Compact next-generation sequencing datasets and efficient sequencing processing using them

Abstract

In a method comprising operating on genetic sequencing reads acquired by processing base sequences collected from a tissue sample, a compact textual representation of the genetic sequencing reads is generated. The compact text representation includes: (1) a text string representing a base sequence, and (2) a base quality text field identifying the longest subsequence of a base sequence for which the base of the subsequence base quality scores for bases satisfying a base quality score threshold; and storing a compact textual representation of a gene sequencing read in a raw read memory. To provide flexibility, the base quality text field may identify the longest subsequence for each of two or more different base quality score thresholds. During read alignment, offset boundaries for gene sequencing reads can be efficiently selected using the contents of the base quality text field.

Description

technical field [0001] The following relates to the field of genetic analysis, and to the same application in medical fields such as including the fields of oncology, veterinary medicine, etc. Background technique [0002] Efficient gene-sequencing systems, sometimes called "next-generation sequencing" (NGS) systems, are capable of rapidly and essentially automatically sequencing entire genomes. Although NGS accuracy is sufficient for clinical applications and is expected to improve as the technology matures, existing NGS systems sometimes exhibit lower Reading Accuracy. [0003] To assess read precision (or reliability), a base quality score is typically calculated for each base of a read. In the case of Sanger sequencing, the phred quality score is calculated from the spectrogram data by calculating parameters such as peak shape and resolution for the sequenced bases, and comparing these values ​​to an empirically built lookup table. Phred scores are generally considere...

AI Technical Summary

Problems solved by technology

[0009] The combination of the size of large genomic datasets and the rapidly decreasing cost of performing NGS means that genetic data storage is a major part of the total cost of sequencing applications and is expected to decrease as sequencing becomes cheaper and produces larger datasets. Continue growing

Furthermore, large raw read datasets translate into higher computational costs for downstream processing (such as alignment)

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