Skip Navigation


Bioinformatics Advance Access originally published online on September 13, 2005
Bioinformatics 2005 21(21):4026-4032; doi:10.1093/bioinformatics/bti662
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/21/4026    most recent
bti662v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (11)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Markowetz, F.
Right arrow Articles by Spang, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Markowetz, F.
Right arrow Articles by Spang, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Non-transcriptional pathway features reconstructed from secondary effects of RNA interference

Florian Markowetz *, Jacques Bloch and Rainer Spang

Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63–73, 14195 Berlin, Germany

*To whom correspondence should be addressed.

Motivation: Cellular signaling pathways, which are not modulated on a transcriptional level, cannot be directly deduced from expression profiling experiments. The situation changes, when external interventions such as RNA interference or gene knock-outs come into play. Even if the expression of the signaling genes is not changed, secondary effects in downstream genes shed light on the pathway, and allow partial reconstruction of its topology.

Results: We introduce an algorithm to infer non-transcriptional pathway features based on differential gene expression in silencing assays. We demonstrate the power of our algorithm in the controlled setting of simulation studies, and explain its practical use in the context of an RNA interference dataset investigating the response to microbial challenge in Drosophila melanogaster.

Contact: florian.markowetz{at}molgen.mpg.de

Received on July 14, 2005; revised on September 2, 2005; accepted on September 3, 2005

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
L. Kaderali, E. Dazert, U. Zeuge, M. Frese, and R. Bartenschlager
Reconstructing signaling pathways from RNAi data using probabilistic Boolean threshold networks
Bioinformatics, September 1, 2009; 25(17): 2229 - 2235.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
B. Anchang, M. J. Sadeh, J. Jacob, A. Tresch, M. O. Vlad, P. J. Oefner, and R. Spang
Special Feature: Complex Systems: From Chemistry to Systems Biology Special Feature: Modeling the temporal interplay of molecular signaling and gene expression by using dynamic nested effects models
PNAS, April 21, 2009; 106(16): 6447 - 6452.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
H. Frohlich, M. Fellmann, H. Sultmann, A. Poustka, and T. Beissbarth
Estimating large-scale signaling networks through nested effect models with intervention effects from microarray data
Bioinformatics, November 15, 2008; 24(22): 2650 - 2656.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
H. Frohlich, T. Beissbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, and F. Markowetz
Analyzing gene perturbation screens with nested effects models in R and bioconductor
Bioinformatics, November 1, 2008; 24(21): 2549 - 2550.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
T. Chiang, N. Li, S. Orchard, S. Kerrien, H. Hermjakob, R. Gentleman, and W. Huber
Rintact: enabling computational analysis of molecular interaction data from the IntAct repository
Bioinformatics, April 15, 2008; 24(8): 1100 - 1101.
[Abstract] [Full Text] [PDF]

Home page
 Brief Funct Genomic ProteomicHome page
F. Cordero, M. Botta, and R. A. Calogero
Microarray data analysis and mining approaches
Brief Funct Genomic Proteomic, January 22, 2008; (2008) elm034v1.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
F. Markowetz, D. Kostka, O. G. Troyanskaya, and R. Spang
Nested effects models for high-dimensional phenotyping screens
Bioinformatics, July 1, 2007; 23(13): i305 - i312.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. H. Ohn, J. Kim, and J. H. Kim
Genomic characterization of perturbation sensitivity
Bioinformatics, July 1, 2007; 23(13): i354 - i358.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.