Plant centromere identification and boundary defining method
By constructing a standardized bioinformatics analysis process, the centromere boundary of plants is automatically identified and defined, solving the problems of inaccurate identification and ambiguous boundaries in existing technologies. This enables efficient and reproducible plant genome analysis, applicable to a variety of plants.
Patent Information
- Application Number
- CN202511970667.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies lack standardized and automated methods to accurately identify and define plant centromere boundaries, resulting in low comparability of cross-species analysis results, blurred boundaries, and low automation of processes, which affects analytical efficiency and reproducibility.
A standardized and automated bioinformatics analysis workflow was constructed, including data quality control, alignment, signal enrichment analysis, and boundary determination. The MACS2 software was used to identify the CENH3 binding peak region, and the boundary was determined by extending the boundary with a fixed step size, outputting a standardized genome coordinate file.
It enables objective and accurate identification and boundary delineation of plant centromere regions, improves the reproducibility and comparability of analysis results, is applicable to different plant genomes, provides high-resolution analysis tools, and supports chromosome structural variation analysis and molecular breeding.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of bioinformatics technology, and in particular relates to a method for identifying and defining the centromere of plants. Background Technology
[0002] Centromeres are key functional structures on chromosomes that mediate spindle attachment and chromosome segregation. Precise centromere localization is crucial for studying chromosome behavior, genome evolution, and crop genetic improvement. Currently, centromere localization primarily relies on cytogenetic techniques (fluorescence in situ hybridization, FISH) and histone CENH3-based chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-seq).
[0003] In experimental studies, FISH technology is commonly used to verify the reliability of obtained centromere-related region sequences or to further clarify centromere localization. Currently, obtaining DNA sequences of centromere regions mainly relies on ChIP-seq sequencing data of CENH3. CENH3, as a centromere-specific histone marker, can be used in ChIP-seq technology to achieve high-throughput centromere mapping across the entire genome, providing a powerful tool for research.
[0004] However, for the bioinformatics analysis of CENH3 ChIP-seq data, there is still a lack of standardized and automated methods for accurately defining centromere boundaries. Existing analyses mostly rely on researchers' subjective experience in judging the distribution of sequencing signals, roughly delineating regions by manually observing enrichment peaks or simply setting global thresholds, which has the following significant problems: (1) Inconsistent standards: The sequence composition, length, and CENH3 signal patterns of centromeres differ significantly among different plant species, while existing methods mostly use fixed parameters and lack guidance on parameter adjustment for species-specific signal characteristics, resulting in low comparability of results when analyzing across species; (2) Blurred boundaries: Plant centromere regions are usually rich in repetitive sequences, and ChIP-seq signals decay at the boundaries, lacking objective and consistent algorithms to determine the start and end boundaries of the signals; (3) Low degree of automation: The composition of centromeres varies greatly among species, and existing analysis processes rely heavily on manual intervention and experience, making it difficult to achieve high-throughput and batch data processing, affecting analysis efficiency and reproducibility. Therefore, developing an objective, automated, high-resolution method for centromere identification and boundary delineation applicable to different plant genomes is an urgent need in current plant genomics and molecular breeding research. Summary of the Invention
[0005] To address the aforementioned technical challenges, this invention proposes a method for identifying and defining plant centromeres. By constructing a standardized and automated bioinformatics analysis workflow, it systematically processes and intelligently interprets CENH3 ChIP-seq data, achieving objective and accurate identification and boundary definition of centromere regions across the entire genome. This reduces the shortcomings of existing technologies, which rely on human experience, have ambiguous boundary determinations, and suffer from poor repeatability. It provides a high-resolution, reproducible, and widely applicable analytical tool for plant chromosome research.
[0006] To achieve the above objectives, the present invention provides a method for identifying and defining the centromere of a plant, comprising the following steps: 1) Perform quality control on the raw CENH3 ChIP-seq sequencing data to remove low-quality bases, adapter sequences, and low-complexity sequences; 2) Align the reads after quality control in step 1) to the target plant reference genome, convert the files, calculate the average coverage or ChIP signal of each bin according to the preset window, and generate a preliminary CENH3 distribution curve; 3) Use signal enrichment analysis software to identify the CENH3 binding peak region and preliminarily delineate the candidate centromere regions of each chromosome; 4) Select the segment with continuous intensity higher than the preset threshold as the core centromere, and extend it to both sides with a fixed step size until the signal intensity is lower than the preset threshold. Determine the start and end positions of the centromere boundary and output the signal.
