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

Asan M S H Mohideen1, Steinar D Johansen1, Igor Babiak1

  • 1Genomics Group, Faculty of Biosciences and Aquaculture, Nord University, P.O. Box 1490, 8049 Bodø, Norway.

Biomolecules
|June 12, 2020
PubMed
Summary

Sequencing data preprocessing is essential for analysis. A new tool, adapt_find, automates adapter sequence identification in raw sequencing datasets, improving efficiency and reliability.

Keywords:
454 pyrosequencingIlluminaIon-TorrentSOLiDadapter oligonucleotidesadapter trimmingrandomized adapterssingle-read sequencingsmall RNA sequencing

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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.