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The presence of one of these features, or the presence of multiple features, will cause for multiple lines to be plotted in a various possibility of configurations, depending on the features present in the sequences. A feature that will cause a very different result on the dot plot is the presence of low-complexity region/regions.
The marker-and-cell method is commonly used in computer graphics to discretize functions for fluid and other simulations. [1] It was developed by Francis Harlow and his collaborators at the Los Alamos National Laboratory .
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter , wxPython , Qt , or GTK .
Download as PDF; Printable version; In other projects ... move to sidebar hide. Dot plot may refer to: Dot plot (bioinformatics), for comparing two sequences ...
Solid Converter PDF is document reconstruction software from Solid Documents which converts PDF files to editable formats. Originally released for the Microsoft Windows operating system, a Mac OS X version was released in 2010. The current versions are Solid Converter PDF 9.0 for Windows and Solid PDF to Word for Mac 2.1.
A dot chart or dot plot is a statistical chart consisting of data points plotted on a fairly simple scale, typically using filled in circles. There are two common, yet very different, versions of the dot chart. The first has been used in hand-drawn (pre-computer era) graphs to depict distributions going back to 1884. [1]
The Lineweaver–Burk plot derives from a transformation of the Michaelis–Menten equation, = + in which the rate is a function of the substrate concentration and two parameters , the limiting rate, and , the Michaelis constant.
M is, therefore, the binary logarithm of the intensity ratio (or difference between log intensities) and A is the average log intensity for a dot in the plot. MA plots are then used to visualize intensity-dependent ratio of raw microarray data (microarrays typically show a bias here, with higher A resulting in higher |M|, i.e. the brighter the ...