生物多樣性與生態 Biodiversity and Ecology
| Time: | 10:10 ~ 12:00 on Wednesday |
| Room: | 博愛校區 賢齊館 BI310 |
| Textbook: | "Biology, 12th Edition" by Campbell, Urry, Cain, Wasserman, Minorsky, and Orr; Pearson 2021 |
| Grading: | 期末開書考 100% |
| Office hour: | 15:00 ~ 17:00 on Tuesday
15:00 ~ 17:00 on Thursday |
| TA: | 張景淞 |
| Reference: |
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| 2/21 | Introduction to Viruses | |
| 3/6 | Prokaryotes | |
| 3/6 | The Origin and Evolution of Eukaryotes | |
| 3/13 | Nonvascular and Seedless Vascular Plants | |
| 3/20 | Seed Plants | |
| 3/27 | Introduction to Fungi | |
| 4/10 | An introduction to Animal Diversity | |
| 4/17 | Invertebrates | |
| 4/24 | Vertebrates | |
| 5/1 | An Overview of Ecology | |
| 5/1 | Behavioral Ecology | |
| 5/8 | Populations and Life History Traits | |
| 5/15 | Biodiversity and Communities | |
| 5/22 | Energy Flow and Chemical Cycling in Ecosystems | |
| 5/29 | Conservation and Global Ecology | |
| 6/5 | Final exam (open book) | |
分子演化 Molecular Evolution
| Time: | 13:20 ~ 16:20 on Monday |
| Room: | 賢齊館 BI305 |
| Textbook: | None |
| Grading: | Homework 100% |
| Office hour: | 15:00 ~ 17:00 on Tuesday
15:00 ~ 17:00 on Thursday |
| TA: | |
| Reference: | 以下圖書可在交大浩然圖書館借閱 (連結為電子書)：
"Molecular evolution" by Wen-Hsiung Li; Sinauer Associates, 1997
"Molecular evolution :a phylogenetic approach" by Roderic D.M. Page & Edward C. Holmes; Blackwell Science, 1998
"Fundamentals of molecular evolution" by Dan Graur & Wen-Hsiung Li; Sinauer Associates, 2000
"Molecular evolution and phylogenetics" by Masatoshi Nei & Sudhir Kumar; Oxford University Press, 2000
"Data analysis in molecular biology and evolution" by Xuhua Xia; Kluwer Academic, 2000
"Bioinformatics and molecular evolution" by Paul G. Higgs & Teresa K. Attwood; Blackwell Pub., 2005
"Statistical methods in molecular evolution" by Rasmus Nielsen; Springer, 2005
"Computational molecular evolution" by Ziheng Yang; Oxford University Press, 2006 |
| 2/19 | Introduction | PPT |
| 2/26 | Dynamics of Genes in Populations | PPT |
| 3/4 | Dynamics of Genes in Populations | PPT |
| 3/11 | Dynamics of Genes in Populations | |
| 3/18 | Models of Nucleotide Substitution | PPT |
| 3/25 | Models of Nucleotide Substitution | PPT |
| 4/1 | Models of Amino Acid and Codon Substitution | PPT |
| 4/8 | Models of Amino Acid and Codon Substitution | PPT |
| 4/15 | Alignment | PPT |
| 4/22 | Phylogeny Reconstruction: Distance Methods | PPT |
| 4/29 | Phylogeny Reconstruction: Maximum Parsimony | PPT |
| 5/6 | Phylogeny Reconstruction: Maximum Likelihood | PPT |
| 5/13 | Comparison of Methods and Tests on Trees | PPT |
| 5/20 | Molecular Clock and Estimation of Species Divergence Times | PPT |
| 5/27 | Neutral and Adaptive Protein Evolution | PPT |
| 6/3 | Bayesian Methods | PPT |
| 6/3 | DNA Polymorphism in Populations | |
Homework:
E-mail your homework to me directly before the due date.
HW 1.
(due on 2/22) |
Collect the coding sequences of the HLA class I family, and their homologous sequences in other species (at least including human, chimpanzee, and macaque). Build a fasta file (*.fas). Use MEGA to perform a multiple sequences alignment and export a MEGA file (*.meg).
Collect the coding sequences of the mitochondrial cytochrome b genes for as many mammalian species as you can (at least 30 species, including some closely related species, and some divergent species pairs). Also export a MEGA file.
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HW 2.
(due on 2/29) |
Show the changes of allele frequencies over time for recessive alleles, dominant alleles, codominant alleles, overdominant alleles, and underdominant alleles under different selection coefficients and different initial allele frequencies.
