Genome Reconstruction via Breakpoint Graph Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing whole genome data, particularly in cancer cells, face challenges in accurately identifying genetic variations and reconstructing complex cancer genome karyotypes due to limited resolution and inability to interpret rearranged chromosome structures at a single nucleotide level.
Innovation Solution
A graph-based framework that uses input structural variation calls, unmapped reads, read-depth information, and single nucleotide polymorphisms to construct a breakpoint graph, allowing for the classification of rearrangement topologies and derivation of cancer genome karyotypes, including the detection of initial structural variations, construction of allele-specific and haplotype breakpoint graphs, and enumeration of Eulerian paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional karyotyping techniques (G-banding, FISH) are used to detect structural variations, then the existence of complex SVs can be confirmed, but the resolution is limited to 5 Mb and cannot accurately identify SVs at single nucleotide level
Solution Approach 1:
The patent segments the genome into discrete genomic segments represented as nodes in a graph, with edges representing adjacencies between segments. This segmentation allows high-resolution SV detection by analyzing individual segment relationships rather than relying on low-resolution banding patterns, achieving single nucleotide level precision while maintaining manageable computational complexity through systematic graph construction
Solution Approach 2:
The patent introduces a breakpoint graph as an intermediary data structure between raw sequencing data and SV identification. This graph mediates the analysis by representing genomic segments and their adjacencies, enabling high-resolution SV detection through graph traversal and path finding algorithms without directly processing the entire 3 billion nucleotide sequence, thus improving resolution while managing complexity
2Reliability
If graph-based methods integrate multiple genomic information to identify SVs, then SV detection capability improves, but the methods do not analyze actual karyotypes and cannot generate karyotypic topologies
Solution Approach 1:
The patent makes the breakpoint graph analysis universally applicable to multiple cancer types and genomic scenarios. The same graph construction and path finding algorithms can detect SVs, reconstruct karyotypes, and identify topological structures (HSR, DM, chromothripsis) across different cancer types, achieving both high SV detection accuracy and versatile karyotype reconstruction capability
Solution Approach 2:
The patent transitions from two-dimensional SV detection (presence/absence of SVs) to three-dimensional karyotype reconstruction by adding the dimension of chromosomal structure and topology. By representing chromosomes as paths through the breakpoint graph and analyzing cyclic structures, the method recovers karyotypic topologies while maintaining SV detection accuracy
3Loss of information
If iterative chromosome structural variations are analyzed to understand cancer evolution, then the basis of cancer development can be identified, but the complexity of analyzing 3 billion nucleotide sequences makes accurate variation identification difficult
Solution Approach 1:
The patent extracts and focuses analysis on breakpoint regions and genomic segments where SVs occur, rather than processing the entire 3 billion nucleotide sequence uniformly. By taking out only the relevant segments and representing them as graph nodes, the method reduces computational complexity while preserving complete genetic variation information through the graph structure
Solution Approach 2:
The patent creates a graphical representation (breakpoint graph) that copies the essential relationships and adjacencies from the complex genomic data in a simplified format. This graph copy maintains all genetic variation information while being much easier to process computationally, enabling analysis of cancer evolution without the full complexity of raw sequencing data
Data Source
AI summary
Disclosed is a genome reconstruction method using whole genome data. According to the present invention, the genome reconstruction method reduces detection errors by converting a nucleotide sequence having a structural variation into a graph form, and then reconstructing the graph so that the structural variation and the copy number variation have consistent values. Thereafter, the genome arrangement form was restored by constructing a haplotype graph using heterozygous single nucleotide polymorphism information and then finding an Eulerian path with a minimum entropy value.