The following topics are based on reference papers.
Block motif model for aligning local functional segments of proteins.
Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald,
A. F., and Wootton, J. C. (1993), Detecting subtle sequence
signals: A Gibbs sampling strategy for multiple alignment, Science
262, p.208-214.
Liu, J. S., Neuwald, A. F., and Lawrence, C. E. (1995),
Bayesian models for multiple local sequence alignment and Gibbs
sampling strategies, JASA 90, no.432, p.1156-1170.
Algorithms of predicting protein functions.
Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D., and
Yeates, T. O. (1999), Assigning protein functions by comparative genome
analysis: Protein phylogenetic profiles, PNAS 96,
p.4285-4288. (PDF
file is also available on the site.)
Marcotte, E. M., Pellegrini, M., Ng, H. L., Rice, D. W., Yeates,
T. O., and Eisenberg, D. (1999), Detecting protein function and
protein-protein interactions from genome sequences, Science 285,
p.751-753.
Marcotte, E. M., Pellegrini, M., Thompson, M. J., Yeates, T. O., and
Eisenberg, D. (1999), A combined algorithm for genome-wide prediction
of protein function, Nature
402, p.83-86.
cDNA microarrays and oligonucleotide arrays.
Brown, P. O. and Botstein, D. (1999), Exploring the new world of
the genome with DNA microarrays, Nature
Genetics 21, p.33-37.
Lockhart, D. J. and Winzeler, E. A. (2000), Genomics, gene
expression and DNA arrays, Nature
405, p.827-836.
Lipshutz, R. J., Fodor, S. P. A., Gingeras, T. R., and Lockhart, D. J.
(1999), High density synthetic oligonucleotide arrays, Nature
Genetics 21, p.20-24.
Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K.,
Eisen, M. B., Brown, P. O., Botstein, D., and Futcher, B. (1998),
Comprehensive identification of cell cycle-regulated genes of the Yeast
Saccharomyces cerevisiae by microarray hybridization, Molecular
Biology of the Cell, 9, p.3273-3297. MBC
Online (PDF available)
Cluster analysis of the array data.
Eisen M. B., Spellman, P. T., Brown, P. O., and Botstein, D.
(1998), Cluster analysis and display of genome-wide expression
patterns, PNAS
95, p.14863-14868. (PDF available)
Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C.
W., Furey, T. S., Ares M. J., and Haussler, D. (2000), Knowledge-based
analysis of microarray gene expression data by using support vector
machines, PNAS
97, p.262-267. (PDF available)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S.,
Dmitrovsky, E., Lander, E. S., and Golub, T. R. (1999), Interpreting
patterns of gene expression with self-organizing maps: Methods and
application to hematopoietic differentiation, PNAS
96, p.2907-2912. (PDF available)
Gene expression data analysis, assessment of gene effects and models
of variations.
The lectures of 11/19 and 11/26 will be based on the references of
Li, C. and Wong, W. H. (2001), Model-based analysis of
oligonucleotide arrays: Expression index computation and outlier
detectionPNAS Vol. 98, 31-36
Tseng, G. C., Oh, M., Rohlin, L., Liao, J. C., and Wong, W. H. (2001)
Issues in cDNA microarray analysis: quality filtering, channel
normalization, models of variation and assessment of gene effects, Nucleic
Acids
Research, Vol 29, No 12,2549-2557
Dimension reduction for array data.
Alter, O., Brown, P. O., and Botstein, D. (2000), Singular value
decomposition for genome-wide expression data processing and modeling,
PNAS
97, p.10101-10106. (PDF available)
Holter, N. S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J. R.,
and Fedoroff, N. V. (2000), Fundamental patterns underlying gene
expression profiles: Simplicity from complexity, PNAS
97, p.8409-8414. (PDF available)