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HW 3.
(due on 3/7) |
If you can program, (a) draw a figure showing the changes in frequencies of alleles subject to random genetic drift in populations of different sizes (say, 10 different sizes). Try different initial allele frequencies. (b) Draw figures showing the probability distributions of allele frequencies in a diploid population of N=100 (with 10,000 replicates) for generation 1, 5, 20, 100, 500, and 2000. Also try different initial allele frequencies; If you cannot program, use Excel to do the second job. You can use N=5 (2N=10) and 100 replicates instead. You can survey generation 1, 3, 5, and 20 instead.
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HW 4.
(due on 3/14) |
(a) Include the factor of "selection" to repeat the last homework. (b) Calculate the probability of fixation in slide 20.
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HW 5.
(due on 3/21) |
Use the "general substitution model" (the parameters refer to the substitution numbers observed in pseudogenes as shown in the PPT file) to display the nucleotide (A, T, C, G) probability (frequency) changes with time, as well as the change of the similarity, I. You can define different initial frequencies for A, T, C, and G.
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HW 6.
(due on 3/28) |
Display the transitional difference (ts) and the transversional difference (tv) with time.
Calculate the number of nucleotide differences, the proportion of nucleotide differences, JC69 one-parameter distance, and K80 two-parameter distance for the mitochondrial cytochrome b sequences you constructed in HW1. Compare your results with what MEGA computes for you.
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HW 7.
(due on 4/4) |
Calculate S0, S2, S4, V0, V2, and V4 between human HLA-A and HLA-B genes for the first 240 nucleotides.
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HW 8.
(due on 4/11) |
Use MEGA to calculate different genetic distances (number of transitions, number of transversions, JC69 one-parameter distance, K80 two-parameter distance, synonymous distance, nonsynonymous distance, and amino acid distance, etc.) for the mitochondrial cytochrome b sequences you constructed in HW1. Draw figures to compare these distances.
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HW 9.
(due on 4/18) |
Align the two sequences manually with identity score = 5, transition score = -1, transversion score = -3, gap penalty = -7. Try different parameters.
GATCTCGTCACTACTAATCGTACGTCATGCTGCT
GATAGTATTACTAGTACGTTATTTGCCTGCT
How about adding 2 nucleotides in the second sequence?
GATCTCGTCACTACTAATCGTACGTCATGCTGCT
GATAGTATTACTAGTACGTTATTTGCCTGCTGC
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HW 10.
(due on 4/25) |
Build a UPGMA tree and a NJ tree manually based on the mitochondrial cytochrome b sequence alignment you constructed in HW 1 (you can select 6 ~ 10 sequences). You can select any distance model you like. Compare your results with what MEGA builds for you.
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HW 11.
(due on 5/2) |
Build a NJ tree for the mitochondrial cytochrome b sequence alignment you constructed in HW 1 first. Use this topology and the parsimony principle to assign possible nucleotides on each internal node. You can just use the first 5 informative sites. Count the number of total substitutions on this tree. Compare this result with the Maximum Parsimony tree generated by MEGA. If they are different, illustrate what might be the reason.
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HW 12.
(due on 5/9) |
Calculate and compare the log likelihood values for the two topologies in the last slide.
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HW 13.
(due on 5/16) |
Build a phylogenetic tree based on the mitochondrial cytochrome b sequence alignment you constructed in HW 1 with 100 bootstrap repeats. Repeat this process 10 times. Construct another tree with 1000 bootstrap repeats. Also repeat this process 10 times. Compare these trees and their bootstrap supporting values. Identify the nodes with their bootstrap values less than 80 (or the couple nodes with the least supports). Based on these nodes, redraw a tree topology with polytomies. Try to list all possible bifurcating tree topologies based on these polytomies.
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HW 14.
(due on 5/23) |
Use synonymous distances (Ks) and nonsynonymous distances (Ka) to build two NJ trees for the mitochondrial cytochrome b sequences. Compare the topologies and branch lengths of these two trees. Select some branches which may have different evolutionary rates. Perform the relative rate test on them. Build one ML tree using the same parameters used in Timetree with 6~10 species remained. Construct its timetree manually. Assume human and chimpanzee diverged 6 million years ago, try to estimate the divergence time for other nodes.
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HW 15.
(due on 5/30) |
Use the HLA sequence alignment you constructed in HW 1. Calculate Ka and Ks; Could you show the evidence of positive selection? Could you identify which region is under positive selection mostly? Does positive selection occur between different loci? Does positive selection occur between different species?
