Construction, visualisation, and clustering of transcription networks from microarray expression data.

Network analysis transcends conventional pairwise approaches to data analysis as the context of components in a network graph can be taken into account. Such approaches are increasingly being applied to genomics data, where functional linkages are used to connect genes or proteins. However, while microarray gene expression datasets are now abundant and of high quality, few approaches have been developed for analysis of such data in a network context. We present a novel approach for 3-D visualisation and analysis of transcriptional networks generated from microarray data. These networks consist of nodes representing transcripts connected by virtue of their expression profile similarity across multiple conditions. Analysing genome-wide gene transcription across 61 mouse tissues, we describe the unusual topography of the large and highly structured networks produced, and demonstrate how they can be used to visualise, cluster, and mine large datasets. This approach is fast, intuitive, and versatile, and allows the identification of biological relationships that may be missed by conventional analysis techniques. This work has been implemented in a freely available open-source application named BioLayout Express(3D)

MINER: exploratory analysis of gene interaction networks by machine learning from expression data

<p>Abstract</p> <p>Background</p> <p>The reconstruction of gene regulatory networks from high-throughput "omics" data has become a major goal in the modelling of living systems. Numerous approaches have been proposed, most of which attempt only "one-shot" reconstruction of the whole network with no intervention from the user, or offer only simple correlation analysis to infer gene dependencies.</p> <p>Results</p> <p>We have developed MINER (Microarray Interactive Network Exploration and Representation), an application that combines multivariate non-linear tree learning of individual gene regulatory dependencies, visualisation of these dependencies as both trees and networks, and representation of known biological relationships based on common Gene Ontology annotations. MINER allows biologists to explore the dependencies influencing the expression of individual genes in a gene expression data set in the form of decision, model or regression trees, using their domain knowledge to guide the exploration and formulate hypotheses. Multiple trees can then be summarised in the form of a gene network diagram. MINER is being adopted by several of our collaborators and has already led to the discovery of a new significant regulatory relationship with subsequent experimental validation.</p> <p>Conclusion</p> <p>Unlike most gene regulatory network inference methods, MINER allows the user to start from genes of interest and build the network gene-by-gene, incorporating domain expertise in the process. This approach has been used successfully with RNA microarray data but is applicable to other quantitative data produced by high-throughput technologies such as proteomics and "next generation" DNA sequencing.</p

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.&#xd;&#xa;&#xd;&#xa

Current State-of-the-Art Bioinformatics Methods in Alzheimer's Disease Studies

Alzheimeri tõbi on kõige levinum dementsuse vorm ning see esineb ülemaailmselt vanematel inimestel. Uuringud keskenduvad põhjuste ja ravi leidmisele. Käsitletavad meetodid põhinevad geeniekspressiooni andmetel. Erinevalt avalduvad geenid eraldatakse ning kasutatakse edasistes analüüsides.Käesolev bakalaureusetöö pakub ülevaadet Alzheimeri tõve uuringutes kasutatavatest bioinformaatilistest meetoditest. Tuleneval mitmekülgsete meetodite hulgal põhinev analüüs kirjeldab lähenemisi lühidalt ning toob välja näiteid valitud artiklite hulgast.This thesis provides an overview of the state-of-the-art methods currently used in studying Alzheimer's disease.\\The first section contains background information relevant to the better understanding of the subsequent analysis section. The section is divided into two, providing descriptions of main biological and bioinformatical ideas and methods.\\The second section contains the analysis of a selected subset of articles and provides a case study of a single chosen article. The analysis is split into parts relative to the studies conducted and compares the methods described.\\The resulting overview of the articles can be used a short introduction of the current state in research focused on the better understanding of the neurodegenerative disease

Annotation of gene function in citrus using gene expression information and co-expression networks

Background The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed Results We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit Conclusions Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citru

Novel methods for constructing, combining and comparing co-expression networks: Towards uncovering the molecular basis of human cognition

