Nucleotide Sequence Matching via Mer Sorting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional gene assembly systems face challenges in efficiently processing large nucleotide sequence datasets due to high computational overhead and long processing times, especially when dealing with enormous data sets that exceed the capacity of volatile memory.
Innovation Solution
A layout assembly system that divides nucleotide sequences into mers, assigns indices, and uses a mer generating and sorting system, hit determination system, hit table sorting system, and post processing system to efficiently compare and align sequences, reducing the need for loading entire datasets into volatile memory and minimizing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional gene assembly systems process entire datasets in a single operation, then complete genome assembly is achieved, but processing time becomes excessively long and memory requirements exceed volatile memory capacity
Solution Approach 1:
The patent divides the genome assembly process into distinct phases: read processing phase where reads are converted to layout information and stored in nonvolatile memory, and assembly phase where the layout is used to guide efficient assembly. This segmentation allows the system to handle large datasets without loading everything into volatile memory simultaneously, thereby reducing processing time while maintaining completeness.
Solution Approach 2:
The system performs preliminary processing of reads to generate layout information (contig-scaffold- chromosome mappings) before the actual assembly operation. This pre-computed layout is stored in nonvolatile memory and serves as a guide during assembly, eliminating the need for time-consuming real-time comparisons and significantly reducing overall assembly time.
2Speed
If entire nucleotide sequence datasets are loaded into volatile memory for processing, then random access and processing speed improve, but memory requirements become unmanageable for large datasets
Solution Approach 1:
The patent segments the dataset handling into two parts: layout information (compact metadata about genome structure) is stored in nonvolatile memory, while only the actual assembly operations load necessary portions into volatile memory. This segmentation enables efficient processing of large datasets without requiring excessive memory capacity.
Solution Approach 2:
The layout information acts as an intermediary between the stored read data and the assembly process. Instead of directly comparing all reads during assembly, the system uses the pre-computed layout as a mediator to guide the assembly, reducing both memory requirements and access time by avoiding redundant data transfers.
3Reliability
If raster-type scan is used for pairwise comparison of sequences, then systematic comparison is achieved, but computational overhead increases significantly for large datasets
Solution Approach 1:
The system performs preliminary organization of reads into layouts (grouping by contig, scaffold, and chromosome) before the pairwise comparison phase. This pre-organization eliminates the need for exhaustive raster-type scanning by providing a structured framework that guides comparisons, thereby maintaining completeness while reducing computational overhead.
Solution Approach 2:
Instead of applying uniform raster-type scanning across the entire dataset, the patent applies localized comparisons within the context of the layout structure. Comparisons are performed within specific genomic regions and contexts defined by the layout, rather than globally across all sequences, reducing overall computational complexity while maintaining reliability.
Data Source
AI summary
Systems and methods automatically identify a set of read sequences in one or more larger nucleotide sequences within a set of comparing sequences as a template. The sequences of each set are divided into smaller mer sequences and sorted to arrange the mer sequences in order, and the sets of mers originating from the read sequence set and the comparing sequence set are compared pairwise to determine matching regions between the sequences of the read sequence set and the sequences of the comparing set. The sorting of the sequence sets prior to the pairwise comparison reduces the amount of volatile memory required to assemble the read sequence set and also reduces the overall time to identify matches of the read sequence set in one or more larger nucleotide sequence databases.