TRELLIS+: an effective approach for indexing genome-scale sequences using suffix trees

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput sequencing generates vast amounts of data, necessitating scalable data management solutions.
• Existing suffix tree approaches face challenges in handling genome-scale datasets due to memory limitations.

Purpose of the Study:

• To introduce TRELLIS+, a novel disk-based suffix tree approach for efficient sequence data management.
• To demonstrate the scalability and performance of TRELLIS+ for large-scale sequence indexing and retrieval.

Main Methods:

• Development of TRELLIS+, a disk-based suffix tree algorithm.
• Implementation of a novel string buffering strategy to manage large datasets with limited main memory.
• Experimental evaluation comparing TRELLIS+ against existing suffix tree methods.

Main Results:

• TRELLIS+ demonstrates superior performance compared to existing suffix tree approaches.
• The method successfully indexes genome-scale sequences, including the entire Human genome.
• Rapid query processing is achieved over the disk-based index.

Conclusions:

• TRELLIS+ provides a scalable and efficient solution for managing and analyzing massive sequence data.
• The approach overcomes memory constraints, enabling the indexing of large genomes.
• TRELLIS+ facilitates rapid sequence data retrieval and analysis in bioinformatics.