Network analyses, such as gene co-expression networks are an important approach for the systems-level study of biological data. For example, understanding patterns of \linebreak co-regulation in mental disorders can contribute to the development of new therapies and treatments. In a gene regulatory process a particular TF or ncRNA can up- or down-regulate other genes, therefore it is important to explicitly consider both positive and negative interactions. Although exists a variety of software and libraries for constructing and investigating such networks, none considers the sign of interaction. It is also required that the represented networks have high accuracy, where the interactions found have to be relevant and not found by chance or background noise. Another issue derived from building co-expression networks is the reproducibility of those. When constructing independent networks for the same phenotype, though, using different expression datasets, the output network can be remarkably distinct due to biological or technical noise in the data. However, most of the times the interest is not only to characterise a network but to compare its features to others. A series of questions arise from understanding phenotypes using co-expression networks: i) how to construct highly accurate networks; ii) how to combine multiple networks derived from different platforms; iii) how to compare multiple networks. For answering those questions, i) I improved the wTO method to construct highly accurate networks, where now each interaction in a network receives a probability. This method showed to be much more efficient in finding correct interactions than other well-known methods; ii) I developed a method that is able to combine multiple networks into one building a CN. This method enables the correction for background noise; iii) I developed a completely novel method for the comparison of multiple co-expression networks, CoDiNA. This method identifies genes specific to at least one network. It is natural that after associating genes to phenotypes, an inference whether those genes are enriched for a particular disorder is needed. I also present here a tool, RichR, that enables enrichment analysis and background correction. I applied the methods proposed here in two important studies. In the first one, the aim was to understand the neurogenesis process and how certain genes would affect it. The combination of the methods shown here pointed one particular TF, ZN787, as playing an important role in this process. Moreover, the application of this toolset to networks derived from brain samples of individuals with cognitive disorders identified genes and network connections that are specific to certain disorders, but also found an overlap between neurodegenerative disorders and brain development and between evolutionary changes and psychological disorders. CoDiNA also pointed out that there are genes involved in those disorders that are not only human-specific

Construction of a large scale integrated map of macrophage pathogen recognition and effector systems

<p>Abstract</p> <p>Background</p> <p>In an effort to better understand the molecular networks that underpin macrophage activation we have been assembling a map of relevant pathways. Manual curation of the published literature was carried out in order to define the components of these pathways and the interactions between them. This information has been assembled into a large integrated directional network and represented graphically using the modified Edinburgh Pathway Notation (mEPN) scheme.</p> <p>Results</p> <p>The diagram includes detailed views of the toll-like receptor (TLR) pathways, other pathogen recognition systems, NF-kappa-B, apoptosis, interferon signalling, MAP-kinase cascades, MHC antigen presentation and proteasome assembly, as well as selected views of the transcriptional networks they regulate. The integrated pathway includes a total of 496 unique proteins, the complexes formed between them and the processes in which they are involved. This produces a network of 2,170 nodes connected by 2,553 edges.</p> <p>Conclusions</p> <p>The pathway diagram is a navigable visual aid for displaying a consensus view of the pathway information available for these systems. It is also a valuable resource for computational modelling and aid in the interpretation of functional genomics data. We envisage that this work will be of value to those interested in macrophage biology and also contribute to the ongoing Systems Biology community effort to develop a standard notation scheme for the graphical representation of biological pathways.</p

An Always Correlated gene expression landscape for ovine skeletal muscle, lessons learnt from comparison with an “equivalent” bovine landscape

BACKGROUND: We have recently described a method for the construction of an informative gene expression correlation landscape for a single tissue, longissimus muscle (LM) of cattle, using a small number (less than a hundred) of diverse samples. Does this approach facilitate interspecies comparison of networks? FINDINGS: Using gene expression datasets from LM samples from a single postnatal time point for high and low muscling sheep, and from a developmental time course (prenatal to postnatal) for normal sheep and sheep exhibiting the Callipyge muscling phenotype gene expression correlations were calculated across subsets of the data comparable to the bovine analysis. An “Always Correlated” gene expression landscape was constructed by integrating the correlations from the subsets of data and was compared to the equivalent landscape for bovine LM muscle. Whilst at the high level apparently equivalent modules were identified in the two species, at the detailed level overlap between genes in the equivalent modules was limited and generally not significant. Indeed, only 395 genes and 18 edges were in common between the two landscapes. CONCLUSIONS: Since it is unlikely that the equivalent muscles of two closely related species are as different as this analysis suggests, within tissue gene expression correlations appear to be very sensitive to the samples chosen for their construction, compounded by the different platforms used. Thus users need to be very cautious in interpretation of the differences. In future experiments, attention will be required to ensure equivalent experimental designs and use cross-species gene expression platform to enable the identification of true differences between different species