compariSeq: Rethinking Sequence Logos
نویسندگان
چکیده
Comparing Sequence Charts (compariSeq) is a redesign of traditional sequence logos for the task of comparing multiple biological sequences. All relevant data encoded in traditional sequence logos is conserved, however, attention is directed to the most important data, colors are more perceptually accessible, and the task of direct comparison at particular locations is supported.
منابع مشابه
Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos
Sequence logos are stacked bar graphs that generalize the notion of consensus sequence. They employ entropy statistics very effectively to display variation in a structural alignment of sequences of a common function, while emphasizing its over-represented features. Yet sequence logos cannot display features that distinguish functional subclasses within a structurally related superfamily nor do...
متن کاملenoLOGOS: a versatile web tool for energy normalized sequence logos
enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the availab...
متن کاملCodonLogo: a sequence logo-based viewer for codon patterns
MOTIVATION Conserved patterns across a multiple sequence alignment can be visualized by generating sequence logos. Sequence logos show each column in the alignment as stacks of symbol(s) where the height of a stack is proportional to its informational content, whereas the height of each symbol within the stack is proportional to its frequency in the column. Sequence logos use symbols of either ...
متن کاملLOGOS: a modular Bayesian model for de novo motif detection
The complexity of the global organization and internal structures of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection it is necessary to model the complex dependencies within and among motifs and incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal ...
متن کاملA modular Bayesian model for de novo motif detection
The complexity of the global organization and internal structure of motifs in higher eukaryotic organisms raises significant challenges for motif detection techniques. To achieve successful de novo motif detection it is necessary to model the complex dependencies within and among motifs and incorporate biological prior knowledge. In this paper, we present LOGOS, an integrated LOcal and GlObal m...
متن کامل