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Related Experiment Video

Updated: May 30, 2026

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

Nephele: genotyping via complete composition vectors and MapReduce.

Marc E Colosimo¹, Matthew W Peterson, Scott Mardis

¹The MITRE Corporation, 202 Burlington Rd, Bedford MA 01730, USA. mcolosimo@mitre.org.

Source Code for Biology and Medicine

|August 20, 2011

Summary

This summary is machine-generated.

Nephele efficiently genotypes large genomic datasets using a complete composition vector algorithm and parallel processing. This significantly reduces computational cost and time for generating genotype trees compared to traditional phylogenetic methods.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Next-generation sequencing generates large genomics datasets, posing challenges for traditional phylogenetic methods.
Genotyping is crucial for understanding infectious disease origins, spread, and for microbial forensics.
Existing phylogenetic approaches require multiple sequence alignments and are computationally intensive for large datasets.

Purpose of the Study:

To develop a computationally efficient method for large-scale genomic data analysis and genotyping.
To overcome the limitations of traditional phylogenetic methods in handling rapidly growing sequence datasets.
To enable rapid and accurate genotype determination for disease surveillance and microbial forensics.

Main Methods:

Utilized the complete composition vector algorithm to represent sequences as k-mer derived vectors, bypassing multiple sequence alignment.
Employed affinity propagation clustering to group sequences into genotypes based on vector distance.
Leveraged Hadoop MapReduce for parallel execution across multiple compute nodes.

Main Results:

Nephele represents sequences as vectors and clusters them into genotypes with high correlation to expert-defined clades.
Achieved a significant reduction in computational cost compared to traditional phylogenetic methods.
Generated a neighbor-joined tree of over 10,000 16S samples in under 2 hours.

Conclusions:

Nephele substantially decreases processing time for generating genome-scale genotype trees.
The tool is effective for analyzing tens to hundreds of organisms.
Enables efficient large-scale genomic data interpretation for various applications.