Reading List

1. Intro to Genomics, Biotechnologies,CS for Biologists

• See the Lecture Notes

2. Gene Expression Data Analysis

• Lecture Papers:
  • Huber, Heydebreck, and Vingron, Analysis of Microarray Gene Expression Data.
• Additional Material:
  •  A Model Based Background Adjustment for Oligonucleotide Expression Arrays, Wu, Z., Irizarry, R. A., Gentleman, R., Martin ez-Murillo, F., and Spencer, F.,
  • Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons, Nucleic Acids Research, 2006, 34:2 Harr, B., and Schlotterer, C.,
  • "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)

3. Classification

• 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
• 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)

• 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

5. Promoter Region Analysis

• 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

6. Data Integration I: Expression + Promoter Region

• 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, (2008) A Simple Model of the Modular Structure of Transcriptional Regulation in Yeast. J Comp Bio (4): 393-405 (2008)

7. Data Integration II: Expression, TF DNA, PPI and others

• Lecture Papers:
• Lee et al., Transcriptional regulatory networks in S. Cerevisiae, Science 2002
• De Bie et al., Discovering transcriptional modules from motif, ChIP-chip and microarray data, PSB 2004
  
• Liu et al., An algorithm for finding protein-DNA binding sites with applications to chromatin immunoprecipitation microarray experiments, Nat. Biotech, 2002
  
• Additional Reading:
• Bar-Joseph et al., Computational discovery of gene modules and regulatory networks, Nat. Biotech. 2003
  
• Marcotte et al., A Combined Algorithm for Genome-wide Prediction of Protein Function, Nature, v. 402, 1999, 83-86.
  
• Ge et al., Correlation Between Transcriptome and Interactome Mapping Data from Saccharomyces Cerevisiae, Nature Genetics, v. 29, 2001, 482-486.

8. Biological Networks, Gene Network Modeling and Inference

• Lecture Notes
• 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.

9. Data Integration III: Towards Networks

• Lecture Notes
• Lecture Papers: 
• Additional Reading:
  • Tanay et al,  Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data
  • Gene network papers collection