Fall 2010
MOLECULAR PHYLOGENETICS & EVOLUTION
Bot 669 CRN 78342
Dr. Sterling Keeley
St. John 508
Email: sterling@hawaii.edu
956-8043
Mondays
2:30-3:30 St. John 400
Wednesdays 1:30-4:30 St. John 400/510
Date
Week 1
23-25 Aug
Proposed Topics
Introduction/ Lecture(s): Fusion of Molecular Biology +
Phylogenetics + Population Biology. Why use phylogenetic
methods?
Reading, Discussion Recent Papers (abstracts)
Assignments
journal article
Week 2
Lecture(s): review Systematics, Cladistics, Taxonomic
concepts, new areas
Discussion of current papers & Abstracts
journal article
30Aug-Sept 1
Lab: Finding and downloading sequence data: NCBI,
GenBank, Swiss Prot, EMBL
Sample data set. BLAST search . FASTA, NEXUS formats
Assignment: Find and download sequences from
organism(s) related to your thesis research. Store in file for
future. BLAST to find other related groups (could serve as
outgroups later)
Week 3
6-8 Sept
Week 4
13-15 Sept
Week 5
20-22 Sept
Find and download
sequences from
organism(s) related
to your thesis
research.
Lecture(s): Alignment, credibility of data, homology,
missing sequences, gaps. Discussion of article & abstracts
Lab: Alignment software: MUSCLE, Clustal, Se-Al carbon
Assignment: Align sequences retrieved from GenBank
(above). Submit to me
journal
article/reading
Lecture(s): Alignment issues continued. Gap coding
Discussion of article & abstracts
Lab: Code gaps, treat as missing data, 5th character.
Assignment: Code gaps in your data set, submit to me
journal
article/reading
Lecture(s): Phylogenetic Analyses-Parsimony and Neighborjoining (N-J), outgroups, consensus trees, bootstrap
Lab: PAUP package, run various types of analyses, do
consensus trees (strict and majority rule), bootstrap (sample
data)
Assignment: Choose outgroups, run Maximum Parsimony
and N-J (minimum evolution) with consensus trees (strict and
journal
article/reading
Alignment
Gap coding
MP, NJ, Bootstrap,
PAUP
majority rule) bootstrap, analyses of your data. What is the
difference in your findings with strict vs majority rule
(submit)?
Week 6
27-29 Sept
Lecture(s): Bayesian inference, assumptions, Model choice
Lab: MrBayes, MEGA
Assignment: Run your data, use different number of chains,
number of generations, burn-in with each, compare trees with
prior Parsimony and N-J trees (submit)
Week 7
4-6 Oct
Week 8
11-13 Oct
journal
article/reading
MrBayes
Lecture(s): Maximum likelihood, model assumptions,
Modeltest
Lab: Run maximum-likelihood analysis with sample data
set, Modeltest
Assignment: Run a maximum-likelihood analysis with your
data, compare tree with those obtained earlier (submit). What
are the differences?
journal
article/reading
Lecture(s): Incongruence testing (partition homogeneity in
PAUP), choice in combining data partitions, Bremer support,
bootstrap
Lab: Run partition homogeneity test on sample data sets,
Bremer support, bootstrap.
Assignment: Run partition homogeneity test on your data.
What do Bremer Support and bootstrap values tell you about
the strength of the branches? Decide if combining partitions
can be supported. (submit)
journal
article/reading
RaxML, ML PAUP
Comparisons
Partition
homogeneity
Week 9
18-20 Oct
Lecture(s): Molecular evolution, transition/transversions,
journal
coding, non-coding, loops & stems, how different is different? article/reading
Lab: Raw data to sequences-electropherograms, base-calling,
contigs
Assignment: use either your own sequence data or that
See column 2
provided by me, view electropherograms, determine quality of
sequence, form contigs, correct base calls, trim sequences and
align (show me)
Week 10
25-27 Oct
Lecture(s): Population specific tools, microsatellites, SNPs,
journal
AFLPs, ISSRs
article/reading
Lab: use selected software for pop structure, sample data sets
Assignment: Use own data or that supplied, run analysis (at
See column 2
least one type), represent the output in a figure
Week 11
Lecture(s): History of molecular approaches: protein
journal
1-3 Nov
electrophoresis, RFLP, Restriction site mapping, Finger
printing, microsatellites
Lab: open (your project)
Assignment: Obtain literature, examine types of data
analyses, determine how you might use these approaches in
your work (submit summary)
article/reading
See column 2
Week 12
8-10 Nov
Lecture(s): Molecular evolution, what’s in a genome:
journal
Ribosomal vs nuclear DNA, single and multiple copy genes,
article/reading
gene families, satellites. Utility of different molecules
Lab: open (your project)
Assignment: Complete analyses of your data, i.e. parsimony, See column 2
N-J, Bayes, M-L. Prepare trees (MEGA or other) (show me)
Week 13
15-17 Nov
Lecture(s): Specifics of organellar genomes, animals, plants,
fungi, horizontal gene transfer
Lab: open
Assignment: Write results and discussion section, consider
implications (submit)
Week 14
Lecture: Genomics, phylogenomics or Molecular clocks
22-24 Nov
Thanksgiving
Lab: open
Assignment: write Abtract, Introduction, Methods(submit)
Week 15
Lecture(s) Presentation of your results (15 min each)
29 Nov-1Dec
Lab: Presentation of your results (15 min each)
Assignment Due (Journal article presentation of your
work) Submit
Week 16
6-8 Dec
Due (Journal article presentation of your work) Submit
Finals Week
Meet to go over papers (if desired) informal setting
journal
article/reading
See column 2
journal
article/reading
See column 2
See column 2
Grading: will be based on participation in class, hand in of assignments on time, quality of final
paper.
Objectives: Learn data analyses, presentation and explanation, types of data available and which are
most appropriate for your organism and type of information needed (question), how to put it all
together for publication. You are encouraged to use your own data if you have it or that from a
similar organism where learning the literature will be of value to your work.
Additionally, to learn to scan and sort literature to become aware of the big picture while
working on your immediate research problem.