By V. G. Levitsky, A. V. Katokhin, O. A. Podkolodnaya, D. P. Furman (auth.), Nikolay Kolchanov, Ralf Hofestaedt (eds.)
Bioinformatics of Genome legislation and Structurecovers:
-regulatory genomic sequences: databases, wisdom bases, desktop research, modeling, and popularity;
-large-scale genome research and useful annotation;
-gene constitution detection and prediction;
-comparative and evolutionary genomics;
-computer research of genome polymorphism and evolution; desktop research and modeling of transcription, splicing, and translation; structural computational biology: structure-function association of genomic DNA, RNA, and proteins;
-gene networks, sign transduction pathways, and genetically managed metabolic pathways: rules of association, operation, and evolution;
-data warehousing, wisdom discovery and information mining; and,
-analysis of simple styles of genome operation, association, and evolution.
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Additional info for Bioinformatics of Genome Regulation and Structure
While the number of false positive as well as negative signals resulting from this strategy is unclear, we regard this window size as suitable for determining whether there exists a correlation between positions of predicted ARS consensus sequences and zero crossings of the GC-skew. ARS were predicted by extracting complete matches to ACS patterns. , 2001), they should represent useful predictors for testing a putative correlation. E. --~--~--- 0 1e+06 2e+06 3e+06 4e+06 5e+06 Position (base pairs) Figure 1.
The trend observed in Figure 3a) are mainly due to clustering of the base skew polarity switches. We then studied the distributions of distances for ARS and zero crossings separately. While distances between predicted ARS resemble a uniform distribution, the GC-skew zero crossings are strongly clustered at distances around 10-20 kb (data not shown). e. with polarity switch counts slightly above or below 800), with results supporting our conclusions. Table 1. For each of the 16 chromosomes of S.
METHODS AND ALGORITHMS First, tRNAs available in Sprinzl tRNA database (Sprinzl, 1998) were collected. All of them where divided into 22 families by their isoacceptor specificity (20 amino acids plus methionine-initiator and selenocysteine). These families were converted into templates (positional frequency matrices). During this process, representation of each tRNA sequence was taken into account (Sibbald, 1990). An original method of enlarged DNA nucleotide sequence similarity was used to detect highly divergent tRNA copies.
Bioinformatics of Genome Regulation and Structure by V. G. Levitsky, A. V. Katokhin, O. A. Podkolodnaya, D. P. Furman (auth.), Nikolay Kolchanov, Ralf Hofestaedt (eds.)