Transcript Paper Pitch Reverse-engineering transcription control networks

AN INTEGRATED APPROACH TO
RECONSTRUCTING GENOME SCALE
TRANSCRIPTIONAL REGULATORY NETWORKS
SAHEED IMAM ET.AL.
A paper pitch by Ivan Valdes, Samar Tareen, Chibuike
Ugwuoke, Maryam Suleimani, Gianluca Mazzoni
Transcriptional Regulatory Networks
• Transcriptionally regulatory networks (TRNs) dynamically
alter gene expression in response to stimuli
• Many approaches generate TRNs based on the
assumption: expression is directly related to cognate
transcription factors (TFs).
• Drawback: Compromised by indirect effects such as co-expressed
but not co-regulated genes
• A novel workflow based on:
• Integration of comparative genomics data
• Global gene expression
• Intrinsic properties of TFs
The Workflow
Phylogenetic Footprinting
• Incorporates comparative
genomics and
phylogenetics
• Selection of appropriate
organisms
• Very closely related
organisms: might be
uninformative
• Very far: might not be
conserved
Author’s analysis: as few as
6 appropriately selected
organisms were sufficient
for a robust analysis
Identification of Orthologs
• Orthologs are genes
between species
sharing common
ancestry
• OrthoMCL to detect
orthologs
• Builds upon the
bidirectional best BLAST
using Markov Cluster
algorithm (MCL)
Integration of Gene Expression Data
• Matching clusters to
expression profiles
• Used DISTILLER
• Bi-clustering algorithm
• Presence/absence of
motifs via binary
classification
• Gives sub-conditions for
shared significant coexpression pattern
Linking TFs to Clusters
• Uses correlation
expression profiles to
map TFs to targets
• Proximity of location
• Similarity in DNA
binding motif
• Phylogenetic
correlation
QUESTIONS?