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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Detection of Homologous Recombination Intermediates via Proximity Ligation and Quantitative PCR in Saccharomyces cerevisiae

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Scalable homology detection with ERAST.

Yinuo Jiang^1,2, Bing He³, Zihan Wu¹

¹AI for Life Sciences Laboratory, Tencent, Shenzhen, China.

Nature Biotechnology

|April 1, 2026

Summary

ERAST is a new tool for fast and accurate homology searches in large biological sequence databases. It uses AI and vector databases to find evolutionary relationships and functional similarities efficiently.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Homology search is crucial for understanding evolutionary relationships and functional similarities in biological sequences.
Existing methods like BLAST, Foldseek, and MMseqs2 face challenges in efficiently processing massive biological databases.
The scale of biological data necessitates novel approaches for rapid and accurate homology detection.

Purpose of the Study:

To introduce ERAST (efficient retrieval-augmented search tool), a novel solution for large-scale homology searching.
To leverage large language models and vector database technology for enhanced homology search performance.
To provide a tool capable of processing approximately one billion biological sequences efficiently and accurately.

Main Methods:

ERAST integrates large language models with advanced vector database technology.
The tool employs preretrieval, retrieval, and postretrieval optimization stages to enhance search quality.
Advanced indexing, fine-grained segmentation, and metadata integration are utilized for efficient processing.

Main Results:

ERAST demonstrates significant speed improvements, being approximately 50x faster than Foldseek and 50,000x faster than TM-align.
The tool achieves high precision in homology searches across nucleotide and protein sequences.
ERAST can search billions of sequences in milliseconds, setting a new benchmark for database scale.

Conclusions:

ERAST offers a highly efficient and precise solution for homology searching in massive biological sequence datasets.
The combination of AI and vector databases represents a powerful approach for computational biology challenges.
ERAST facilitates rapid identification of evolutionary and functional relationships, accelerating biological discovery.