[0007] Furthermore, in step 1), the quality control uses the FASTP software, version v0.23.4.
[0008] Furthermore, in step 2), the comparison was performed using Bowtie2 software, version v2.2.5.
[0009] Furthermore, in step 2), the file conversion is performed using samtools with a preset window size of 10kb. The preset window size is used by bedtools to calculate the average coverage or ChIP signal for each bin.
[0010] Furthermore, in step 3), the signal enrichment analysis uses the MACS2 software, version v2.2.9.1, with the following parameters: -g dm -n chip --broad --broad-cutoff 0.1.
[0011] Furthermore, in step 4), the step size is fixed at 5kb.
[0012] Furthermore, in step 4), the preset threshold is based on the average sequencing depth of each sequencing experiment, and is set to 0.8 reads per bin in diploid plants.
[0013] Further, in step 4), the output is a BED format file containing the genomic coordinates of the centromere region.
[0014] Furthermore, the plant centromere identification and boundary delineation method is applicable to diploid plants, including peppers, corn, and soybeans.
[0015] It also provides the application of plant centromere identification and boundary delineation methods in genome structure analysis and breeding research.
[0016] Compared with the prior art, the present invention has the following advantages and technical effects: 1) This invention integrates the steps from raw data quality control, comparison, signal enrichment analysis to boundary determination, and combines key software versions and parameters to construct a standardized analysis process, which greatly reduces the bias introduced by human intervention and subjective experience, improves the reproducibility and comparability of analysis results, and is conducive to data integration and comparison between different studies. 2) This invention adopts a strategy of combining core region identification with bidirectional step extension. First, the core centromere is determined based on continuous high signal. Then, it is finely extended to both sides with a fixed step size (5kb) until the signal intensity decays to below the preset threshold. This method can capture signal gradient changes more sensitively, has high boundary identification resolution, and achieves accurate boundary positioning, providing a reliable basis for chromosome structural variation analysis and molecular breeding. 3) This invention takes into account the diversity of plant genomes and sets differentiated signal intensity thresholds (e.g., 0.8 reads per bin in diploid plants) to address the systematic differences in CENH3 signal intensity in diploid plant genomes. This strategy of adjusting signal intensity thresholds based on species specificity allows the method to be flexibly applied to a wide range of species, from model plants to complex crops, thus broadening the scope of application of the technology. 4) The final output of this invention is a BED format coordinate file, which is directly compatible with mainstream genome browsers (IGV, UCSC Genome Browser) or used for bioinformatics analysis (centromere region sequence feature analysis, collinearity comparison, associated genetic markers, etc.), providing a ready-to-use and highly reliable data foundation for chromosome structural biology, genome evolution research and molecular marker-assisted breeding. Detailed Implementation
[0017] The technical solution of the present invention will be further described below through embodiments.
[0018] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
[0019] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Experimental methods in the following embodiments that do not specify specific conditions are generally determined according to national standards. Experimental instruments, equipment, and reagents in the following embodiments that do not specify their sources are all commercially available materials.
[0020] Unless otherwise defined or stated, all technical and scientific terms used in this invention have the same meaning as those skilled in the art. Furthermore, any methods and materials similar to or equivalent to those described herein can be applied to the methods of this invention. It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.
[0021] Example 1 Taking the potato DM genome as an example: The centromere origin and termination positions of each chromosome in potato DM were determined. Chromatin immunoprecipitation (CENH3 Chip-seq) data were obtained from the NCBI website, accession number PRJNA962192.
