Census-based rapid and accurate metagenome taxonomic profiling

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Accurate taxonomic profiling of metagenomic samples is crucial for diagnostics, epidemiology, biodiversity, and food safety.
• Metagenomic sequencing generates vast datasets, making traditional read mapping computationally intensive.
• Decreasing sequencing costs drive the increasing use of metagenomic data for sample analysis.

Purpose of the Study:

• To develop a rapid and accurate computational tool for metagenome taxonomic profiling.
• To address the computational challenges of mapping millions of sequence reads to large nucleotide databases.
• To provide a robust method for estimating taxonomic composition and population distribution in metagenomic samples.

Main Methods:

• Developed CensuScope, a tool utilizing a robust subsampling-based algorithm.
• The algorithm randomly extracts a subset of reads and maps them to the NCBI nt database.
• The process is iterated multiple times to identify a consensus taxonomic composition, ensuring accuracy and robustness.

Main Results:

• CensuScope provides rapid and accurate taxonomic profiling of metagenomic data.
• The tool estimates both the taxonomic profile and relative frequencies of organisms within a sample.
• Subsampling 250 reads with 50 iterations achieves >99% statistical power for detecting 1% abundant taxa.

Conclusions:

• CensuScope is a versatile tool, runnable on laptops to high-performance computers.
• Recommendations for read subsampling size and iteration number are provided for optimal results.
• CensuScope is available for download and integrates with the High-performance Integrated Virtual Environment (HIVE).