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Related Concept Videos

Range00:59

Range

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The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
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Variation: Normal Distribution, Range, and Standard Deviation02:32

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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Graphs of Functions01:30

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Integrating long-range connectivity information into de Bruijn graphs.

Isaac Turner1, Kiran V Garimella1,2, Zamin Iqbal1,3

  • 1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.

Bioinformatics (Oxford, England)
|March 20, 2018
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We introduce the Linked de Bruijn Graph (LdBG), a novel data structure that enhances de Bruijn graphs by storing long-range sequence information. This new graph improves genome assembly and variant calling accuracy compared to existing methods.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • De Bruijn graphs are essential for sequence analysis but lack long-range information.
  • Existing methods can yield suboptimal results due to this limitation.

Purpose of the Study:

  • Introduce a novel assembly graph data structure, the Linked de Bruijn Graph (LdBG).
  • Enhance de Bruijn graphs to store long-range connectivity information.
  • Improve sequence assembly and variant calling.

Main Methods:

  • Constructed LdBG by annotating de Bruijn graphs.
  • Demonstrated lossless sequence recovery from LdBG with error-free data.
  • Performed assembly simulations comparing LdBG, de Bruijn graph, and String Graph Assembler (SGA).

Main Results:

  • LdBG successfully stores and recovers sequence information.
  • LdBG outperformed de Bruijn graphs and SGA in assembly simulations.
  • Applied LdBG to *Klebsiella pneumoniae* data for large variant calls and genomic context analysis.

Conclusions:

  • LdBG is a powerful data structure for sequence analysis.
  • LdBG enables accurate long-range variant calling and genomic characterization.
  • LdBG implementation is available in McCortex.