[0022] (1) FastP (v0.23.4) was used to perform quality control on the raw short reads obtained from CENH3 ChIP-seq sequencing, removing low-quality bases, adapter sequences, and low-complexity sequences. (The minimum Phred quality score threshold for a single base to be considered acceptable was set to 15 using the -q 15 parameter; bases with quality scores below this threshold were judged as low-quality. Secondly, the -n5 parameter was used to filter out sequences with more than 5 unknown bases (N) in each read, thus removing reads with excessive sequencing uncertainty. Simultaneously, the program enabled low-quality read filtering by default (-u 40), meaning that if more than 40% of the bases in a read are low-quality, the entire read will be discarded.) (2) Using Bowtie2 (v2.2.5), the reads after quality control in step (1) were aligned to the potato DM reference genome, retaining unique alignment results with an alignment rate greater than 90%; (3) Use samtools to convert files, and use bedtools to calculate the average coverage or ChIP signal of each bin with a non-overlapping window of 10 kb to generate a preliminary CENH3 signal distribution map; (4) Use MACS2 (v2.2.9.1) to identify broad peaks in ChIP-seq signals. The parameters are set to "-g dm -n chip --broad --broad-cutoff 0.1". Based on normalized enrichment, first, manually define the approximate range of the centromere of each chromosome, that is, the region where CENH3 signals are enriched at the same time. Among the parameter settings: -g dm specifies the genome size for statistical significance calculation; -n chip specifies the output file prefix for easier subsequent result management; --broad identifies broad peaks for MACS2, suitable for signals like CENH3 that cover long chromatin regions; --broad-cutoff 0.1 sets the broad peak significance threshold, i.e., regions with q-value ≤ 0.1 are considered significant.
[0023] (5) Based on the sequencing depth and ChIP signal level of each sample, a continuous region with a signal intensity greater than 0.8 reads per bin in DM was selected as the core centromere, and the core boundary of the centromere was initially determined. Then, the region was extended to both ends in a 5 kb window until the average signal intensity was lower than 0.8 reads per bin. The positions of these two ends were defined as the starting point and ending point of the centromere. (6) Output the start and end positions of the centromere regions of each chromosome and save them as a BED file. The results are shown in Table 1: Table 1. Centromere initiation and termination positions in potato DM
[0024] Example 2 Taking the chili pepper genome as an example: Selecting pepper genome as the cultivation type C. annuum The T2T gapless genome was provided by the pepper genome database of Peking University Academy of Agricultural Sciences. The chromatin immunoprecipitation (CENH3 Chip-seq) data were sourced from the NCBI website, accession number PRJNA962192.
[0025] (1) FastP (v0.23.4) was used to perform quality control on the raw short reads obtained from CENH3 ChIP-seq sequencing, removing low-quality bases, adapter sequences, and low-complexity sequences. (The minimum Phred quality score threshold for a single base to be considered acceptable was set to 15 using the -q 15 parameter; bases with quality scores below this threshold were judged as low-quality. Secondly, the -n5 parameter was used to filter out sequences with more than 5 unknown bases (N) in each read, thus removing reads with excessive sequencing uncertainty. Simultaneously, the program enabled low-quality read filtering by default (-u 40), meaning that if more than 40% of the bases in a read are low-quality, the entire read will be discarded.) (2) Using Bowtie2 (v2.2.5), the reads after quality control in step (1) were aligned to their respective high-quality reference genomes, retaining unique alignment results with an alignment rate greater than 90%; (3) Use samtools to convert files, and use bedtools to calculate the average coverage or ChIP signal of each bin with a non-overlapping window of 10 kb to generate a preliminary CENH3 signal distribution map; (4) Use MACS2 (v2.2.9.1) to identify broad peaks in ChIP-seq signals. The parameters are set to "-g dm -n chip --broad --broad-cutoff 0.1". Based on normalized enrichment, first, manually define the approximate range of the centromere of each chromosome, that is, the region where CENH3 signals are enriched at the same time. Among the parameter settings: -g dm specifies the genome size for statistical significance calculation; -n chip specifies the output file prefix for easier subsequent result management; --broad identifies broad peaks for MACS2, suitable for signals like CENH3 that cover long chromatin regions; --broad-cutoff 0.1 sets the broad peak significance threshold, i.e., regions with q-value ≤ 0.1 are considered significant.
