST tool was used to calculate the number of times each probe appeared across the entire genome, based on a 75% homology (-perc_identify 0.75) ratio. Then, probes with a 75% homology ratio of more than 3 occurrences were removed, resulting in the final probe dataset. The obtained probe dataset is shown in Table 1 below.

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] Table 1 shows a total of 237 probes obtained, with a total size of 20 kb. The target region was provided to an external probe supplier for probe design and synthesis. Capture experiments were then conducted under identical conditions to compare the capture performance of the two probe groups. The results are shown in Table 1. Figure 3 and Figure 4 middle.

[0117] KBD is a probe designed according to the method of this disclosure (Table 1), and KBR is an externally ordered probe. The experiment was repeated twice; in terms of capture efficiency, the KBD probe was approximately 7% higher than the KBR probe. Figure 3 In terms of target region coverage uniformity, the KBD probe performed similarly to the KBR probe at 0.2mean and 0.5mean, showing no significant difference. The results indicate that the KBD probe binds better to the target region and captures more effective data. This is particularly beneficial for clinical gene testing panels with limited data capacity, and it is also more competitive in detecting mutations, SVs, and CNVs. Therefore, theoretically, the probe design software disclosed herein can be used in any scenario requiring the design of human probe sets.

[0118] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A method for designing probes, comprising the following steps: (1) One or more target regions on the genome will be organized into BED files. Each BED file for a target region includes the chromosome number where the target region is located, the start position of the target region, and the end position of the target region. (2) Obtain the genome sequence corresponding to each target region; (3) Obtain a first k1-mer sequence set based on the sequence of the genome and count the number of the first k1-mer sequences; obtain a second k1-mer sequence set based on the sequence of one or more of the target regions and count the frequency of each sequence in the second k1-mer sequence set on the genome, wherein k1 is selected from an integer from 10 to 20; (4) Obtain a k2-mer probe dataset based on sequences from one or more of the target regions, where k2 is the probe length; (5) Calculate the Ud value of each probe in the k2-mer probe dataset, and sort the probes in each target region according to the Ud value from largest to smallest, and determine the probe with the largest Ud value as the target probe for that target region; (6) Repeat step (5) n times to select n probes for each target area; (7) Calculate the homology between the obtained probe and the genome sequence, obtain the number of times each probe has a homology ≥75% with the genome sequence, and select probes with a homology ≤5 to generate a probe data set; in, In step (5), the probe sets in the k2-mer probe dataset are traversed to obtain the third k1-mer sequence set, where the Ud value = the number of probe-specific k1-mer sequences / probe length. The number of probe-specific k1-mer sequences is obtained through the following steps: traversing the probes in the k2-mer probe dataset and obtaining a third k1-mer sequence set with a step size of 1 bp; aligning the sequences in the third k1-mer sequence set with the sequences in the second k1-mer sequence set; when the alignment result is 100% consistent and the frequency of the corresponding sequence in the second k1-mer sequence set is 1, the number of probe-specific k1-mer sequences is incremented by 1.

2. The method according to claim 1, characterized in that, In step (1), if the distance between two target regions is ≤50 bp, then the two target regions are merged into one target region.

3. The method according to claim 1, characterized in that, In step (2), the genome sequence corresponding to each target region extended 10 to 30 bp on both sides is obtained from the genome file.

4. The method according to claim 1, characterized in that, In step (2), the genome sequence corresponding to each target region extended 20 bp on both sides is obtained from the genome file.

5. The method according to claim 1, characterized in that, In step (3), the information of the first k1-mer sequence set and the second k1-mer sequence set is saved into a dictionary.

6. The method according to claim 1, characterized in that, Save the information of the first k1-mer sequence set and the second k1-mer sequence set to a binary file.

7. The method according to claim 1, characterized in that, k1 is 15.

8. The method according to claim 1, characterized in that, In step (4), the sequence of one or more target regions is traversed and the probe is made to the specified length in steps of 1 bp.

9. The method according to claim 8, characterized in that, The length of the probe is in the range of 25 to 200 nt.

10. The method according to claim 8, characterized in that, Each target region yields a dataset containing multiple probes.

11. The method according to claim 1, characterized in that, Step (5) also includes calculating the values ​​of Ua, hairpin structure, dimer, and / or GC55 for each probe, and for each probe in the target region, sorting them according to Ud value from largest to smallest, Ua value from smallest to largest, hairpin structure value from smallest to largest, and / or dimer value from smallest to largest, and selecting the probe ranked first as the target probe. The Ua value of each probe is calculated using the mean absolute error model. The Ua value of each probe is obtained through the following steps: a) calculating the theoretical spacing distance when the specific k2-mer sequence is uniformly distributed on the probe; b) calculating the actual spacing distance of the k2-mer sequence on the probe; c) calculating the mean absolute error between the actual spacing distance and the theoretical spacing distance; d) dividing the mean absolute error obtained in step c) by the length of the probe to obtain the Ua value.

12. The method according to claim 1, characterized in that, In step (6), for each target region, among the n probes, the ends of two adjacent probes overlap each other.

13. The method according to claim 12, characterized in that, For each target region, among the n probes, the ends of two adjacent probes overlap by 5~20 bp.

14. The method according to claim 1, characterized in that, In step (7), the homology between the probes obtained in steps (5) and (6) and the sequence of the genome is calculated, and the number of times each probe has a homology of ≥75% with the sequence of the genome is obtained. Probes with a homology of ≤5 times are selected to generate a probe data set.

