SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) generates short DNA reads crucial for biological research.
• Current NGS data requires extensive quality control (QC) due to technical and biological limitations.
• Choosing appropriate QC tools and parameters is complex, often requiring detailed knowledge of sequencing protocols.

Purpose of the Study:

• To develop a user-friendly pipeline, SHI7, for simplifying NGS short-read data quality control.
• To automate the prediction of sequencing adaptors, read types (shotgun vs. amplicon, paired-end vs. single-end), and stitchability.
• To enable automatic learning of optimal quality trimming parameters from the data itself.

Main Methods:

• Development of a streamlined bioinformatics pipeline named SHI7.
• Implementation of algorithms to predict sequencing adaptor presence and type.
• Integration of modules for automatic determination of read pairing status, stitchability, and quality score trimming.

Main Results:

• SHI7 successfully predicts common sequencing adaptors and identifies data types (shotgun/amplicon, paired-end/single-end).
• The pipeline accurately assesses read pair stitchability and estimates overlap.
• SHI7 automates parameter selection for quality trimming, reducing user burden.

Conclusions:

• SHI7 significantly simplifies the quality control of short-read NGS data.
• The pipeline empowers researchers, regardless of expertise, to perform robust data QC.
• SHI7 facilitates easier adoption of best practices for analyzing diverse sequencing datasets.