Enriching for direct regulatory targets in perturbed gene-expression profiles

Tringe, Susannah G.; Wagner, Andreas; Ruby, Stephanie W.

doi:10.1186/gb-2004-5-4-r29

Cited by 11 publications

(3 citation statements)

References 47 publications

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“…The higher proportion of repeats observed in nucleotide sequences was consistent with the complexity of the organisms we examined. On the other hand, non-coding region variations are suggested to play roles in advanced regulatory functions to affect the expression of coding genes [ 25 ]. Thus, variations in the non-coding region may also have impacts on BDNF availability.…”

Section: Resultsmentioning

confidence: 99%

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

2015

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ObjectivesBrain-derived neurotrophic factor (BDNF) plays important roles in neuronal survival and differentiation; however, the effects of BDNF on mood disorders remain unclear. We investigated BDNF from the perspective of various aspects of systems biology, including its molecular evolution, genomic studies, protein functions, and pathway analysis.MethodsWe conducted analyses examining sequences, multiple alignments, phylogenetic trees and positive selection across 12 species and several human populations. We summarized the results of previous genomic and functional studies of pro-BDNF and mature-BDNF (m-BDNF) found in a literature review. We identified proteins that interact with BDNF and performed pathway-based analysis using large genome-wide association (GWA) datasets obtained for mood disorders.ResultsBDNF is encoded by a highly conserved gene. The chordate BDNF genes exhibit an average of 75% identity with the human gene, while vertebrate orthologues are 85.9%-100% identical to human BDNF. No signs of recent positive selection were found. Associations between BDNF and mood disorders were not significant in most of the genomic studies (e.g., linkage, association, gene expression, GWA), while relationships between serum/plasma BDNF level and mood disorders were consistently reported. Pro-BDNF is important in the response to stress; the literature review suggests the necessity of studying both pro- and m-BDNF with regard to mood disorders. In addition to conventional pathway analysis, we further considered proteins that interact with BDNF (I-Genes) and identified several biological pathways involved with BDNF or I-Genes to be significantly associated with mood disorders.ConclusionsSystematically examining the features and biological pathways of BDNF may provide opportunities to deepen our understanding of the mechanisms underlying mood disorders.

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Section: Resultsmentioning

confidence: 99%

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

2015

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“…Self-regulation could be deduced by using chromatin immunoprecipitation [ 29 ]. Combinatorial gene regulations could be deduced by using the expression profiles of multiple gene deletion mutants [ 30 ]. Synthetic genetic arrays can systematically construct a collection of double-gene deletion mutants [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Kyoda

Baba

Kitano

et al. 2011

Source Code Biol Med

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BackgroundWe previously developed the DBRF-MEGN (difference-based regulation finding-minimum equivalent gene network) method, which deduces the most parsimonious signed directed graphs (SDGs) consistent with expression profiles of single-gene deletion mutants. However, until the present study, we have not presented the details of the method's algorithm or a proof of the algorithm.ResultsWe describe in detail the algorithm of the DBRF-MEGN method and prove that the algorithm deduces all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.ConclusionsThe DBRF-MEGN method provides all of the exact solutions of the most parsimonious SDGs consistent with expression profiles of gene deletion mutants.

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“…Large-scale network analysis of indirect regulatory interactions in yeast was recently studied in [4,5,6]. These works focused on the classification of regulations as either direct or indirect and subsequently pruning of indirect regulations.…”

Section: Introductionmentioning

confidence: 99%

Prediction and verification of indirect interactions in densely interconnected regulatory networks

Yan,

Maslov,

Mazo

et al. 2007

Preprint

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We develop a matrix-based approach to predict and verify indirect interactions in gene and protein regulatory networks. It is based on the approximate transitivity of indirect regulations (e.g. A → B and B → C often implies that A → C) and optimally takes into account the length of a cascade and signs of intermediate interactions. Our method is at its most powerful when applied to large and densely interconnected networks. It successfully predicts both the yet unknown indirect regulations, as well as the sign (activation or repression) of already known ones. The reliability of sign predictions was calibrated using the gold-standard sets of positive and negative interactions. We fine-tuned the parameters of our algorithm by maximizing the area under the Receiver Operating Characteristic (ROC) curve. We then applied the optimized algorithm to large literature-derived networks of all direct and indirect regulatory interactions in several model organisms (Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster).

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Enriching for direct regulatory targets in perturbed gene-expression profiles

Abstract: This study presents an algorithm to infer direct regulatory relationships using gene expression profiles from cells in which individual genes are deleted or overexpressed.

Cited by 11 publications

References 47 publications

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

Explore the Features of Brain-Derived Neurotrophic Factor in Mood Disorders

A proof of the DBRF-MEGN method, an algorithm for deducing minimum equivalent gene networks

Prediction and verification of indirect interactions in densely interconnected regulatory networks

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