Computational tools and Interoperability in Comparative ... - CBS

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Tools
for Comparison of Bacterial Genomes
6 Codon Usage Comparisons
Once the genes of a given genome have been defined, their codon usage can be analyzed. Since
the genetic code is redundant, with up to 6 codons per amino acid, variable codons are used at
different frequencies. Much of the redundancy in the genetic code is due to third base
variation. > Figure 4 displays the amino acid usage for three prokaryotic genomes: Methanosphaera
stadtmanae (27.6% GC), an archaeal methanogen that uses methanol and hydrogen to
produce methane; Desulfitobacterium hafniense (47.4% GC), a Firmicute that efficiently
dehalogenates tetrachloroethene and polychloroethanes; and Anaeromyxobacter dehalogenans
(75% GC). This species, the first myxobacteria to be grown as a pure culture, can use orthosubstituted
mono- and dichlorinated phenols. The frequency of each possible codon is plotted
in a wheel plot in the upper part of the figure, arranged such that their third base is conserved
in each quarter. The bias in codon usage towards the third position can also be seen in the
sequence logo plots in the lower part of > Fig. 4. From both graphics it is evident that genomic
GC content highly affects codon use (or the other way round). Based on a genome’s bias in
codon usage, it is possible to predict its likely environmental niche (Willenbrock et al., 2006).
Moreover, it is known that amino acid usage (not shown here) depends on environment, based
on analysis of metagenomic samples (Musto et al., 2006, Foerstner et al., 2005).
7 Protein Sequence Comparisons
One can compare each individual gene in a given genome by BLAST against a set of genomes.
This produces a huge amount of data that can be graphically represented in a BLAST Matrix
(Binnewies et al., 2005, Ussery et al., 2009). A BLAST Matrix is not symmetrical, as the
outcome is determined by which genome is used as query sequence. The diagonal of a BLAST
matrix represents a BLASTof a genome against itself. The self-match (the gene finding itself) is
discarded, thus the reported scores reflect internal homologues present in a given genome.
Most of these have been derived from gene duplication and are thus paralogs.
When more information should be visualized a BLAST Atlas is helpful. Such an atlas uses
one genome as a reference against which the gene conservation of other genomes is plotted
(Hallin and Ussery, 2004, Skovgaard et al., 2002). In this case gene location only refers to the
location in the reference genome, which of course can be varied in multiple BLAST Atlases.
A BLAST Atlas is also a suitable platform to visualize metagenomic data. So far, we have
not dealt with metagenomics extensively, mainly because this approach very rarely results in
completely assembled microbiological genomes. But for a BLAST Atlas, that is not a problem,
as one can combine all the metagenomic DNA in one lane, thereby ignoring from which
organism the detected genes originated. All obtained BLAST hits are plotted around a
reference genome. An example of a BLAST Atlas is given in > Fig. 5, centered around
Pelotomaculum thermopropionicum, a thermophilic, syntropic Firmicute that can utilize
1-butanol, 1-propanol, 1-pentanol or 1,3-propanediol as a carbon source. Note that despite
the high number of lanes, conserved and variable genes can still be easily visually inspected.
From compacting a single genome into a Genome Atlas, we’ve now moved several levels up
and compact multiple genomes into a single atlas. In > Fig. 5, the P. thermopropionicum
genome is compared to many species of Clostridia, as well as other bacteria. Unfortunately,
very few BLAST hits were found with the metagenomics samples so there is very little color in
those three lanes. Compared to well characterized genomes (like E. coli), relatively few hits are