Informatics for unveiling hidden genome signatures.

By Takashi Abe, Shigehiko Kanaya, Makoto Kinouchi, Y. Ichiba, Tokio Kozuki, Toshimichi Ikemura. 2003

268 citations recorded by the source corpus.

With the increasing amount of available genome sequences, novel tools are needed for comprehensive analysis of species-specific sequence characteristics for a wide variety of genomes. We used an unsupervised neural network algorithm, a self-organizing map (SOM), to analyze di-, tri-, and tetranucleotide frequencies in a wide variety of prokaryotic and eukaryotic genomes. The SOM, which can cluster complex data efficiently, was shown to be an excellent tool for analyzing global characteristics of genome sequences and for revealing key combinations of oligonucleotides representing individual genomes. From analysis of 1- and 10-kb genomic sequences derived from 65 bacteria (a total of 170 Mb) and from 6 eukaryotes (460 Mb), clear species-specific separations of major portions of the sequences were obtained with the di-, tri-, and tetranucleotide SOMs. The unsupervised algorithm could recognize, in most 10-kb sequences, the species-specific characteristics (key combinations of oligonucleotide frequencies) that are signature features of each genome. We were able to classify DNA sequences within one and between many species into subgroups that corresponded generally to biological categories. Because the classification power is very high, the SOM is an efficient and fundamental bioinformatic strategy for extracting a wide range of genomic information from a vast amount of sequences.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Subsequent tools aimed to classify 300-3000 base pair reads from complex mixtures of microbial genomes based on tetranucleotide frequencies, which differ across organisms [224], using supervised [225,226] or unsupervised methods [227].”
TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences (2004, background): “Since numerous studies have shown that oligonucleotide frequencies within DNA sequences exhibit species-specific patterns [9-18], comparative analysis of such oligonucleotide frequencies is a promising approach to this problem.”
Community structure and metabolism through reconstruction of microbial genomes from the environment (2004, methodology): “Methods for the analysis of genome signatures currently under development may be required for binning genome data from more complex communitie”
Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities (2005, background): “]. In the absence of one of these genes, genome signatures such as dinucleotide frequencies, codon bias, and GC-content, developed by Karlin and others in a long series of papers [”
Clustering metagenomic sequences with interpolated Markov models (2010, background): “They are fairly constant within a genome [27-29] and can be useful for inferring phylogenies [30].”
A Bioinformatician's Guide to Metagenomics (2008, background): “sequences and include self-organizing maps (1) and the program TETRA (132).”