RecoverY: k-mer-based read classification for Y-chromosome-specific sequencing and assembly

Area of Science:

• Genomics
• Bioinformatics
• Mammalian Genetics

Background:

• The Y chromosome is challenging to assemble due to its repetitive nature and haploid state, leading to under-representation in genome assemblies.
• Current methods for Y chromosome read enrichment and identification often require manual parameter tuning, resulting in suboptimal assemblies.

Purpose of the Study:

• To develop an automated algorithm, RecoverY, for accurate identification and selection of Y-specific DNA reads for improved genome assembly.
• To overcome the limitations of manual parameter setting in existing k-mer abundance-based strategies.

Main Methods:

• RecoverY utilizes prior knowledge of related species' Y chromosomes or known Y transcript sequences to automatically determine k-mer abundance thresholds.
• The algorithm was evaluated on both simulated and real genomic data from human and gorilla.
• Performance was assessed by comparing assembly quality metrics against alternative filtering strategies.

Main Results:

• RecoverY significantly outperforms existing methods in Y-specific read selection, leading to vastly superior genome assemblies.
• Compared to a previous manual strategy, RecoverY achieved a 33% increase in assembly size and a 20% improvement in NG50.
• The automated parameter selection in RecoverY demonstrates robustness and effectiveness.

Conclusions:

• RecoverY provides a robust and automated solution for improving Y chromosome genome assembly.
• The tool enhances the accuracy and efficiency of Y-specific read identification, leading to higher quality genome assemblies.
• RecoverY is freely available, facilitating advancements in mammalian Y chromosome research.