[0026] (5) Based on the sequencing depth and ChIP signal level of each sample, a continuous region with a signal intensity greater than 0.8 reads per bin in continuous bins (10kb) was selected as the core centromere, and the core boundary of the centromere was initially determined. Then, the region was extended to both ends in a 5kb window until the average signal intensity was lower than 0.8 reads per bin. The positions of these two ends were defined as the starting point and ending point of the centromere. (6) Output the start and end positions of the centromere regions of each chromosome and save them as a BED file. The results are shown in Table 2.
[0027] Table 2 Chili Peppers C. annuum Centromere origin and end position
[0028] As can be seen, Examples 1-2 clearly demonstrate that by applying this method, the DM potato variety and chili peppers can be successfully processed. C. annual The entire process of identifying and precisely defining the centromere regions across the entire genome of the strain is automated and at high resolution. Starting from the raw data, the entire process involves standardized quality control, alignment, and signal enrichment analysis to objectively output the start and end positions of the centromeres on each chromosome.
[0029] In summary, the plant centromere identification and boundary delineation method proposed in this invention successfully achieves automated and high-precision identification and boundary determination of centromere regions in the plant genome by establishing a complete and standardized bioinformatics analysis workflow. Based on CENH3 ChIP-seq data, this method sequentially employs key steps such as rigorous data quality control, efficient genome alignment, broad peak signal identification, candidate region delineation, core region locking, and refined boundary extension, ultimately outputting standardized centromere genome coordinates.
[0030] In terms of process standardization and automation, this invention constructs a complete, repeatable, and batch-executable analysis process by fixing key software tools, versions, and operating parameters. This significantly reduces subjective bias introduced by human intervention and improves the consistency and comparability of analysis results. Regarding innovative boundary determination strategies, an algorithm combining "continuous high-signal core region identification" and "fixed-step-size bidirectional signal extension" is employed. This algorithm can sensitively capture gradient changes in the signal of the filamentous region, solving the boundary ambiguity problem of traditional methods. In terms of adaptive threshold mechanisms, differentiated signal intensity determination thresholds are set based on the sequence characteristics and signal intensity differences of different diploid plant genomes, ensuring the method's applicability across various plant species, including peppers. C. annual Its wide applicability and accuracy in various crops such as potato DM, corn, and soybeans provide a powerful tool for plant centromere research, as well as a reliable basis for chromosome structural variation analysis and molecular breeding. It holds significant theoretical value and application prospects for a deeper understanding of chromosome biology, genome evolution, and the advancement of crop genetic improvement.
[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for identifying and defining the boundaries of plant centromeres, characterized in that, Includes the following steps: 1) Perform quality control on the raw CENH3 ChIP-seq sequencing data to remove low-quality bases, adapter sequences, and low-complexity sequences; 2) Align the reads after quality control in step 1) to the target plant reference genome, convert the files, calculate the average coverage or ChIP signal of each bin according to the preset window, and generate the CENH3 distribution curve; 3) Use signal enrichment analysis software to identify the CENH3 binding peak region and preliminarily delineate the candidate centromere regions of each chromosome; 4) Select the segment with continuous intensity higher than the preset threshold as the core centromere, and extend it to both sides with a fixed step size until the signal intensity is lower than the preset threshold. Determine the start and end positions of the centromere boundary and output the signal.
2. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The quality control described in step 1) uses the FASTP software.
3. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The comparison described in step 2) uses Bowtie2 software.
4. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The file conversion mentioned in step 2) uses samtools with a preset window size of 10kb. The preset window is used by bedtools to calculate the average coverage or ChIP signal of each bin.
5. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The signal enrichment analysis described in step 3) uses the MACS2 software, with the following parameters: -g dm -n chip --broad --broad-cutoff 0.
1.
6. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The fixed step size mentioned in step 4) is 5kb.
7. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The preset threshold mentioned in step 4) is set to 0.8 reads per bin in diploid plants.
8. The method for identifying and defining plant centromeres according to claim 1, characterized in that, The output described in step 4) is a BED format file containing the genomic coordinates of the centromere region.
9. A method for identifying and defining the centromere of a plant according to any one of claims 1 to 8, characterized in that, The method is applicable to diploid plants, including peppers, corn, and soybeans.
10. The application of the method according to claim 9 in genome structure analysis and breeding research.