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Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Bar Graph

Bar Graph

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...

Punnett Squares

Punnett Squares

Punnett Squares

Punnett Squares

Multiple Bar Graph

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...

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Related Experiment Video

Updated: May 19, 2026

Quantification of Orofacial Phenotypes in Xenopus

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Comparing pedigree graphs.

Bonnie Kirkpatrick¹, Yakir Reshef, Hilary Finucane

¹Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA. bbkirk@eecs.berkeley.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|August 18, 2012

Summary

This summary is machine-generated.

This study introduces new algorithms for comparing estimated family trees (pedigrees) with true pedigrees using genetic data. It addresses pedigree isomorphism and edit distance, providing efficient solutions and complexity analyses for these computational problems.

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Area of Science:

Computational Biology
Bioinformatics
Graph Theory

Background:

Traditional pedigree construction relies on costly genealogical record examination.
Automated methods reconstruct ancestral individuals from extant genetic data.
Evaluating automated pedigree reconstruction requires comparing estimated to true pedigrees.

Purpose of the Study:

To develop and analyze algorithms for comparing estimated pedigrees to true pedigrees.
To address the pedigree isomorphism and pedigree edit distance problems.
To assess the computational complexity of pedigree comparison.

Main Methods:

Developed a linear-time algorithm for the leaf-labeled pedigree isomorphism problem.
Presented exact and heuristic algorithms for the pedigree edit distance problem.
Proved hardness results for pedigree isomorphism (as hard as graph isomorphism) and edit distance (APX-hard, NP-hard).

Main Results:

A linear-time algorithm for leaf-labeled pedigree isomorphism.
Multiple algorithms for pedigree edit distance, including fast exact and general heuristic approaches.
Demonstrated the computational hardness of pedigree comparison problems.

Conclusions:

The study provides efficient algorithms and complexity insights for pedigree comparison.
These methods are crucial for evaluating automated pedigree reconstruction from genetic data.
The findings advance the field of computational phylogenetics and bioinformatics.