Methods for detecting variants in next-generation sequencing genomic data

Inventors

XU, Zhenyu • Song, Lin

Assignees

Sophia Genetis SA • Sophia Genetics SA

Abstract

A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variants from next generation sequencing reads out of a pool of enriched genomic patient samples without suffering from next generation sequencing workflow biases such as those introduced by sequencing errors in particular in repeat patterns regions of the human genome such as homopolymers or heteropolymers. The variant calling module may estimate the probability distribution of the length of the repeat pattern for each patient sample and cross-analyze it against other samples in a single experimental pool to identify best-fit variant models for each pair of samples. The variant calling module may further group samples according to their matching best-fit variant models and identify which group of patient samples carries the wild type reference without the need for control data in the pool. The variant calling module may subsequently characterize the homozygous or heterozygous repeat patterns variants for each patient sample with improved specificity and accuracy even in the presence of next generation sequencing biases.

Core Innovation

The method detects and reports, with a processor, a variant as a repeat of at least two nucleotide patterns in a genomic sequence of a patient sample. It identifies a reference repeat pattern P_ref=N*l as the repeat of l (l>=2) genomic patterns N in a genomic region of a human genome reference sequence, where the variant is located in the repeat-pattern genomic region. The method reports variants for hereditary disease or somatic disease by determining whether the sample variant corresponds to the wild type reference repeat pattern.

For each patient sample i, the method obtains a set S_i of next generation sequencing data reads covering the reference repeat pattern genomic region, from a pool of n enriched genomic patient samples (n>=4). The method aligns and re-aligns the next generation sequencing data reads via a realignment algorithm executed by a genomic data analyzer to provide refined data variant calling data, including receiving a next generation sequencing analysis request, identifying characteristics associated with the request, executing a first data alignment, identifying second characteristics including a data alignment pattern identifier, identifying one or more reads with soft clipping regions from the first alignment, and configuring a data re-alignment on the one or more reads with soft clipping regions.

The method measures the distribution P_i of the length of the repeat pattern in the reads for each patient sample, and for a possible pair of patient samples i and j estimates a best-fit model [V_i↔j1|V_i↔j2] of two allele variants for sample i relative to sample j with a confidence level L_ij. For each possible triplet of patient samples i, j>i, k>j, it compares their respective best-fit models, groups matching best-fit models into groups with an increased confidence level, iterates the comparison until stable groups are formed, identifies the most likely group carrying the wild type variant, and for each patient sample reports the sample variant as the wild type reference repeat pattern P_ref=N*l; for each patient sample out of the wild type group, it unbiases the best-fit variant model of that group and reports an unbiased best-fit model variant.

Claims Coverage

Independent claim clm-00001 defines the overall method of detecting and reporting repeat-pattern variants by combining read alignment/re-alignment with cohort-based repeat-length distribution modeling and confidence-stabilized wild-type group selection. No other independent claims are present in the provided partial content.

The independent claim covers repeat-pattern variant detection by identifying a reference repeat pattern, performing request-driven alignment followed by soft-clip re-alignment, measuring repeat-length distributions, estimating pairwise best-fit allele models with confidence, stabilizing confidence via triplet comparisons and grouping, selecting the wild-type group, reporting wild-type repeat patterns, and unbiasing out-of-group samples’ best-fit models relative to the wild-type group to support disease determination.

Stated Advantages

Documented Applications