1. From template networks
1.1 Madan Babu M. Evolutionary dynamics of prokaryotic transcriptional regulatory networks.
1.1.1 Data: template network and target genome
1.2 Lozada-Chavez I. Bacterial regulatory networks are extremely flexible in evolution.
1.2.1 Data: template network and target genome
1.3 Yu H. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs.
1.3.1 http://interolog.gersteinlab.org
2. Reverse engineering from expression data
2.1 Zou M. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data.
2.1.1 to the point and technical
2.1.2 Improvement on previous DBN methods
2.2 Gardner TS, Collins. Inferring genetic networks and identifying compound mode of action via expression profiling.
2.3 Segal E. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.
2.3.1 Algorithm available
2.4 Tegner J, Collins. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling.
2.4.1 to the point and technical
2.5 Yeung MK, Collins. Reverse engineering gene networks using singular value decomposition and robust regression.
2.5.1 short, to the point
2.6 ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context.
2.6.1 ARACNE algorithm
2.6.2 Improvement on regular Bayesian network approach
(According to authors (referring to static BNs), AJ Hartemink (Nature Genetics 2005) says the opposite (DBNs perform better))
2.7 Yu H, Hartemink AJ. Using Bayesian Network Inference Algorithms to Recover Molecular Genetic Regulatory Networks
2.7.1 DBN algorithm
2.7.2 Apparently equal sensitivity, higher precision than ARACNE
2.8 Wang SC. Reconstructing genetic networks from time ordered gene expression data using Bayesian method with global search algorithm.
2.8.1 http://www.worldscinet.com/185/02/0203/S0219720004000673.html
2.9 Wang Y. Inferring gene regulatory networks from multiple microarray datasets.
2.9.1 Gene Network Reconstruction Tool (GNR)
2.9.2 http://zhangorup.aporc.org/bioinfo/grninfer/
2.9.3 http://digbio.missouri.edu/grninfer/
2.10 Soinov LA. Towards reconstruction of gene networks from expression data by supervised learning.
2.10.1 accessible method
2.11 Nachman I. Inferring quantitative models of regulatory networks from expression data.
2.11.1 quantitative (weighted graphs)
2.12 Mehra S. A Boolean algorithm for reconstructing the structure of regulatory networks.
2.12.1 accessible method
2.12.2 qualitative
2.12.3 CP (Causal Predictor) and RCP (Relaxed Causal Predictor)
2.13 Deng X. EXAMINE: a computational approach to reconstructing gene regulatory networks.
2.13.1 Clustering needed for large systems
2.13.2 EXAMINE algorithm
2.14 Alter O. Reconstructing the pathways of a cellular system from genome-scale signals by using matrix and tensor computations.
2.14.1 Illustrations
2.15 Yun Z. Dynamic algorithm for inferring qualitative models of gene regulatory networks.
2.15.1 DFL (Discrete Function Learning) algorithm
2.15.2 Code given
2.16 Zhao W. Inferring gene regulatory networks from time series data using the minimum description length principle.
2.16.1 Short, algorithm included
2.17 Aburatani S. ASIAN: a web server for inferring a regulatory network framework from gene expression profiles.
2.17.1 ASIAN (Automatic System for Inferring A Network)
2.18 Qian J, Gerstein. Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data.
2.18.1 Support Vector Machines (SVMs)
2.19 Wille A. Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana
2.19.1 Graphical Gaussian Model (GGM)
2.19.2 Implementation may be difficult (mathematical description)
2.20 Butte AJ. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks
2.20.1 RELNET software
2.20.2 RNs (relevance networks)
2.20.3 Earlier paper:
Unsupervised Knowledge Discovery in Medical Databases Using Relevance Networks
2.21 Friedman N. Inferring Cellular Networks Using Probabilistic Graphical Models
2.21.1 Accessible review
3. From binding site data
3.1 Wang T. Identifying the conserved network of cis-regulatory sites of a eukaryotic genome.
3.1.1 PHYLONET algorithm for identification of binding motifs
3.2 Rodionov DA, Arkin, Gelfand. Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks.
3.2.1 Consensus sequences for TF binding
3.3 Rodionov DA, Arkin, Gelfand. Reconstruction of regulatory and metabolic pathways in metal-reducing delta-proteobacteria.