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HW 16.
(due on 6/6) | Try different prior for Treeleft and Treeright to calculate their posterior probabilities (in HW12 last slide).
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計算生物實驗 Computational Biology Lab.
| Time: | 13:20 ~ 16:20 on Wednesday |
| Room: | 賢齊館 BI305 |
| Textbook: | None |
| Grading: | Homework 100% |
| Office hour: | 15:00 ~ 17:00 on Tuesday
15:00 ~ 17:00 on Thursday |
| TA: | |
| 4/24 | | Retrieve sequences from database | |
| 5/1 | | Sequence alignment -- dot matrix | PPT |
| 5/8 | | Sequence alignment -- dynamic programming | |
| 5/15 | | Calculate pairwise distances | |
| 5/22 | | Construct a phylogenetic tree | |
| 5/29 | | Calculate codon usage bias | |
| 6/5 | | Protein structure data | PPT
Data |
Homework:
Retrieve the protein sequences of human hemoglobin (alpha 1) and hemoglobin (beta) from database
Align these two sequences manually
Build a dot matrix for these two sequences
Using dynamic programming to align these two sequences
Using BLOSUM 62; Compare the obtained result with the previous one and make some discussion
Using local alignment; Compare the obtained result with the previous one and make some discussion
Using two types of gap penalty; Compare the obtained result with the previous one and make some discussion
Align the protein sequences of human hemoglobin (alpha 1) and hemoglobin (zeta). To generate the alignment represented in our textbook, what range of the gap penalty should be assigned?
Retrieve all the protein sequences of human and mouse (Mus musculus) hemoglobin from database, and align them based on the alignment result of hemoglobin (alpha 1) and hemoglobin (beta)
Calculate pairwise distances
Based on the calculated pairwise distances, construct a phylogenetic tree; Make some discussion
Retrieve the DNA coding sequences and their corresponding intron sequences of all human hemoglobins, and human TBPL1 (TATA-box binding protein like 1 gene) from the database
Calculate GC content for the coding sequences and intron sequences (GCi)
Calculate GC1, GC2, and GC3 for the coding sequences
Compare GC3 and GCi among these genes
Calculate codon usage frequencies for the coding sequences
Calculate RSCU values for the coding sequences
Retrieve the DNA coding sequences of the virus RaTG13 and Human betaherpesvirus 5 strain SOMA from the database
Calculate their GC1, GC2, GC3, and RSCU values
Compare the GC1, GC2, GC3, and RSCU values between the two viruses and human genes
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Contact
| Office: | +886-3-5712121 # 56960 |
| Fax: | +886-3-5729288
+886-3-5712121 # 56960 |
| Email: | yslin nycu.edu.tw |
| Address: | 新竹市博愛街75號
賢齊館 415室
R415, Jan Qi Building, 75 Po-Ai Street, Hsinchu, Taiwan 30068 |
| Lab: | +886-3-5712121 # 56961 |
Lectures
普通生物學（一）
General Biology (I)
計算生物實驗
Computational Biology Lab.
遺傳學
Genetics
生物多樣性與生態
Biodiversity and Ecology
演化生物學
Evolutionary Biology
分子演化
Molecular Evolution
Links
NCBI
EnsEMBL
Genome OnLine Database
Approved Sequencing Targets
UCSC Genome Bioinformatics
Stanford Genomic Resources
TGI - The Gene Index
J. Craig Venter Institute
Broad Institute
HapMap
SGD
SMD
MIPS
RCSB PDB
SCOP
ExPASy - SwissProt - PROSITE
CE - Combinatorial Extension
RepeatMasker
MEGA
PAUP
PAML
PhyML
CONSEL
MacClade
MrBayes
DAMBE
LiKaKs
Structure (population)
DnaSP
Arlequin
MCL - a cluster algorithm for graphs
The R Manuals
SimpleR
Chi-square Test
Fisher's Exact Test
Kolmogorov-Smirnov Test
Nature
Science
PNAS
PLoS Biology
Current Biology
Cell
EMBO
Nature Ecology & Evolution
Nature Genetics
Nature Biotechnology
Trends in Genetics
Genome Research
Genome Biology
Molecular Biology & Evolution
Nucleic Acids Research
Genetics
Evolution
Bioinformatics
Journal of Molecular Biology
Journal of Molecular Evolution
MPE
Proteins
Gene
國科會
Journal Citation Reports
政府電子採購網
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