Systematic identification of functional plant modules through the integration of complementary data sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

EZH2 RIP-seq Identifies Tissue-specific Long Non-coding RNAs.

BackgroundPolycomb Repressive Complex 2 (PRC2) catalyzes histone methylation at H3 Lys27, and plays crucial roles during development and diseases in numerous systems. Its catalytic subunit EZH2 represents a key nuclear target for long non-coding RNAs (lncRNAs) that emerging to be a novel class of epigenetic regulator and participate in diverse cellular processes. LncRNAs are characterized by high tissue-specificity; however, little is known about the tissue profile of the EZH2- interacting lncRNAs.ObjectiveHere we performed a global screening for EZH2-binding lncRNAs in tissues including brain, lung, heart, liver, kidney, intestine, spleen, testis, muscle and blood by combining RNA immuno- precipitation and RNA sequencing. We identified 1328 EZH2-binding lncRNAs, among which 470 were shared in at least two tissues while 858 were only detected in single tissue. An RNA motif with specific secondary structure was identified in a number of lncRNAs, albeit not in all EZH2-binding lncRNAs. The EZH2-binding lncRNAs fell into four categories including intergenic lncRNA, antisense lncRNA, intron-related lncRNA and promoter-related lncRNA, suggesting diverse regulations of both cis and trans-mechanisms. A promoter-related lncRNA Hnf1aos1 bound to EZH2 specifically in the liver, a feature same as its paired coding gene Hnf1a, further confirming the validity of our study. In addition to the well known EZH2-binding lncRNAs like Kcnq1ot1, Gas5, Meg3, Hotair and Malat1, majority of the lncRNAs were firstly reported to be associated with EZH2.ConclusionOur findings provide a profiling view of the EZH2-interacting lncRNAs across different tissues, and suggest critical roles of lncRNAs during cell differentiation and maturation

Doctor of Philosophy

dissertationSynthetic biology is a new field in which engineers, biologists, and chemists are working together to transform genetic engineering into an advanced engineering discipline, one in which the design and construction of novel genetic circuits are made possible through the application of engineering principles. This dissertation explores two engineering strategies to address the challenges of working with genetic technology, namely the development of standards for describing genetic components and circuits at separate yet connected levels of detail and the use of Genetic Design Automation (GDA) software tools to simplify and speed up the process of optimally designing genetic circuits. Its contributions to the field of synthetic biology include (1) a proposal for the next version of the Synthetic Biology Open Language (SBOL), an existing standard for specifying and exchanging genetic designs electronically, and (2) a GDA work ow that enables users of the software tool iBioSim to create an abstract functional specication, automatically select genetic components that satisfy the specication from a design library, and compose the selected components into a standardized genetic circuit design for subsequent analysis and physical construction. Ultimately, this dissertation demonstrates how existing techniques and concepts from electrical and computer engineering can be adapted to overcome the challenges of genetic design and is an example of what is possible when working with publicly available standards for genetic design

Maximal information component analysis: a novel non-linear network analysis method.

BackgroundNetwork construction and analysis algorithms provide scientists with the ability to sift through high-throughput biological outputs, such as transcription microarrays, for small groups of genes (modules) that are relevant for further research. Most of these algorithms ignore the important role of non-linear interactions in the data, and the ability for genes to operate in multiple functional groups at once, despite clear evidence for both of these phenomena in observed biological systems.ResultsWe have created a novel co-expression network analysis algorithm that incorporates both of these principles by combining the information-theoretic association measure of the maximal information coefficient (MIC) with an Interaction Component Model. We evaluate the performance of this approach on two datasets collected from a large panel of mice, one from macrophages and the other from liver by comparing the two measures based on a measure of module entropy, Gene Ontology (GO) enrichment, and scale-free topology (SFT) fit. Our algorithm outperforms a widely used co-expression analysis method, weighted gene co-expression network analysis (WGCNA), in the macrophage data, while returning comparable results in the liver dataset when using these criteria. We demonstrate that the macrophage data has more non-linear interactions than the liver dataset, which may explain the increased performance of our method, termed Maximal Information Component Analysis (MICA) in that case.ConclusionsIn making our network algorithm more accurately reflect known biological principles, we are able to generate modules with improved relevance, particularly in networks with confounding factors such as gene by environment interactions

Multiconstrained gene clustering based on generalized projections

<p>Abstract</p> <p>Background</p> <p>Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem.</p> <p>Results</p> <p>We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods.</p> <p>Conclusions</p> <p>The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions.</p

Polar Mapper: a computational tool for integrated visualization of protein interaction networks and mRNA expression data

Polar Mapper is a computational application for exposing the architecture of protein interaction networks. It facilitates the system-level analysis of mRNA expression data in the context of the underlying protein interaction network. Preliminary analysis of a human protein interaction network and comparison of yeast oxidative stress and heat shock gene expression responses are addressed as case studies