Difference between revisions of "Lecture 17"
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[[Image:L17_s014.jpg|frame|none|Lecture 17, Slide 014<br> | [[Image:L17_s014.jpg|frame|none|Lecture 17, Slide 014<br> | ||
| + | Note that distance methods first reduce the explicit molecular data into a matrix of pairwise distances, then operate on that "summary" to construct the most plausible tree. They are fast, but less accurate than some alternatives. | ||
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[[Image:L17_s015.jpg|frame|none|Lecture 17, Slide 015<br> | [[Image:L17_s015.jpg|frame|none|Lecture 17, Slide 015<br> | ||
Revision as of 19:02, 28 November 2006
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Phylogenetic Analysis
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Lecture Slides
Slide 001
Lecture 17, Slide 001
Slide 002
Lecture 17, Slide 002
Slide 003
Lecture 17, Slide 003
Slide 004
Lecture 17, Slide 004
Slide 005
Lecture 17, Slide 005
Slide 006
Lecture 17, Slide 006
Slide 007
Lecture 17, Slide 007
Slide 008
Lecture 17, Slide 008
Slide 009
Lecture 17, Slide 009
Slide 010
Lecture 17, Slide 010
Slide 011
Lecture 17, Slide 011
Slide 012
Lecture 17, Slide 012
Usually, analysis of confidence implies a "bootstrapping" procedure: rerun the analysis many times with partial data and analyze which features of the tree (branching order -> topology!) are well conserved, and which ones depend critically on unreliable features of the input data.
Usually, analysis of confidence implies a "bootstrapping" procedure: rerun the analysis many times with partial data and analyze which features of the tree (branching order -> topology!) are well conserved, and which ones depend critically on unreliable features of the input data.
Slide 013
Lecture 17, Slide 013
Slide 014
Lecture 17, Slide 014
Note that distance methods first reduce the explicit molecular data into a matrix of pairwise distances, then operate on that "summary" to construct the most plausible tree. They are fast, but less accurate than some alternatives.
Note that distance methods first reduce the explicit molecular data into a matrix of pairwise distances, then operate on that "summary" to construct the most plausible tree. They are fast, but less accurate than some alternatives.
Slide 015
Lecture 17, Slide 015
Slide 016
Lecture 17, Slide 016
Slide 017
Lecture 17, Slide 017
Slide 018
Lecture 17, Slide 018
Slide 019
Lecture 17, Slide 019
Slide 020
Lecture 17, Slide 020
Slide 021
Lecture 17, Slide 021
Slide 022
Lecture 17, Slide 022
Slide 023
Lecture 17, Slide 023
Slide 024
Lecture 17, Slide 024
Slide 025
Lecture 17, Slide 025
Slide 026
Lecture 17, Slide 026
Slide 027
Lecture 17, Slide 027
