Human haplogroup hierarchical tracking and macro haplogroup normalization method
By establishing an adjacency list of authoritative phylogenetic trees and a normalized dictionary of synonyms, the problem of inconsistent haplogroup hierarchical classification was solved, and the automated processing and standardization of mtDNA and Y chromosome were realized. This improved the efficiency of haplogroup hierarchical tracking and the comparability of results, and provided a user-friendly interface and visualization functions.
Patent Information
- Application Number
- CN202511666640.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-02-10
AI Technical Summary
The lack of a unified standard for haplogroup hierarchical analysis in existing technologies makes it difficult to compare different research results. Manual table lookup is inefficient and difficult to handle large-scale samples. Furthermore, there is a lack of effective mechanisms for handling nomenclature heterogeneity and standardized normalization methods. In particular, the classification process for mtDNA and Y chromosome haplogroup hierarchies is cumbersome and the nomenclature is inconsistent.
It adopts an adjacency list data structure based on authoritative phylogenetic trees, constructs a synonym normalized dictionary, implements a hierarchical path backtracking algorithm, generates evolutionary paths by combining depth-first search, provides a path output interface, supports data input and preprocessing of multiple file formats, and integrates haplogroup path tracing and macro-haplogroup mapping functions.
It achieves standardization and automation at the haplogroup level, improves the comparability of results across studies, enhances processing efficiency, ensures the reproducibility and universality of results, and provides a user-friendly experience.
Smart Images
Figure CN121506255A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of population genetics and bioinformatics software methods, and in particular to a method for human haplogroup hierarchical tracking and macro-haplogroup normalization. Background Technology
[0002] Currently, the nomenclature rules for mtDNA haplogroups suffer from significant confusion. The alphabetical order does not correspond to the actual phylogenetic relationships, making it difficult to directly determine upstream and downstream hierarchical relationships. Comparisons of haplogroup hierarchies used in different studies are often affected by inconsistencies in naming, such as differences in aliases and suffixes. There is a lack of automated "root-to-leaf" path generation tools for batch samples, and a lack of unified macro-haplogroup classification standards. In contrast, Y chromosome haplogroups have rich and complex hierarchies, a large number of hierarchies, and significant differences in the number of hierarchies between different macro-haplogroups. This makes the multi-level classification process extremely cumbersome, and different studies often use multiple aliases, which is not conducive to comparing results from different studies.
[0003] Existing technologies for solving the above problems still have the following shortcomings: 1. There is a lack of unified standards for haplogroup hierarchical analysis, making it difficult to compare results from different studies; 2. Manual table lookup is inefficient, prone to errors, and difficult to handle large-scale samples; 3. Most existing tools only target a single type of mtDNA or chrY, lacking a comprehensive solution; 4. There is a lack of effective mechanisms for handling naming heterogeneity and standardized normalization methods.
[0004] Therefore, this invention proposes a method for human haplogroup hierarchical tracking and macro-haplogroup normalization. Summary of the Invention
[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for human haplogroup hierarchical tracking and macro-haplogroup normalization.
[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for human haplogroup hierarchical tracking and macro-haplogroup normalization includes the following steps: S1: Establish an adjacency list data structure based on an authoritative phylogenetic tree; S2: Construct a thesaurus normalized to address the heterogeneity problem in haplogroup naming; S3: Implement a hierarchical path backtracking algorithm, which backtracks from the target haplogroup node to the root node based on the depth-first search principle to generate a complete evolutionary path. The hierarchical path backtracking algorithm includes a path integrity verification mechanism, which automatically detects and reports broken paths or circular reference errors to ensure the biological rationality of the generated path. S4: Provides a path output interface, supporting both forward and reverse path representation.
[0007] Preferably, in step S1, the adjacency list data structure includes node identifier, node name, parent node identifier, and phylogenetic position information.
[0008] Preferably, in step S1: the adjacency list data structure is based on the PhyloTree database to construct mtDNA haplogroup relationships and on the ISOGG database to construct Y chromosome haplogroup relationships, ensuring the authority and accuracy of the data.
[0009] Preferably, in step S2: the thesaurus normalization dictionary adopts a multi-level matching strategy, first performing exact matching, then inclusion matching, and finally using a fuzzy matching algorithm to process spelling variants. The dictionary is updated regularly to cover newly discovered haplogroup naming variants.
