Axe: rapid, competitive sequence read demultiplexing using a trie.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• DNA sequencing generates large datasets requiring efficient processing.
• Demultiplexing is crucial for assigning sequence reads to their original samples.
• Existing methods may struggle with complex indexing strategies or sequencing errors.

Purpose of the Study:

• To introduce Axe, a novel algorithm for rapid DNA sequence demultiplexing.
• To develop a robust method capable of handling in-read indices with errors and variations.
• To provide an efficient tool for large-scale genomic data analysis.

Main Methods:

• Developed a rapid algorithm implemented in C for demultiplexing DNA sequence reads.
• The algorithm, Axe, identifies the optimal in-read index within a sequence.
• Axe accommodates combinatorial indexing, variable index lengths, and multiple mismatches.

Main Results:

• Axe accurately demultiplexes DNA sequence reads with in-read indices.
• The algorithm demonstrates high performance even with sequencing errors.
• It successfully handles complex indexing scenarios, including combinatorial indexing and mismatches.

Conclusions:

• Axe offers a fast and accurate solution for demultiplexing DNA sequence reads.
• The algorithm enhances the efficiency of processing large genomic datasets.
• Axe is a valuable tool for researchers working with in-read indexed sequencing data.