ARAMIS: From systematic errors of NGS long reads to accurate assemblies

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Next-generation sequencing (NGS) long-read technologies, like PacBio, have advanced genomics but suffer from insertion/deletion (indel) error rates.
• Existing short-read correction algorithms are time-consuming and may not fully resolve these errors.

Purpose of the Study:

• To introduce Accurate long-Reads Assembly correction Method for Indel errorS (ARAMIS), a novel pipeline for correcting indel errors in long-read sequencing data.
• To evaluate ARAMIS's performance on diverse organisms and identify factors influencing indel error types.

Main Methods:

• ARAMIS integrates multiple short-read correction tools into a single-step pipeline.
• The pipeline was tested on PacBio-assembled genomes from six organisms with varying GC content, size, and complexity.
• Systematic sequencing errors in homopolymeric regions and their relation to GC content were analyzed.

Main Results:

• ARAMIS effectively corrects indel errors in long-read assemblies, outperforming other tools in accuracy and computational efficiency.
• PacBio sequencing errors, particularly indels in homopolymeric regions, are linked to organism GC content.
• The study identified systematic error patterns that could lead to incorrect biological interpretations in published research.

Conclusions:

• ARAMIS provides a robust and efficient solution for long-read error correction, enhancing genome assembly quality.
• Understanding the relationship between GC content and indel errors is crucial for accurate genomic analysis.
• ARAMIS offers insights into indel error nature and distribution, aiding future genomic studies.