Sequence Assembly Using Greedy Suffix Tree Overlap Algorithms
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Solution Overview
Problem
Current methods for aligning sequencing reads from advanced sequencing platforms like Illumina HiSeq and Pacific Biosciences' SMRT technology are inefficient due to lower accuracy and shorter read lengths, requiring new mapping methods that can handle higher error rates and insertion/deletion biases.
Innovation Solution
The development of novel algorithms and data structures, such as the greedy suffix tree overlap algorithm and BLASR (Basic Local Alignment with Successive Refinement), which use suffix trees and sparse dynamic programming to detect overlaps and refine alignments, especially for long reads with high indel rates, and the use of probabilistic graphical models to represent error characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional alignment methods (Smith-Waterman, MEGABLAST, BLAT) are used for sequencing reads, then alignment accuracy is maintained, but computational cost becomes prohibitive and processing speed decreases
Solution Approach 1:
The alignment process is divided into multiple stages: first using a fast heuristic method (BLASTN with word size 11) to identify candidate regions, then applying more accurate but computationally intensive dynamic programming only to these candidate regions. This segmentation allows the system to maintain high processing speed while achieving accurate alignment results.
Solution Approach 2:
The system performs preliminary alignment using BLASTN to quickly identify candidate regions before applying more rigorous dynamic programming alignment. This preliminary action filters out non-candidate regions, reducing the computational burden on subsequent accurate alignment steps while maintaining overall accuracy.
2Adaptability or versatility
If reads from advanced sequencing platforms are processed using traditional methods, then existing workflows are maintained, but error rates and indel biases cannot be properly handled
Solution Approach 1:
The alignment parameters and strategies are dynamically adjusted based on the sequencing platform and read characteristics. The system adapts word size, alignment thresholds, and dynamic programming parameters to match the specific error profiles and indel biases of different platforms, maintaining compatibility while improving reliability.
Solution Approach 2:
The system modifies alignment parameters such as word size, e-value thresholds, and dynamic programming matrices to be optimized for specific sequencing platforms. This allows traditional workflows to be maintained while properly handling platform-specific error rates and indel biases through parameter optimization.
3Productivity
If heuristics are used for read alignment to reduce computational cost, then processing speed improves, but sensitivity to genomic variation and sequencing error decreases
Solution Approach 1:
The alignment process segments reads into candidate regions identified by BLASTN, then applies dynamic programming only to these regions. This segmentation allows heuristics to maintain high processing speed for the majority of reads while sensitive dynamic programming is applied only where needed to maintain sensitivity to variation and error.
Solution Approach 2:
The system uses feedback from BLASTN alignment results to determine which regions require dynamic programming analysis. This feedback mechanism ensures that computational resources are allocated to regions with potential variations or errors, maintaining sensitivity while overall processing speed is improved through efficient resource allocation.
Data Source
AI summary
Computer implemented methods, and systems performing such methods for processing signal data from analytical operations and systems, and particularly in processing signal data from sequence-by-incorporation processes to identify nucleotide sequences of template nucleic acids and larger nucleic acid molecules, e.g., genomes or fragments thereof. In particularly preferred embodiments, nucleic acid sequences generated by such methods are subjected to de novo assembly and/or consensus sequence determination.