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    PE-Trimmer is a new algorithm that effectively removes low-quality reads and adapter sequences from next-generation sequencing (NGS) data. This tool improves downstream analysis by enhancing sequence accuracy and data quality.

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    Area of Science:

    • Genomics
    • Bioinformatics

    Background:

    • Next-generation sequencing (NGS) generates vast amounts of data, but sequence artifacts like errors and low-quality reads hinder downstream analysis.
    • Accurate processing of NGS data is crucial for reliable biological insights.

    Purpose of the Study:

    • To introduce PE-Trimmer, a novel algorithm designed for sensitive and specific trimming of next-generation sequencing data.
    • To address the challenge of sequence artifacts in paired-end reads.

    Main Methods:

    • PE-Trimmer identifies and removes technical sequences and adapter residues from paired-end reads.
    • It utilizes a quality score statistics histogram to determine trimming ranges.
    • A lightweight scoring model enhances trimming accuracy and strategy selection.

    Main Results:

    • PE-Trimmer demonstrated superior performance in removing low-quality reads and adapter sequences across five test datasets.
    • Comparative analysis showed PE-Trimmer outperformed five other leading trimming methods.
    • The algorithm is configurable and offers high throughput in multi-threaded mode.

    Conclusions:

    • PE-Trimmer provides an effective solution for cleaning NGS data, improving overall sequence quality.
    • The algorithm's accuracy and efficiency make it a valuable tool for bioinformatics analysis.
    • Its ability to handle paired-end reads and adapter contamination enhances its utility.