Compositional biases of bacterial genomes and evolutionary implications

By S. Karlin, J. Mrázek, A. Campbell. 1997

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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].”
Metagenomic Analyses: Past and Future Trends (2010, background): “In contrast, composition-based binning methods analyze intrinsic sequence features, such as GC content (10), codon usage (5), or oligonucleotide frequencies (59, 100), and compare these features with reference genome sequences of known taxonomic origins.”
Clustering metagenomic sequences with interpolated Markov models (2010, background): “These “genome signatures” are influenced by a variety of factors including codon usage, DNA structure, replication and repair processes, and evolutionary pressures [24-26].”
Informatics for unveiling hidden genome signatures. (2003, background): “This finding is consistent with that of Karlin et al. (1997) on compositional biases of bacterial genomes, again indicating that SOMs can effectively classify sequences according to biological categories.”
Analysis of codon usage. (2000, background): “There are 2818 copies of HIP1D distributed randomly over the genome on average every 1268 bp, this distribution and a high density suggested that they contribute to genome-wide activities (Karlin et al. 1997).”
A Bioinformatician's Guide to Metagenomics (2008, background): “Cellular processes such as codon usage, restriction-modification systems and DNA repair mechanisms produce sequence composition signatures, primarily oligonucleotide (word) frequencies, that are distinct in different genomes (31, 65, 66).”
Microsatellites within genes: structure, function, and evolution. (2004, background): “These SSR motifs were reminiscent of the presence of repetitive elements consisting of uptake signal sequences, intergenic dyad sequences, and multiple tetranucleotide iteration (Karlin, Mrazek, and Campbell 1997).”