AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Low-complexity regions (LCRs) in genomic and proteomic sequences are crucial for regulatory and structural functions.
• Identifying LCRs is challenging due to their varied nature (repeats, GC-biased regions, etc.) and interference with standard sequencing and assembly methods.
• Existing methods struggle with automatic and accurate detection of LCRs, especially those with implicit or distant patterns.

Purpose of the Study:

• To develop and present a novel method and tool, AlcoR, for the automatic modeling, segmentation, and visualization of LCRs in biological sequences.
• To enable the distinction of both local and distant low-complexity patterns through models with adjustable memory.
• To provide a reference-free and alignment-free approach for LCR identification.

Main Methods:

• AlcoR utilizes a novel method for automatic LCR modeling and distinction, capable of handling different pattern complexities.
• The tool incorporates flexible simulation methods for generating biological sequences with controlled complexity levels.
• It includes sequence masking and a visualization tool for generating LCR maps in an ideogram style.

Main Results:

• AlcoR demonstrates high efficiency and accuracy in segmenting and visualizing LCRs across synthetic, semi-synthetic, and natural sequences.
• The tool successfully generated a whole-chromosome low-complexity map for a complete human genome.
• Haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar were analyzed using AlcoR.

Conclusions:

• AlcoR offers fast sequence characterization through data complexity analysis, particularly useful for novel or unknown sequences.
• The method is implemented in C with multithreading for computational speed, is flexible, and has no external dependencies.
• AlcoR is freely available, supporting broad accessibility for genomic and proteomic sequence analysis.