Computational Genomics 10-810 / MSCBIO2070 (co-listed as 02710, 03715), Spring 2008 Ziv Bar-Joseph, Takis Benos School of Computer Science, Carnegie-Mellon University Department of Computational Biology, University of Pittsburgh

Syllabus and Course Schedule

Module	Material covered	Online material and links	Dates and Instructor
Introduction	A primer of molecular biology, cell biology and genetics	Lecture 1 (1/8): Intro to molecular biology-1 Lecture 2 (1/10): Intro to molecular biology-2	Jan 8: Takis Benos Jan 10: Takis Benos
Biological Sequence Analysis	Sequence analysis - heuristic algorithms Profile HMMs Gene finding Motif finding	Lecture 3 (1/15):  Sequence analysis & evolution Lecture 4 (1/17): Sequence analysis & evolution (part II) Elements of Statistical Learning Lecture 5 (1/22): Gene finding Lecture 6 (1/24): Motif finding Lecture 7 (1/29): Motif discovery algorithms (part I) Lecture 8 (1/31): Motif discovery algorithms (part II)	Jan 15: Takis Benos Jan 17: Takis Benos Jan 22: Takis Benos Problem set 1, Data: genomic (problem 1) promoters (problem 2) Additional files:MEME Help file Jan 24: Takis Benos Jan 29: Takis Benos Jan 31: Takis Benos
Advanced Sequence Analysis	Motif Alignment and Evolution microRNA genes Genome Assembly	Lecture 9 (2/5): Motif Alignment and evolution microRNA genes Lecture 10 (2/7):  Genome Sequencing and Assembly	Feb 5: Takis Benos Feb 7: Takis Benos Problem set 1 due Problem set 2, Data: Sequences (problem 2)
Gene Expression Analysis	Overview Normalization and differentially expressed genes Clustering Classification Gene expression Dynamics	Lecture 11 (2/12):  Microarrays Lecture 12 (2/14): Normalization Lecture 13 (2/19):  Differentially expressed genes Lecture 14 (2/21):  Clustering Lecture 15 (2/26):  Classification Lecture 16 (2/28):  Time series expression	Feb 12: Ziv Bar-Joseph Feb 14: Ziv Bar-Joseph Feb 19: Ziv Bar-Joseph Problem set 2 due Problem set 3 Data: Dataset (problem 3) Feb 21: Ziv Bar-Joseph Project proposals due Feb 26: Ziv Bar-Joseph Feb 28: Ziv Bar-Joseph Problem set 3 due
Midterm		Midterm questions and solutions	Mar 4:
Sequence Analysis and Evolution	Genome alignmens PhyloHMM Phylogenetic tree building	Lecture 17 (3/6): Genome comparisons, phyloHMMs Lecture 18 (3/18): Molecular evolution (CANCELLED)	Mar 6: Takis Benos Mar 18: Takis Benos
Guest Lecture	Russell Schwartz PhD	"Genome polymorphisms"	Mar 20
Systems Biology	Network evolution scale-free network network dynamics Network algorithms Topology and network motifs Cross-species network alignment Bayesian Networks regulatory networks Moeule networks Dynamic models Physical networks Protein-protein interactions	Lecture 19 (3/25): Biological networks Lecture 20 (3/27): Network algorithms Lecture 21 (4/1): Protein-protein interactions Lecture 22 (4/3): Regulatory networks and motif alignments Lecture 23 (4/8):  Reconstructing regulatory networks Lecture 24 (4/10):  Physical networks Lecture 25 (4/15): The ENCODE Project	Mar 25: Ziv Bar-Joseph Mar 27: Ziv Bar-Joseph Problem set 4 Apr 1: Ziv Bar-Joseph Apr 3: Ziv Bar-Joseph Apr 8: Ziv Bar-Joseph Apr 10: Ziv Bar-Joseph Problem set 4 due Apr 15: Takis Benos
Guest Lecture	Naftali Kaminski, MD, Pulmonary Medicine	"General approaches to the analysis of microarray experiments"	Apr 17
Project presentation (final paper and poster)			Apr 30

Recitation Schedule

Date	Time	Place	Topic

Additional Readings:

  Jan 22: Additional readings for lecture 5 (gene finding)

• C. Burge, S. Karlin (1997). Prediction of complete gene structures in human genomic DNA. J Mol Biol, 268, 78--94.
   
• Korf I, Flicek P, Duan D, Brent MR (2001). Integrating genomic homology into gene structure prediction. Bioinformatics, 17 Suppl 1: S140--148.
   
• Gross SS, Brent MR (2006). Using multiple alignments to improve gene prediction. J Comput Biol, 13, 379--393
   
• Rogic S, Mackworth AK, Ouellette FB (2001). Evaluation of Gene-Finding Programs on Mammalian Sequences. Genome Res, 11, 817--832
   

 
  Jan 29: Additional readings for lecture 7 (motif discovery and microRNA)

• Hertz GZ, Stormo GD (1999). Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics, 15, 563--577
   
• Lawrence CE, Altschul SF, Boguski MS, Liu JS, Neuwald AF, Wootton JC (1993). Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science, 262, 208--214
   
• Bailey TL, Elkan C (1995). The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol, 3, 21--29.
   
• Mahony S, Golden A, Smith TJ, Benos PV (2005). Improved detection of DNA motifs using a self-organized clustering of familial binding profiles. Bioinformatics, 21 Suppl 1, i283--i291.
   
• M Tompa et al. (2005). Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology, 23, 137--144.
   
• Miranda KC, Huynh T, Tay Y, Ang YS, Tam WL, Thomson AM, Lim B, Rigoutsos I (2006). A Pattern-Based Method for the Identification of MicroRNA Binding Sites and Their Corresponding Heteroduplexes. Cell, 126, 1203--1217.
   

 
  Feb 14: Additional readings for lecture 12 (normalization of miroarray data)

• Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response PNAS, 98, 511--5121.
   

 
  Feb 19: Additional readings for lecture 13 (Differentially expressed genes)

• M Eisen et al. Cluster analysis and display of genome-wide expression patterns PNAS, 95, 14863--14868
  
   

Feb 21: Additional readings for lecture 14 (Clustering)

• T R Golub et al. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring Science, 1999, 531--537