1,721,109 research outputs found
miRNA bioinformatics and pathway analysis
No full textThe microRNA (miRNA) mode of action is cooperative in many different ways, leading to an intricate network of coregulations, interactions, and competitive effects on gene target regulations. It is widely known that cells express multiple miRNAs at the same time, a single gene can be targeted by multiple miRNAs and different types of mRNAs competitively act as decoys for miRNAs. Synergistic activity was highlighted by an analysis of the chromosomal distribution of miRNAs in relation to the studied pathology: clustered miRNAs (multiple miRNAs that share the same promoter because they were originated by a long primary transcript) are not only coexpressed but also act in concerted ways. Moreover, experimental evidence demonstrated that, although miRNAs downregulate gene expressions locally, considering a global signaling pathway their function is not necessary repressive but it is dependent on the contextual topology of the pathway. In this scenario, the study of the direct targets of a miRNA, considered as entities isolated from the general context, gives only a partial information regarding miRNA function. To have a holistic perspective of miRNA functions the interactions within the wide and intricate molecular network in which miRNA exploit their action has to be considered. Here we review some of the bioinformatic tools and computational strategies based on pathway and network analyses to infer miRNA functions
A graphical models approach for comparing gene sets
No full textRecently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway
naturally define a gene set. The gene sets are usually identified from a priori
biological knowledge. Nowadays, many bioinformatics resources store such kind
of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes,
among others). In this paper we exploit a multivariate approach, based on graphical
models, to deal with gene sets defined by pathways. Given a sample of microarray
data corresponding to two experimental conditions and a pathway linking some
of the genes, we investigate whether the strength of the relations induced by the
functional links change among the two experimental conditions
Along signal paths: an empirical gene set approach exploiting pathway topology
No full textGene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis clipper, that tries to fill in this gap. clipper im- plements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step clipper selects signifi- cant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expres- sion datasets. Our results demonstrate the efficacy of clipper in the identification of signal transduc- tion paths totally coherent with the biological problem
Impact of probe annotation on the integration of miRNA-mRNA expression profiles for miRNA target detection
Full text linkMicroRNAs (miRNAs) are small non-coding RNAs that mediate gene expression at the post-transcriptional and translational levels by an imperfect binding to target mRNA 3'UTR regions. While the ab-initio computational prediction of miRNA-mRNA interactions still poses significant challenges, it is possible to overcome some of its limitations by carefully integrating into the analysis the paired expression profiles of miRNAs and mRNAs. In this work, we show how the choice of a proper probe annotation for microarray platforms is an essential requirement to achieve good sensitivity in the identification of miRNA-mRNA interactions. We compare the results obtained from the analysis of the same expression profiles using both gene and transcript based custom CDFs that we have developed for a number of different annotations (ENSEMBL, RefSeq, AceView). In all cases, transcript-based annotations clearly improve the effectiveness of data integration and thus provide a more reliable confirmation of computationally predicted miRNA-mRNA interaction
Gene set analysis exploiting the topology of a pathway.
Full text linkBACKGROUND: Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it. RESULTS: We propose a novel gene set analysis specifically designed for gene sets defined by pathways. Such analysis, based on graphical models, explicitly incorporates the dependence structure among genes highlighted by the topology of pathways. The analysis is designed to be used for overall surveillance of changes in a pathway in different experimental conditions. In fact, under different circumstances, not only the expression of the genes in a pathway, but also the strength of their relations may change. The methods resulting from the proposal allow both to test for variations in the strength of the links, and to properly account for heteroschedasticity in the usual tests for differential expression. CONCLUSIONS: The use of graphical models allows a deeper look at the components of the pathway that can be tested separately and compared marginally. In this way it is possible to test single components of the pathway and highlight only those involved in its deregulation
Simplifying amino acid alphabets by means of a branch and bound algorithm and substitution matrices
No full textMOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules
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