Clonotype Profile Determination via Sequence Tree Coalescence
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Solution Overview
Problem
Current methods for determining clonotypes and clonotype profiles from sequence data face challenges due to the complexity of large nucleic acid populations, limited predictability of natural variability, and noise introduced during sample preparation and measurement steps, which affects the accuracy and efficiency of diagnostic and prognostic applications.
Innovation Solution
The method involves forming a sequence tree data structure to organize and compare sequence reads, coalescing candidate clonotypes based on frequency and sequence differences, and removing coalesced clonotypes to efficiently distinguish genuine sequence differences from errors, thereby generating accurate clonotype profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sequence-based profiles are used to analyze large populations of nucleic acids, then measurement precision and sensitivity are improved, but device complexity and difficulty of data processing increase
Solution Approach 1:
The patent segments the complex sequence data into manageable units by identifying and isolating specific clonotypes within the larger nucleic acid population. This is achieved through algorithms that partition sequences based on similarity thresholds and frequency criteria, transforming the overwhelming complexity of analyzing entire populations into a systematic process of identifying discrete, meaningful units.
Solution Approach 2:
The patent extracts meaningful clonotype information from the complex sequence data by filtering out noise and irrelevant variations. Through algorithms that compare sequence reads against reference databases and apply statistical criteria, the method isolates genuine clonotypes from sequencing errors and natural variability, extracting only the biologically relevant signals.
2Reliability
If comprehensive sequence analysis is performed on large nucleic acid populations, then diagnostic and prognostic accuracy are improved, but analysis time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing sequence data to identify and filter out obvious errors before the main analysis. It also pre-establishes reference databases and similarity thresholds, allowing the actual clonotype determination to proceed more rapidly through comparison against these pre-prepared resources rather than analyzing everything from scratch.
Solution Approach 2:
The patent incorporates feedback mechanisms where the analysis process continuously refines its results by comparing identified clonotypes against established databases and statistical criteria. This feedback loop allows the system to correct misidentifications and converge on accurate results more efficiently, improving reliability while managing computational time through iterative refinement rather than brute-force analysis.
3Measurement precision
If sequence differences are distinguished from errors, then measurement precision is improved, but the complexity of data processing increases
Solution Approach 1:
The patent applies local quality assessment by evaluating different regions of the sequence data with appropriate stringency. It identifies clonotypes based on local similarity patterns, applying higher scrutiny to regions with ambiguous signals and lower scrutiny to clearly defined sequences. This localized approach allows precise distinction between true differences and errors without uniformly applying complex processing to entire datasets.
Data Source
AI summary
The invention is directed to methods for determining clonotypes and clonotype profiles in assays for analyzing immune repertoires by high throughput nucleic acid sequencing of somatically recombined immune molecules. In one aspect, the invention comprises generating a clonotype profile from an individual by generating sequence reads from a sample of recombined immune molecules; forming from the sequence reads a sequence tree representing candidate clonotypes each having a frequency; coalescing with a highest frequency candidate clonotype any lesser frequency candidate clonotypes whenever such lesser frequency is below a predetermined value and whenever a sequence difference therebetween is below a predetermined value to form a clonotype. After such coalescence, the candidate clonotypes is removed from the sequence tree and the process is repeated. This approach permits rapid and efficient differentiation of candidate clonotypes with genuine sequence differences from those with experimental or measurement errors, such as sequencing errors.