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```Basis State Prediction of Cell-Cycle
Transcription Factors in Saccharomyces
cerevisiae
Dr. Matteo Pellegrini
Dr. Shawn Cokus
Sherri Rose
UCLA
Molecular, Cell, and
Developmental Biology
Department
Background: Expression Analysis

Microarrays measure the mRNA
concentration of genes expressed within a
yeast cell.
 Current statistical techniques to analyze
microarray data: Principal Component
Analysis (PCA), Singular Value
Decomposition (SVD), Independent
Component Analysis (ICA).
 These techniques do not always lead to clear
interpretations because they use complicated
linear combinations.
Rationale: Basis State Prediction
Use
biologically meaningful
basis states.
Develop a technique that will
describe expression data in
terms of these states.
Transcription Factor Binding Basis
States
The binding of 204 transcription
factors to yeast genes was measured.
 pharyngula.org
Expression Data Basis States
Describe expression data using basis states.
Y(1) = f(1)  e(1, 1) + f(2)  e(1, 2) + … + f(n)  e(1, n)
Y(2) = f(1)  e(2, 1) + f(2)  e(2, 2) + … + f(n)  e(2, n)
.
gene value
in original
experiment
.
.
.
.
.
.
.
.
.
activity
coefficient for
transcription
factor n
.
.
binding of
transcription
factor n to
gene 2
Y(m) = f(1)  e(m, 1) + f(2)  e(m, 2) + … + f(n)  e(m, n)
Strategy: Basis State Prediction
Expression data
Generated linear combinations of
transcription factor binding basis states
Graphical representation
Analysis
Goal: Basis State Prediction of
Cell-Cycle Dependence
•Predict transcription factors
that are cell-cycle dependent.
•Compare the expression of a
transcription factor to its
activity.
Yeast Cell Cycle
M/G1
 http://www.tau.ac.il/
Fourier Transform
Data that appears to be periodic can be
modeled as a sum of related sine waves.
The Fourier transform decomposes a
cycle of data into its sine components.
Fourier transform was applied to identify:
1) periodic transcription factor activity
2) mRNAs expressed in a periodic manner
Results I: Transcription Factors
with Periodic Activity
Analysis produced a rank-ordered list of transcription factors.
Some transcription factors are already known to be involved in
cell cycle transcription.
Transcription Factor
Periodic Activity
YOX1
0.367
SWI6
0.349
SWI4
0.337
YKR064W
0.332
NDD1
0.327
MBP1
0.321
FKH1
0.320
UGA3
0.310
RME1
0.303
HIR3
0.294
SWI5
0.292
Not listed: ACE2
unknown
protein
transcription
factor
associated
with stress
response
Results I: Comparing Transcription
Factor Activity and Expression
Some of the transcription factors with periodic activity
do not have periodic expression levels.
Transcription Factor
Periodic Activity
Expression
YOX1
0.367
0.252
SWI6
0.349
0.155
SWI4
0.337
0.286
YKR064W
0.332
0.077
NDD1
0.327
0.287
MBP1
0.321
0.040
FKH1
0.320
0.219
UGA3
0.310
0.040
RME1
0.303
0.023
HIR3
0.294
0.065
SWI5
0.292
0.143
Results I: Comparing Transcription
Factor Activity and Expression
Interactions Between Transcription Factors:
MBP1 forms a complex with SWI6. This may explain the periodic
activity of MBP1 in the cell cycle.
Transcription Factor
Periodic Activity
Expression
YOX1
0.367
0.252
SWI6
0.349
0.155
SWI4
0.337
0.286
YKR064W
0.332
0.077
NDD1
0.327
0.287
MBP1
0.321
0.040
FKH1
0.320
0.219
UGA3
0.310
0.040
RME1
0.303
0.023
HIR3
0.294
0.065
SWI5
0.292
0.143
Results I: Comparing
Transcription Factor
Activity and
Expression
Periodic
Interactions Between
Transcription Factors
MBP1 forms a
complex with SWI6.
This may explain
the periodic activity
of MBP1 in the cell
cycle.
Not Periodic
Results I: Comparing Transcription
Factor Activity and Expression
Identifying New Cell-Cycle Transcription Factors:
YKR064W a hypothetical protein. One might hypothesize that it is periodic
in the cell cycle due to unknown protein interactions.
Transcription Factor
Periodic Activity
Expression
YOX1
0.367
0.252
SWI6
0.349
0.155
SWI4
0.337
0.286
YKR064W
0.332
0.077
NDD1
0.327
0.287
MBP1
0.321
0.040
FKH1
0.320
0.219
UGA3
0.310
0.040
RME1
0.303
0.023
HIR3
0.294
0.065
SWI5
0.292
0.143
Results: Prediction of Cell-Cycle
Dependence

What does this show?
– One can use this method to identify
transcription factors that are cell-cycle
dependent.
– One can analyze differences in
expression versus activity in
transcription factors.
Basis State Prediction: The Future
The ability to describe complex
expression microarray data in
terms of small numbers of basis
states can increase our
understanding of the data and
quantitative models of
transcriptional networks.
References


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. Mol. Biol. Cell 9: 3273-3297.
Harbison, C.T., Gordon, B., Lee, T.I., Rinaldi, N.J.,
MacIsaac, K.D., Danford, T.W., Hannett, N.M., Tagne,
J.B., Reynolds, D.B., Yoo, J., Jennings, E.G.,
Zeitlinger, J., Pokholok, D.K., Kellis, M., Rolfe, P.A.,
Takusagawa, K.T., Lander, E.S., and Gifford, D.K.
2004. Transcriptional regulatory code of a eukaryotic
genome. Nature 431: 99-104.
```
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