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Papers to be discussed:

(A)  Fundamental and required

(B)  Required and more challenging

(C)  Not required (for the experts who want broader knowledge)

(L) Lecture

(P) Student course presentation

(R) Review paper, provides useful background information

 

Week 1: Microarray data and functional analysis: the very basics. (slides)

  1. (A; L) Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Golub et al. Science 1999.

 

  1. (A; L) A network-based analysis of systemic inflammation in humans. Calvano et al. Nature 2005

 

  1. (C) Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Li et al, PNAS 2001.

dChip software website (http://www.dchip.org)

 

Week 2: Reconstruction of signal transduction network; The use of False Discover Rate (FDR)

 

  1. (A; P; R) Reconstruction of cellular signalling networks and analysis of their properties. Subramaniam et al, Nature Reviews Molecular Cell Biology 2005.
    Presented by: Zeynep Madak-Erdogan (slides)
     
  2. (A; P) Statistical significance for genomewide studies. Storey and Tibshirani, PNAS 2003
    Hints on reading: Multiple hypothesis testing - what's wrong with p values?
     
  1. (B; L) Significance analysis of microarrays applied to the ionizing radiation response. Tusher et al. PNAS 2001.
    SAM software website (http://www-stat.stanford.edu/~tibs/SAM/)
    Presented by: Feng Hong
     
  2. (C; R) The Segmentation Clock: Converting Embryonic Time into Spatial Pattern  Olivier Pourquie, Science 2003

Week 3: Identification of transcription factor binding sites: the basics

    8.    (A; P) Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Chip Lawrance and Jun Liu et al, Science 1993
           Presented by: Ayano Sakai

    9.    (A; P; R) Computational Discovery of Gene Regulatory Binding Motifs: A Bayesian Perspective. Jun Liu et al, Statistical Science, 2004
           Presented by: Alexander L Kosorukov

    10.    (A; L; R) Statistical models for biological sequence motif discovery. Jun Liu et al, Case Studies in Bayesian Statistics VI, Springer.  2002.

    11.    (C) Core Transcriptional Regulatory Circuitry in Human Embryonic Stem Cells. Rick Young group, Cell 2005

    12.    (C) Integrating sequence motif discovery and microarray Analysis. Conlon et al, PNAS 2003

 

Week 4: Comparative genomics and Bayesian methods I: Hierarchical modeling & Monte Carlo computation

 

    13.    (A; L; R) Comparative genomics: Genome wide analysis in metazoan eukaryotes. Ureta-Vidal et al, Nature Genetics 2003
           Presented by: Mao-Feng Ger (slides)

    14.    (B; P) CisModule: De novo discovery of cis-regulatory modules by hierarchical mixture modeling. Zhou and Wong, PNAS 2004
           Presented by: Xinguo Jiang (slides)

            Discussion leader: Xin He (slides)

    15.    (C) De novo cis-regulatory module elicitation for eukaryotic genomes. Gupta and Liu, PNAS 2005

 

Week 5:  Machine learning in Genomics

 

    16.    (C; R) Unsupervised Learning. Ghahramani, Advanced Lectures on Machine Learning LNAI 3176. Springer-Ver 2004

 

    17.    (B; P) Clustering of time-course gene expression data using a mixed-effects model with B-splines.  Supplementary material. Luan and Li, Bioinformatics 2003
            Presented by: Zeynep Madak-Erdogan

 

    18.    (C) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Tseng and Wong, Biometrics. 2005

 

Week 6: Bayesian methods II: Bayesian unsupervised learning

 

    19.    (C) Context-Specific Bayesian Clustering for Gene Expression Data, Nir Friedman et al, Journal of Computational Biology 2002

 

    20.    (B; L) Bayesian Hierarchical Clustering, Heller and Ghahramani, ICML 2005
            Presented by: Guixian Lin.

 

    21.    (C) Empirical Bayes Analysis of a Microarray Experiment, Efron et al, JASA 2001

 

Week 7: Project report 1

            1) Epigenetic modification and control of gene expression
           Presented by: Kuei-Yang Hsien

           

            2) Modeling cross-lab variation and reproducibility of microarray data

            Presented by: Guixian Lin

 

Week 8: Mathematical modeling of Gene Ontology and Signaling pathways I

 

    22.    (B; P; R) Computational approaches to cellular rhythms. GoldBeter, Nature 2002
            Presented by: Kuei-Yang Hsiao

 

    23.    (C) Comparative Analysis of Gene Sets in the Gene Ontology Space under the Multiple Hypothesis Testing Framework. Zhong et al. CSB 2004

 

Week 9: Spring break

 

Week 10: Project report 2

 

Week 11: Mathematical modeling of Signaling pathways II

 

    24.    (B; L) Interlinked Fast and Slow Positive Feedback Loops Drive Reliable Cell Decisions. Brandma et al, Science 2005
            Presented by: Zhewen Fan

 

    25.    (B; P) Sharp developmental thresholds defined through bistability by antagonistic gradients of retinoic acid and FGF signaling. Pourquie and Goldbeter. Developmental Biology, in press.

6.       

7.      To make up the mathematics behind this paper, please read:

8.      26.    (C) The Dynamic Systems Approach to Control and Regulation of IntraCellular Networks. O.Wolkenhauer et al. FEBS Letters 579 (2005), 1846-1853.

 

Week 12: Project report 3

 

Week 13: Time series data analysis

 

    27.    (A; P) Comparing genomic expression patterns across species identifies shared transcriptional profile in aging, Hao Li group, Nature Genetics 2004

 

    28.    (B; P) Significance analysis of time course microarray experiments. Storey et al. PNAS 2005

 

Week 14: Project report 4

 

 

Further reading, not discussed in class

    29.    (C) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Tibshirani et al, PNAS 2002

    30.    (C) Discovery of regulatory elements in vertebrates through comparative genomics. Martin Tompa et al. Nat Biotech 2005

    31.    (C) Semi-supervised methods for predicting patient survival from gene expression. Bair et al, PLOS Biology 2005

    32.    (C) Achieving Stability of Lipopolysaccharide-Induced NF-{kappa}B Activation.  Covert et al, Science 2005