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Two elementary mutation operators, insertion and omission, are exemplarily applied to a hierarchy of graph-based models of increasing expressive power including directed graphs, event sequence graphs, finite-state machines and statecharts.
According to reading the sequence graphs, the genotype was determined.
Results were similar between the two sets of frequency sequence graphs.
The sequence graphs were analysed manually and with Mutation Surveyor software (SoftGenetics, State College, PA, USA).
For all the above time series analyses sequence graphs with predicted and observed quarterly enrollment were visualized and both the absolute and proportional differences were calculated.
Furthermore, we constructed frequency sequence graphs (logos) for 4024 siRNA sequences of 12 29 nt in length with an mRNA match.
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The algorithm converts the input diagrams into a graph representation called as Sequence Graph (SG) and then merges them into a single SG.
The nucleotide sequence is thus converted into a sequence graph.
Such a sequence graph always satisfies detailed balance (Methods).
The sequence graph can be smoothened by a shifting window technique.
Overlapping features are visualized by stacked bars in the so-called feature graph below the sequence graph (Fig. 1).
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