3.4 Gasch AP. Conservation and evolution of cis-regulatory systems in ascomycete fungi.
3.5 Liu Y. Eukaryotic regulatory element conservation analysis and identification using comparative genomics.
3.5.1 Refined TF binding site detection method
3.6 Mwangi MM. Genome wide identification of regulatory motifs in Bacillus subtilis.
3.6.1 Detects possible TF binding sites in annotated genome (upstream of operons)
3.6.2 Regulon clustering
3.7 Alkema WB. Regulog analysis: detection of conserved regulatory networks across bacteria: application to Staphylococcus aureus.
3.7.1 Increased regulon detection reliability
3.7.2 Confidence score based on known regulons
3.7.3 Regulogger algorithm
4. Combined approaches
4.1 Baumbach J. Brinkrolf K. Czaja L. Wittkop T. Rademacher K. Rahmann S and Tauch A. Coryneregnet and Coryneregnet 3. An excellent, user-friendly, integrative platform for the analysis of gene regulatory networks in Corynebacteria and E. coli
4.1.1 This is the first original work that describes the transcriptional regulatory network in Corynebacterium
4.2 Xing B. A causal inference approach for constructing transcriptional regulatory networks.
4.2.1 Genentech
4.2.2 Data: microarray data, promoter sequences, TF binding motifs
4.3 Xing B. A statistical method for constructing transcriptional regulatory networks using gene expression and sequence data.
4.3.1 Data: microarray data, promoter sequences, TF binding motifs
4.4 Chua G. A statistical method for constructing transcriptional regulatory networks using gene expression and sequence data.
4.5 Haverty PM. Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.
4.5.1 CARRIE / ROVER algorithms
4.6 Bar-Joseph Z. Computational discovery of gene modules and regulatory networks.
4.6.1 GRAM regulatory modules algorithm
4.6.2 Data: microarrays, TF binding sites
4.7 Elkon R. Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.
4.7.1 Data: microarrays, TF binding PWMs
4.8 Li S. Constructing biological networks through combined literature mining and microarray analysis: a LMMA approach.
4.8.1 Literature mining + microarrays
4.8.2 might be too complicated / specific
4.9 Kim H. Unraveling condition specific gene transcriptional regulatory networks in Saccharomyces cerevisiae.
4.9.1 Input: template network, microarray data
4.9.2 Output: condition specific network states
4.9.3 MATLAB implementation (United Signature Algorithm)
4.9.4 http://saturn.med.nyu.edu/%7Ekluger/YeastNetwork/supplementary.html
4.10 Gao F. Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.
4.10.1 MA-Networker algorithm
4.10.2 Data: microarrays, TF sites required?
5. Related methods and papers
5.1 Covert MW. Integrating high-throughput and computational data elucidates bacterial networks.
5.1.1 Very interesting paper on a computational model, but no actual network reconstruction (just E. coli)
5.2 Herrgard MJ. Reconstruction of microbial transcriptional regulatory networks.
5.2.1 Pretty good basic review
5.3 Li H. Dissecting the transcription networks of a cell using computational genomics.
5.3.1 Another good basic review
5.4 Herrgard MJ. Reconciling gene expression data with known genome-scale regulatory network structures.
5.4.1 Verification of described networks via expression data
5.5 Barrett CL. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
5.5.1 Algorithmic design of experiments for network determination
5.6 Nakhleh L. Towards the development of computational tools for evaluating phylogenetic network reconstruction methods.
5.6.1 Qualitative evaluation of reconstruction methods
5.7 Werhli AV. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks.
5.7.1 Method comparison
5.7.2 (for microarrays)
5.7.3 (1) Relevance networks (RNs):
Pairwise association scores independent of the remaining network
5.7.4 (2) graphical Gaussian models (GGMs):
Undirected graphical models with constraint-based inference
5.7.5 (3) Bayesian networks (BNs):
Directed graphical models with score-based inference.
5.7.6 BN > GGM > RN
BN: higher performance cost than GGM
..but pays off for perturbation analysis
5.8 Yu X. Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae.
5.8.1 Inference of interaction between TFs from binding site analysis
About
Title
Methods To Reconstruct Transcriptional Regulatory Networks (Table Created By Benjamin Lang)
Subject
Systems Biology: Transcriptional regulatory network
Company
MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, U.K.
Author
Benjamin Lang and M. Madan Babu
Version
524
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