ProxiClust: Data Sparsification and Community Detection for Assembly-Free Metagenomic Binning
Résumé
Metagenomics is an important field in biology where an environmental sample is sequenced to study the genomic content of species present in it. The data obtained from sequencing is a mixture of DNA fragments obtained from several species present in the sample. So an important step in this data analysis is to group together the DNA fragments originating from same specie or genera. In this paper we present an approach named ProxiClust, where we exhibit how Community Detection methods can be used to handle this task. The large size of the dataset poses challenge in using the traditional data mining technique given their computation and memory complexity. We aim to achieve scalability through a deterministic approach by converting the data from cloud points in to a graph and leverage community detection on it for identifying groups. Firstly the relevant pairwise relationships between DNA fragments are extracted by building proximity graphs on the data so storing complete distance matrix in memory can be avoided. The groups on graph are identified by leveraging community detection methods. We perform exploratory study to examine properties of several approaches for this framework and exhibit specific instances of this approach that perform comparably with state of the art binning methods.
Mots clés
Metagenomics
Detection algorithms
Image edge detection
Computational complexity
DNA
Indexes
Data mining
bioinformatics
data analysis
genomics
graph theory
data sparsification
environmental sample
genomic content
DNA fragments
community detection methods
deterministic approach
proximity graphs
binning methods
assembly-free metagenomic binning
distance matrix
ProxiClust approach
Community Detection
Metagenomic Binning
signal processing
statistical analysis
classification