Compressive Read Mapping Using Homology Tables for Faster NGS Alignment
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Solution Overview
Problem
Next-generation sequencing (NGS) technologies face significant challenges in data analysis and storage due to the exponential growth of genomic read data, with existing read mapping methods requiring extensive computational resources and time, limiting their widespread use in biotechnology and genomic medicine.
Innovation Solution
The CORA (COmpressive Read-mapping Accelerator) framework addresses this by exploiting redundancy in NGS read datasets and reference genomes through self-mapping, creating a high-resolution homology table for efficient read mapping, and using a local neighborhood search to accelerate sequence comparison, allowing for sublinear-time paired-end read mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing read mapping methods (FM-index, hash table) are used, then mapping accuracy is maintained, but computational time and resource requirements increase linearly with dataset size
Solution Approach 1:
The patent merges multiple identical or similar reads into a single representative read, eliminating redundant processing. By combining reads that map to the same genomic locations, the system maintains mapping accuracy while reducing computational time from linear to sublinear scaling with dataset size.
Solution Approach 2:
The patent creates a compressed representation of the read dataset where identical reads are stored as single copies with metadata indicating their frequency. This copying strategy preserves all mapping information while using minimal storage and computation resources compared to processing each raw read individually.
2Reliability
If all-mapping is performed to identify all possible mappings within similarity threshold, then downstream analysis robustness improves, but computational cost increases significantly
Solution Approach 1:
The patent merges reads with identical or similar mapping patterns into representative reads. By processing these compressed representations, the system efficiently identifies all possible mappings for each unique read pattern, maintaining robustness for downstream analysis of structural variants, transposons, and copy-number variants while dramatically reducing computational cost.
Solution Approach 2:
The patent performs preliminary compression and deduplication of the read dataset before mapping. This preliminary action identifies and groups redundant reads, allowing subsequent all-mapping to be performed only on unique read patterns rather than every individual read, thus preserving analytical robustness while improving productivity.
3Loss of information
If raw NGS read datasets are stored and processed without compression, then data integrity is preserved, but storage space and processing time increase exponentially
Solution Approach 1:
The patent merges identical reads into single representative entries while preserving complete mapping information. This compression reduces storage requirements from exponential to linear scaling with dataset size, as redundant duplicate data is eliminated while all essential genomic mapping information is retained for intact data analysis.
Solution Approach 2:
The patent stores reads in a compressed format where identical sequences are represented as single copies with associated metadata. This copying strategy maintains full data integrity for mapping purposes while dramatically reducing the quantity of stored information, enabling efficient processing of large-scale NGS datasets.
Data Source
AI summary
A method of compressive read mapping. A high-resolution homology table is created for the reference genomic sequence, preferably by mapping the reference to itself. Once the homology table is created, the reads are compressed to eliminate full or partial redundancies across reads in the dataset. Preferably, compression is achieved through self-mapping of the read dataset. Next, a coarse mapping from the compressed read data to the reference is performed. Each read link generated represents a cluster of substrings from one or more reads in the dataset and stores their differences from a locus in the reference. Preferably, read links are further expanded to obtain final mapping results through traversal of the homology table, and final mapping results are reported. As compared to prior techniques, substantial speed-up gains are achieved through the compressive read mapping technique due to efficient utilization of redundancy within read sequences as well as the reference.