Studying long 16S rDNA sequences with ultrafast-metagenomic sequence classification using exact alignments (Kraken)

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• 16S rDNA sequencing is crucial for microbial community analysis.
• Accurate taxonomic classification of short DNA sequences remains a challenge.
• Existing tools may not fully leverage information from unassembled or low-quality sequences.

Purpose of the Study:

• To evaluate the performance of Kraken for classifying long 16S rDNA sequences from Sanger sequencing.
• To compare Kraken's classification depth and accuracy against the Ribosomal Database Project (RDP) classifier.
• To assess Kraken's utility for unassembled and non-contig sequences.

Main Methods:

• Utilized Kraken, an ultrafast metagenomic sequence classifier employing exact alignments.
• Analyzed 264 long 16S rDNA contigs (1352 ± 153 bp) generated from Sanger sequencing.
• Compared Kraken's results with those obtained from the Ribosomal Database Project (RDP) classifier.

Main Results:

• Kraken achieved deeper classification, assigning 73% of contigs to species or variety levels, compared to RDP's 67% to genus level.
• Unassembled sequences and those not forming contigs, typically discarded, provided meaningful taxonomic information with Kraken.
• Kraken demonstrated comparable or superior classification performance to RDP, even without sequence assembly.

Conclusions:

• Kraken is highly effective for taxonomic assignment of Sanger sequencing-derived 16S rDNA data.
• The assembly step can potentially be eliminated when using Kraken for Sanger sequence analysis.
• Kraken offers a robust and efficient solution for metagenomic classification, including handling challenging sequence data.