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 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 within 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 speed up sequence comparison, enabling 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 resources increase linearly with dataset size
Solution Approach 1:
The patent pre-processes the reference genome to build a compressed index structure (FM-index) before mapping reads. This preliminary action creates a data structure that enables faster querying during the actual mapping process, reducing the computational time required for each read while maintaining mapping accuracy.
Solution Approach 2:
The patent extracts and exploits the redundancy inherent in genomic sequences by using the Burrows-Wheeler Transform to compress the reference genome. This extraction of redundant information creates a more compact representation that requires fewer computational resources for mapping operations.
2Reliability
If all-mapping is performed to identify all possible mappings within similarity threshold, then downstream analysis robustness improves, but computational cost increases exponentially
Solution Approach 1:
The patent performs all-mapping by identifying all possible mappings within a specified similarity threshold using the compressed index. The FM-index structure enables efficient enumeration of all valid mappings without requiring exhaustive comparison, making the computationally intensive all-mapping approach feasible for large datasets.
Solution Approach 2:
The patent uses the compressed FM-index as a copy of the reference genome that preserves all necessary mapping information in a space-efficient format. This compressed copy enables rapid querying and enumeration of all possible read mappings without requiring access to the full uncompressed reference sequence.
3Reliability
If read datasets are stored in uncompressed format, then data integrity is maintained, but storage requirements and processing time increase
Solution Approach 1:
The patent applies the Burrows-Wheeler Transform to change the representation parameters of the reference genome, transforming it into a compressed format that reduces storage requirements while preserving all mapping information. This parameter change enables efficient storage and rapid access to genomic data.
Solution Approach 2:
The FM-index structure nests multiple levels of compressed information within each other, creating a hierarchical representation of the reference genome. This nested structure allows the system to store and efficiently query all necessary mapping information in a compact form.
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.