scholarly journals Analyzing Gene Expression Time-Courses

2005 ◽  
Vol 2 (3) ◽  
pp. 179-193 ◽  
Author(s):  
A. Schliep ◽  
I.G. Costa ◽  
C. Steinhoff ◽  
A. Schonhuth
Keyword(s):  
Gene Expression ◽  
Time Courses ◽  
Expression Time

scholarly journals Robust inference of groups in gene expression time-courses using mixtures of HMMs

2004 ◽  
Vol 20 (Suppl 1) ◽  
pp. i283-i289 ◽  
Author(s):  
A. Schliep ◽  
C. Steinhoff ◽  
A. Schonhuth
Keyword(s):  
Gene Expression ◽  
Robust Inference ◽  
Time Courses ◽  
Expression Time

scholarly journals SIMULTANEOUSLY SEGMENTING MULTIPLE GENE EXPRESSION TIME COURSES BY ANALYZING CLUSTER DYNAMICS

2007 ◽  
Author(s):  
SATISH TADEPALLI ◽  
NAREN RAMAKRISHNAN ◽  
LAYNE T. WATSON ◽  
BHUBANESHWAR MISHRA ◽  
RICHARD F. HELM
Keyword(s):  
Gene Expression ◽  
Multiple Gene ◽  
Time Courses ◽  
Expression Time

scholarly journals CIDER: a pipeline for detecting waves of coordinated transcriptional regulation in gene expression time-course data

2015 ◽  
Author(s):  
Marco Mina ◽  
Giuseppe Jurman ◽  
Cesare Furlanello
Keyword(s):  
Gene Expression ◽  
Transcriptional Regulation ◽  
Gene Transcription ◽  
Environmental Changes ◽  
Expression Patterns ◽  
Transcriptional Networks ◽  
Motif Enrichment ◽  
Time Courses ◽  
External Stimuli ◽  
Expression Time

Cell adaptability to environmental changes is conferred by complex transcriptional regulatory networks, which respond to external stimuli by modulating the expression dynamics of each gene. Hence, deciphering the network of transcriptional regulation is remarkably important, but proves to be extremely challenging, mainly due to the unfavorable ratio between the number of available observations and the number of parameters to estimate. Most of the existing computational methods for the inference of transcriptional networks consider steady-state gene expression datasets, and produce models of transcriptional regulation best explaining the observed static gene expression. Gene expression time-courses are an emergent typology of gene expression data, paving the way to the characterization of the time-dependent dynamics of transcriptional regulation. In this work we introduce the Complexity Invariant Dynamic Time Warping motif EnRichment (CIDER) analysis, a novel computational pipeline to identify the prominent waves of coordinated gene transcription induced in cells by external stimuli, and determine which TFs are involved in the coordination of gene transcription. The CIDER pipeline combines unsupervised time series clustering and motif enrichment analysis to first detect transcriptional expression patterns, and then identify the TFs over-represented in the promoter regions of gene sets with similar expression dynamics. The ability of CIDER to correctly identify regulatory interactions is assessed on a realistic synthetic dataset of gene expression time-courses, generated by simulating the effects of knock-out perturbations on the E. coli regulatory network. The CIDER source code and the validation datasets are available on request from the corresponding author.

scholarly journals Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions

2011 ◽  
Vol 27 (7) ◽  
pp. 946-952 ◽  
Author(s):  
Christoph Hafemeister ◽  
Ivan G. Costa ◽  
Alexander Schönhuth ◽  
Alexander Schliep
Keyword(s):  
Gene Expression ◽  
Bayesian Estimation ◽  
Piecewise Constant ◽  
Piecewise Constant Functions ◽  
Time Courses ◽  
Expression Time
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