High-Throughput Identification of Adapters in Single-Read Sequencing Data.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Public sequencing datasets are rapidly increasing, requiring efficient data preprocessing.
• Adapter sequence removal is a critical initial step in analyzing raw sequencing data.
• Existing tools for automated adapter detection in single-read protocols have limitations.

Purpose of the Study:

• To develop a tool for automating adapter sequence identification in raw single-read sequencing datasets.
• To provide a robust and reliable method for adapter detection across various sequencing technologies and adapter designs.
• To create a valuable toolset for metadata analysis of multiple sequencing datasets.

Main Methods:

• Development of the adapt_find tool for automated adapter sequence identification.
• Verification of adapt_find using publicly available sequencing datasets.
• Development of associated tools: random_mer for N-base detection and fastqc_parser for FASTQC result consolidation.

Main Results:

• adapt_find automates adapter sequence identification without prior knowledge.
• The tool demonstrates robustness, reliability, and high-throughput capabilities.
• Associated tools enhance metadata analysis by detecting random bases and consolidating FASTQC outputs.

Conclusions:

• adapt_find offers a significant improvement for preprocessing raw sequencing data.
• The toolset streamlines metadata analysis for large-scale sequencing projects.
• This automated approach enhances the efficiency and accuracy of genomic data analysis.