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 | |
5/6 | Phylogeny Reconstruction: Maximum Likelihood | |
5/13 | Comparison of Methods and Tests on Trees | |
5/20 | Molecular Clock and Estimation of Species Divergence Times | |
5/27 | Neutral and Adaptive Protein Evolution | |
6/3 | Bayesian Methods | |
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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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
Using local alignment
Using two types of gap penalty
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
Retrieve all the DNA coding sequences of human and mouse (Mus musculus) hemoglobin from database, and subdivide them into 4 groups: human alpha, human beta, mouse alpha, and mouse beta
Calculate GC content for the 4 groups
Calculate GC1, GC2, and GC3 for the 4 groups
Calculate codon usage frequencies for the 4 groups
Calculate RSCU values for the 4 groups
Retrieve ribosomal subunit genes and histone genes, and also calculate their GC, GC1, GC2, GC3, and RSCU values
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Contact
Office: | +886-3-5712121 # 56960 |
Fax: | +886-3-5729288 +886-3-5712121 # 56960 |
Email: | yslinnycu.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
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MPE
Proteins
Gene
國科會
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