Soft Topographic Maps for Clustering and Classifying Bacteria Using Housekeeping Genes

Abstract

The Self-Organizing Map (SOM) algorithm is widely used for building topographic maps of data represented in a vectorial space, but it does not operate with dissimilarity data. Soft Topographic Map (STM) algorithm is an extension of SOM to arbitrary distance measures, and it creates a map using a set of units, organized in a rectangular lattice, defining data neighbourhood relationships. In the last years, a new standard for identifying bacteria using genotypic information began to be developed. In this new approach, phylogenetic relationships of bacteria could be determined by comparing a stable part of the bacteria genetic code, the so-called "housekeeping genes." The goal of this work is to build a topographic representation of bacteria clusters, by means of self-organizing maps, starting from genotypic features regarding housekeeping genes.

Topo-phylogeny: Visualizing evolutionary relationships on a topographic landscape

“…La Rosa et. al [ 23 ] demonstrate a method to create a topographic representation of bacteria clusters organized in a rectangular lattice that defines data neighborhood relationships. Unfortunately, the charts produced are non-intuitive and consequently difficult to decipher.…”

Probabilistic topic modeling for the analysis and classification of genomic sequences

“…The aim of this approach was to find a correlation between clusters and collections of bacteria belonging to the same taxon (taxonomic category). Clustering techniques have been used considering similarity among gene sequences expressed both in terms of classic evolutionary models [ 6 , 7 ], and in terms of compression-based models [ 8 , 9 ], that derive their theoretic assumption from the information theory concepts of Universal Similarity Metric [ 10 ]. The compression-based approaches have been also adopted for the study of phylogenetic relationships among animal species, considering the barcode COI gene [ 11 , 12 ].…”

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network

“…Many approaches for the classification of bacterial isolates, which use, for example, unsupervised clustering [1,2] and probabilistic methods [3], have been proposed. To improve the DNA analysis throughput, the use of well-defined gene regions has been investigated.…”

“…However, these methods exhibit some drawbacks: (1) they require the accurate tuning of the parameters, and (2) they are highly sensitive to the quality of the sequencing technique. For the purpose of taxonomic identification, alignment-free methods for the analysis of short sequences can overcome the drawbacks of classical methods [17], and several researchers have proposed different solutions based on phylogenetic reconstruction, machine learning and/or statistical methods [18].…”