[0010] Preferably, in step S2, the thesaurus normalization dictionary includes the mapping relationship between standard names and aliases, historical names, and abbreviations.
[0011] Preferably, step S2 specifically includes the following steps: S21: A predefined set of standard macrohaploid groups, based on geographical distribution and phylogenetic characteristics; S22: Establish a multi-level mapping rule system, including exact matching rules, prefix matching rules, and feature site matching rules; S23: Automated mapping engine implementation, which automatically identifies the corresponding macro-haplogroup category based on the input haplogroup label; S24: Establishment of a mapping result verification mechanism, including conflict detection and manual review interfaces.
[0012] Preferably, in step S21, the standard macrohaplogroup set includes major branches such as L0-L6, M, N, and R for mtDNA and major haplogroups such as AT for the Y chromosome. Each macrohaplogroup clearly defines the range of subclasses and characteristic mutation sites it contains.
[0013] Preferably, in step S22, the multi-level mapping rules adopt a priority processing mechanism, prioritizing exact matching. When exact matching fails, prefix matching and feature site matching are tried in turn to ensure mapping accuracy and coverage.
[0014] A system for hierarchical tracking and macro-haploid normalization of human haplogroups, comprising: The data input module supports importing multiple file formats, has data validation and error detection functions, and supports common bioinformatics file formats such as CSV, Excel, and VCF. It automatically recognizes file encoding and delimiters, performs data type validation, and checks required fields. The preprocessing module performs data cleaning, format standardization, and outlier handling. The core algorithm module integrates haplogroup path tracing and macro-haplogroup mapping functions; The results output module generates structured data files and visualization charts.
[0015] The beneficial effects of this invention are as follows: • High degree of standardization: Unifies hierarchical representation under different naming systems, significantly improving the comparability of cross-study results; • High level of automation: Supports batch automated processing output, which greatly improves time efficiency compared to traditional manual table lookup methods; • High reproducibility: Based on standardized algorithm processes, it ensures that experimental results are reproducible, facilitating peer review and scientific verification; • High versatility: It supports both mtDNA and Y chromosome lineages to meet different research needs; Excellent user experience: It provides a user-friendly graphical interface and rich visualization functions, reducing the barrier to entry for users. Attached Figure Description
[0016] Figure 1 This is a flowchart of a method for hierarchical tracking and macro-haploid normalization of human haplogroups proposed in this invention. Detailed Implementation
[0017] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.
[0018] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection or setting, a detachable connection or setting, or an integral connection or setting. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0019] Example 1: A method for human haplogroup hierarchical tracking and macro-haplogroup normalization, comprising the following steps: S1: Establish an adjacency list data structure based on an authoritative phylogenetic tree; S2: Construct a thesaurus normalized to address the heterogeneity problem in haplogroup naming; S3: Implement a hierarchical path backtracking algorithm, which backtracks from the target haplogroup node to the root node based on the depth-first search principle to generate a complete evolutionary path. The hierarchical path backtracking algorithm includes a path integrity verification mechanism, which automatically detects and reports broken paths or circular reference errors to ensure the biological rationality of the generated path. S4: Provides a path output interface, supporting both forward and reverse path representation.
[0020] In step S1, the adjacency list data structure includes node identifier, node name, parent node identifier, and phylogenetic position information.
[0021] In step S1: the adjacency list data structure is based on the PhyloTree database to construct mtDNA haplogroup relationships and on the ISOGG database to construct Y chromosome haplogroup relationships, ensuring the authority and accuracy of the data.
[0022] In step S2: the thesaurus normalization dictionary adopts a multi-level matching strategy, first performing exact matching, then inclusion matching, and finally using a fuzzy matching algorithm to process spelling variants. The dictionary is updated regularly to cover newly discovered haplogroup naming variants.
[0023] In step S2, the thesaurus normalization dictionary contains the mapping relationship between standard names and aliases, historical names, and abbreviations.
[0024] Step S2 specifically includes the following steps: S21: A predefined set of standard macrohaploid groups, based on geographical distribution and phylogenetic characteristics; S22: Establish a multi-level mapping rule system, including exact matching rules, prefix matching rules, and feature site matching rules; S23: Automated mapping engine implementation, which automatically identifies the corresponding macro-haplogroup category based on the input haplogroup label; S24: Establishment of a mapping result verification mechanism, including conflict detection and manual review interfaces.
