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One way to visualize the similarity between two protein or nucleic acid sequences is to use a similarity matrix, known as a dot plot. These were introduced by Gibbs and McIntyre in 1970 [1] and are two-dimensional matrices that have the sequences of the proteins being compared along the vertical and horizontal axes.
Gene Set Association Analysis for RNA-Seq GSAASeq are computational methods that assess the differential expression of a pathway/gene set between two biological states based on sequence count data. GeneSCF a real-time based functional enrichment tool with support for multiple organisms.
Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ab initio gene finding, in which the genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes.
An illustration of a Manhattan plot depicting several strongly associated risk loci. A Manhattan plot is a type of plot, usually used to display data with a large number of data-points, many of non-zero amplitude, and with a distribution of higher-magnitude values.
A De Finetti diagram visualizing genotype frequencies as distances to triangle edges x (AA), y (Aa) and z (aa) in a ternary plot. The curved line are the Hardy–Weinberg equilibria. A Punnett square visualizing the genotype frequencies of a Hardy–Weinberg equilibrium as areas of a square. p (A) and q (a) are the allele frequencies.
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Often, it is more useful to calculate the information content with the background letter frequencies of the sequences you are studying rather than assuming equal probabilities of each letter (e.g., the GC-content of DNA of thermophilic bacteria range from 65.3 to 70.8, [3] thus a motif of ATAT would contain much more information than a motif of ...
Within computational biology, an MA plot is an application of a Bland–Altman plot for visual representation of genomic data. The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log ratio) and A (mean average) scales, then plotting these values.