Meta-Prism
Meta-Prism performs ultra-fast, accurate searches of microbial community structures in large-scale metagenomics databases to compare and retrieve similar microbiome samples across tens of thousands of entries.
Key Features:
- Dual-Indexing Approach: Employs a dual-indexing strategy to subgroup samples and enable more precise comparisons among diverse microbial communities.
- Refined Scoring Function: Incorporates a refined scoring function to detect subtle differences and assess similarity between microbial community profiles with high accuracy.
- Parallel Computation (CPU and GPU): Leverages parallel computation on CPU and GPU to accelerate processing, reporting at least a ten-fold speedup over contemporary methods.
- Large-Scale Metagenomic Search Capability: Designed to search and compare microbial community structures within large-scale metagenomics databases and across tens of thousands of samples.
- Support for Human and Environmental Microbiomes: Applicable to microbial community structures derived from both human and environmental sources.
Scientific Applications:
- Similarity Searches: Perform rapid similarity searches across metagenomic samples to identify related microbial communities.
- Comparative Microbiome Analysis: Identify similarities and distinctions within complex microbiome datasets for comparative studies.
- Large-Scale Pattern Discovery: Explore intrinsic patterns among tens of thousands of heterogeneous samples from human and environmental microbiomes.
Methodology:
Meta-Prism applies dual-indexing for sample subgrouping, a refined scoring function for detailed comparison, and parallel computation on CPU and GPU to accelerate searches.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- command-line tool
- Programming Languages:
- C++
- Added:
- 1/18/2021
- Last Updated:
- 2/22/2021
Operations
Publications
Zhu M, Kang K, Ning K. Meta-Prism: Ultra-fast and highly accurate microbial community structure search utilizing dual indexing and parallel computation. Briefings in Bioinformatics. 2020;22(1):557-567. doi:10.1093/bib/bbaa009. PMID:32031567.
DOI: 10.1093/BIB/BBAA009
PMID: 32031567
Funding: - National Science Foundation of China: 31671374, 31871334
- National Undergraduate Training Program for Innovation and Entrepreneurship of China: 201910487071
- Ministry of Science and Technology: 2018YFC0910502