15. The method according to claim 1, characterized in that, Before or after step (7), the method further includes the following steps: comparing the sequence of the probe obtained in steps (5) and (6) with the sequence of a non-target region on the genome, and retaining the probe if the sequence of the probe does not have 15 or more consecutive identical sequences with the sequence of the non-target region.

16. A DNA microarray, characterized in that, The DNA microarray has probes obtained by the method of any one of claims 1 to 15.

17. An apparatus for designing probes, characterized in that, The device includes: The bed file generation unit is used to organize one or more target regions on the genome into bed files. Each target region's bed file includes the chromosome number where the target region is located, the start position of the target region, and the end position of the target region. The genome sequence acquisition unit is used to acquire the genome sequence corresponding to each target region; The first k-mer sequence generation unit is used to obtain a first k1-mer sequence set based on sequences from multiple genomes and count the number of k1-mer sequences, obtain a second k1-mer sequence set based on sequences from one or more target regions, and count the frequency of each sequence in the second k1-mer sequence set on the genome, where k1 is an integer from 10 to 20. The second k-mer sequence generation unit is used to obtain a set of k2-mer probes based on sequences from one or more of the target regions, where k2 is the length of the probe; The probe selection unit calculates the Ud value of each probe in the k2-mer probe dataset, and sorts the probes for each target region in descending order of their Ud values. The probe with the largest Ud value is selected as the target probe for that target region. This process is repeated n times to select n probes for each target region. The homology calculation unit is used to calculate the homology between the obtained probes and the genome sequence, obtain the number of times each probe has a homology ≥75% with the genome sequence, and select probes with a homology ≤5 to generate a probe dataset. The probe screening unit is used to traverse the probes in the k2-mer probe dataset to obtain a third k1-mer sequence set. The probe screening unit also includes a Ud value calculation module, where the Ud value = the number of probe-specific k1-mer sequences / probe length. The number of probe-specific k1-mer sequences is obtained through the following steps: traversing the probes in the k2-mer probe dataset and obtaining a third k1-mer sequence set with a step size of 1 bp; aligning the sequences in the third k1-mer sequence set with the sequences in the second k1-mer sequence set; when the alignment result is 100% consistent and the frequency of the corresponding sequence in the second k1-mer sequence set is 1, the number of probe-specific k1-mer sequences is incremented by 1.

18. The apparatus according to claim 17, characterized in that, In the BED file generation unit, if the distance between two target regions is less than 50 bp, the two target regions are merged into one target region.

19. The apparatus according to claim 17, characterized in that, The genome sequence acquisition unit obtains the genome sequence corresponding to each target region extended 10 to 30 bp on both sides from the genome file.

20. The apparatus according to claim 19, characterized in that, Obtain the genome sequence corresponding to each target region by extending 15 to 25 bp on both sides from the genome file.

21. The apparatus according to claim 17, characterized in that, The first k-mer sequence generation unit saves the information of the first k1-mer sequence set and the second k1-mer sequence set into a dictionary.

22. The apparatus according to claim 17, characterized in that, Save the information of the first k1-mer sequence set and the second k1-mer sequence set to a binary file.

23. The apparatus according to claim 17, characterized in that, k1 is 15.

24. The apparatus according to claim 17, characterized in that, The second k-mer sequence generation unit traverses the sequences of one or more of the target regions and generates probes of the specified length in steps of 1 bp.

25. The apparatus according to claim 24, characterized in that, The length of the probe is in the range of 25 to 200 nt.

26. The apparatus according to claim 24, characterized in that, Each target region yields a dataset containing multiple probes.

27. The apparatus according to claim 17, characterized in that, The probe screening unit also includes a Ua value calculation module, a hairpin structure calculation module, a dimer value calculation module, and / or a GC55 value calculation module.

28. The apparatus according to claim 27, characterized in that, The probe screening unit further sorts each probe according to its Ua value (from smallest to largest), hairpin structure value (from smallest to largest), and / or dimer value (from smallest to largest), and selects the probe ranked first as the target probe.

29. The apparatus according to claim 17, characterized in that, When repeating the screening steps, the first probe screening unit traverses the k2-mer probe data set with a step size of 1 bp based on a determined end position of the target probe, calculates the Ud value of each probe in the probe data set that overlaps with the end position of the determined target probe by 5 to 20 bp, and determines the probe with the largest Ud value as the target probe.

30. The apparatus according to claim 17, characterized in that, The homology calculation unit is used to calculate the homology between the probes obtained in steps (5) and (6) and the genome sequence, to obtain the number of times that each probe has a homology of ≥75% with the genome sequence, and to select probes with a homology of ≤5 to generate a probe data set.

31. The apparatus according to claim 30, characterized in that, The homology calculation unit is used to calculate the homology between the probe obtained in steps (5) and (6) and the k1-mer sequences in the first k1-mer sequence set.

32. The apparatus according to claim 17, characterized in that, The device further includes an alignment unit for comparing the sequence of the probe obtained by the probe screening unit with the sequence of a non-target region on the genome. If the sequence of the probe does not have 15 or more consecutive identical nucleotides with the sequence of the non-target region, the probe is retained.

33. A device, characterized in that, The device includes: Memory, used to store programs; and, A processor for implementing the method of any one of claims 1 to 15 by executing a program stored in the memory.

34. A computer-readable storage medium, characterized in that, The medium stores a program that can be executed to implement the method of any one of claims 1 to 15.

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