[0025] In step S21, the standard macrohaplogroup set includes major branches such as L0-L6, M, N, and R for mtDNA and major haplogroups such as AT for Y chromosome. Each macrohaplogroup clearly defines the range of subclasses and characteristic mutation sites it contains.
[0026] In step S22, the multi-level mapping rules adopt a priority processing mechanism, prioritizing exact matching. When exact matching fails, prefix matching and feature site matching are tried in turn to ensure mapping accuracy and coverage.
[0027] Example 2: A system for human haplogroup hierarchical tracking and macro-haplogroup normalization, comprising: The data input module supports importing multiple file formats, has data validation and error detection functions, and supports common bioinformatics file formats such as CSV, Excel, and VCF. It automatically recognizes file encoding and delimiters, performs data type validation, and checks required fields. The preprocessing module performs data cleaning, format standardization, and outlier handling. The core algorithm module integrates haplogroup path tracing and macro-haplogroup mapping functions; The results output module generates structured data files and visualization charts.
[0028] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A method for hierarchical tracking of human haplogroups and macro-haplogroup normalization, characterized in that, Includes the following steps: S1: Establish an adjacency list data structure based on an authoritative phylogenetic tree; S2: Construct a thesaurus normalized to address the heterogeneity problem in haplogroup naming; S3: Implements a hierarchical path backtracking algorithm, which backtracks from the target haplogroup node to the root node based on the depth-first search principle to generate a complete evolutionary path. The hierarchical path backtracking algorithm includes a path integrity verification mechanism to automatically detect and report broken paths or circular reference errors. S4: Provides a path output interface, supporting both forward and reverse path representation.
2. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 1, characterized in that, In step S1, the adjacency list data structure includes node identifier, node name, parent node identifier, and phylogenetic position information.
3. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 2, characterized in that, In step S1: the adjacency list data structure is based on the PhyloTree database to construct mtDNA haplogroup relationships, and on the ISOGG database to construct Y chromosome haplogroup relationships.
4. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 2, characterized in that, In step S2: the thesaurus normalization dictionary adopts a multi-level matching strategy, first performing exact matching, then inclusion matching, and finally using a fuzzy matching algorithm to handle spelling variations. The dictionary is updated regularly.
5. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 1, characterized in that, In step S2, the thesaurus normalization dictionary contains the mapping relationship between standard names and aliases, historical names, and abbreviations.
6. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 5, characterized in that, Step S2 specifically includes the following steps: S21: A predefined set of standard macrohaploid groups, based on geographical distribution and phylogenetic characteristics; S22: Establish a multi-level mapping rule system, including exact matching rules, prefix matching rules, and feature site matching rules; S23: Automated mapping engine implementation, which automatically identifies the corresponding macro-haplogroup category based on the input haplogroup label; S24: Establishment of a mapping result verification mechanism, including conflict detection and manual review interfaces.
7. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 6, characterized in that, In step S21, the standard macrohaplogroup set includes L0-L6, M, N, and R branches for mtDNA and AT haplogroup for Y chromosome. Each macrohaplogroup clearly defines the range of subclasses and characteristic mutation sites it contains.
8. The method for human haplogroup hierarchical tracking and macro-haplogroup normalization according to claim 6, characterized in that, In step S22, the multi-level mapping rules adopt a priority processing mechanism, prioritizing exact matching. When exact matching fails, prefix matching and feature site matching are tried in turn to ensure mapping accuracy and coverage.
9. A system for human haplogroup hierarchical tracking and macro-haplogroup normalization, used to implement the method for human haplogroup hierarchical tracking and macro-haplogroup normalization as described in any one of claims 1-8, characterized in that, It includes: The data input module supports importing multiple file formats, has data validation and error detection functions, and supports CSV, Excel, and VCF bioinformatics file formats. It automatically recognizes file encoding and delimiters, performs data type validation, and checks required fields. The preprocessing module performs data cleaning, format standardization, and outlier handling. The core algorithm module integrates haplogroup path tracing and macro-haplogroup mapping functions; The results output module generates structured data files and visualization charts.