Reading List

Week 1: Intro to Genomics, Biotechnologies,CS for Biologists

• Lecture Notes: 1, 2, 3

Week 2: Gene Expression Data Analysis, Classification

• Old Lecture Notes: Analysis, Classification
• Lecture Papers:
  • Huber, Heydebreck, and Vingron, Analysis of Microarray Gene Expression Data.
  • Golub et al., Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
  • Brown et al., Knowledge-based analysis of microarray gene expression data by using support vector machines
• Additional Material:
  • "Statistical Analysis of Gene Expression Microarray Data," Terry Speed editor, Chapman and Hall, 2003 (a text covering both statistical analysis and data mining)
  • Hughes et al., Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer, Nature Biotech. 2001, 19, 342-347 (array design)
  • Kerr, Churchill, Experimental Design for Gene Expression Microarrays, Biostatistics 2001, 2, 183-201 (experiment design)
  • Visit Terry Speed's Microarray Data Analysis Group Page for a number of great papers/software (take a look at their Always Log! page in Hints/Prejudices there)
  • Young et al., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation, NAR, 2002, 30, e15. (loess or lowess normalization)
  • Durbin et al., A variance-stabilizing transformation for gene-expression data, Bioinformatics, 2002, 18, 105-110 (statistical treatment)
  • Dudoit et al., Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Tech. Report 578, Stats Dept., UC Berkeley, 2000 (Differential Expression, Multiple testing: FWER)
  • Tusher et al., Significance testing of microarrays applied to the ionizing radiation response, PNAS, 2001, 98, 5116-5121 (Multiple testing: FDR)
  • Troyanskaya et al., Missing value estimation methods for DNA microarrays, Bioinformatics 2001 Jun;17(6):520-5. (Missing data is a very important concern in microarray data analysis)
  • Dudoit et al. (2002), "Comparison of discrimination methods for the classification of tumors using gene expression data", Journal of the American Statistical Association, 97(457):77-87. (Classification)
  • Pomeroy et al. (2002), "Prediction of central nervous system embryonal tumour outcome based on gene expression", Nature, 415:436-442. (Classification, various class. methods used)
  • Software: Many of the analysis and classification methods are implemented in R (see resources)

Week 3: Presentations

Week 4: Clustering

• Lecture Notes  
• Old Lecture Notes: Clustering
• Lecture Papers:
  • Eisen et al., Cluster analysis and display of genome-wide expression patterns
  • Chen et al., Cluster analysis of microarray gene expression data
• Additional Reading:
  • The above book by T. Speed.
  • Alon et al.(1999), "Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays", Proceedings of the National Academy of Sciences, 96(12):6745-6750 (nice application paper)
  • Ross et al. (2000), "Systematic variation in gene expression patterns in human cancer cell lines", Nature Genetics, 24(3):227-235 (nice application paper)
  • Tamayo et al. (1999), Interpreting patterns of gene expression with self organizing maps, 1999. PNAS, v. 96, 2907-2912. (First paper using Self Organizing Maps to cluster microarray data)
  • Sharan and Shamir (2000), "CLICK: A clustering algorithm with applications to gene expression analysis", Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (AAAI Press), pp.307-316. (Graph theoretic clustering)
  • Cheng and Church (2000), "Biclustering of expression data", Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (AAAI Press), pp.93-103. (first biclustering paper on microarray data)
  • Madeira and Oliveira (2004), "Biclustering Algorithms for Biological Data Analysis: A Survey," IEEE/ACM Transactions on Computational Biology and Bioinformatics 1(1): 24 - 45 (a survey of biclustering methods) 
  • Getz et al. (2000), "Coupled Two Way Clustering (CTWC)," PNAS 97, 12079
  • Bergmann et al. (2003), "Iterative signature algorithm for the analysis of large-scale gene expression data," PHYSICAL REVIEW E 67, 031902
  • Segal et al. (2004), "A module map showing conditional activity of expression modules in cancer," Nat Genet. 2004 Oct;36(10):1090-8
• Microarray Data Mining Tools Based on Clustering:
  • R, Bioconductor (in Resources on the class web page)
  • Expander
  • GeneXPress
  • for others see Resources on the class web page

Week 4: Promoter Region Analysis

• Lecture Notes: Motif Finding
• Old Lecture Notes: Finding Motifs in Promoter regions
• Lecture Papers: Tompa et al, An Assessment of Computational Tools for the Discovery of Transcription Factor Binding Sites.
• Additional Reading: The references in the above paper, which cover most available methods

Week 5: Data Integration I: Expression + Promoter Region

• Lecture Notes: Data Integration I
• Lecture Papers: 
  • Tavazoie et al., Systematic determination of genetic network architecture
  • Beer and Tavazoie, Predicting Gene Expression from Sequence
• Additional Reading:
  • Bussemaker et al., (2001) Regulatory element detection using correlation with expression, Nat. Genetics v. 27, 167-171
  • Conlon at al., (2003) Integrating regulatory motif discovery and genome-wide expression analysis, PNAS 100, 3339-3344
  • Filkov and Shah, (2004) Regulation Hierarchies from Expression Data, RECOMB Satellite Workshop on Regulatory Genomics, 2004

Week 5: Data Integration II: Expression, TF DNA, PPI and others

• Old Lecture Notes: Data Integration
• Lecture Papers:
• Additional Papers

Week 6: Presentations

Week 7 & 8: Biological Networks, Gene Network Modeling and Inference

• Lecture Notes: Gene Networks I (examples and continuous modeling) (NEW!)
• Lecture Notes: Gene Networks II (combinatorial models)
• Old Lecture Notes: Modeling, I, II, III, IV
• Lecture Papers: Filkov, Gene Network Inference from Large Scale Expression Data
• Additional Papers: 
  • A collection of paper from last year's Gene Networks class 
  • The references in the above lecture paper.

Week 9: Presentations

Week 10: Data Integration III: Towards Networks

• Old Lecture Notes: Realistic Models
• Lecture Papers: 
• Additional Reading:
  • Tanay et al,  Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data
  • The gene networks paper collection from last year