Mining cancer gene expression databases for latent information on intronic microRNAs.
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ABSTRACT:
SUBMITTER: Monterisi S
PROVIDER: S-EPMC5528658 | biostudies-literature | 2015 Feb
REPOSITORIES: biostudies-literature
Publications
Mining cancer gene expression databases for latent information on intronic microRNAs.
Monterisi Simona S D'Ario Giovanni G Dama Elisa E Rotmensz Nicole N Confalonieri Stefano S Tordonato Chiara C Troglio Flavia F Bertalot Giovanni G Maisonneuve Patrick P Viale Giuseppe G Nicassio Francesco F Vecchi Manuela M Di Fiore Pier Paolo PP Bianchi Fabrizio F
Molecular oncology 20141015 2
Around 50% of all human microRNAs reside within introns of coding genes and are usually co-transcribed. Gene expression datasets, therefore, should contain a wealth of miRNA-relevant latent information, exploitable for many basic and translational research aims. The present study was undertaken to investigate this possibility. We developed an in silico approach to identify intronic-miRNAs relevant to breast cancer, using public gene expression datasets. This led to the identification of a miRNA ...[more]
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Project description:SummaryCurrent challenges in microRNA (miRNA) research are to improve the identification of in vivo mRNA targets and clarify the complex interplay existing between a specific miRNA and multiple biological networks. MiRonTop is an online java web tool that integrates DNA microarrays or high-throughput sequencing data to identify the potential implication of miRNAs on a specific biological system. It allows a rapid characterization of the most pertinent mRNA targets according to several existing miRNA target prediction approaches. It also provides useful representations of the enrichment scores according to the position of the target site along the 3'-UTR, where the contribution of the sites located in the vicinity of the stop codon and of the polyA tail can be clearly highlighted. It provides different graphs of miRNA enrichment associated with up- or down-regulated transcripts and different summary tables about selections of mRNA targets and their functional annotations by Gene Ontology.Availabilityhttp://www.microarray.fr:8080/miRonTop/index.
| S-EPMC2995122 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNA) are an abundant and ubiquitous class of small RNAs that play prominent roles in gene regulation. A significant fraction of miRNA genes reside in the introns of the host genes in the same orientation and are thought to be co-processed from the host gene mRNAs and thus depend on the host gene promoter for their expression. However, several lines of evidence for independent expression of intronic miRNAs exist in the literature but the extent of this independence remains unclear.ResultsWe performed a systematic analysis of genomic regions surrounding intronic miRNAs in the nematode Caenorhabditis elegans and found that, in many cases, there are extended intronic sequences immediately upstream of the miRNAs that are well-conserved between the nematodes. We have generated transcriptional green fluorescent protein reporter fusions in transgenic C. elegans lines and demonstrated that, in all seven investigated cases, the conserved sequences show promoter properties and produce specific expression patterns that are different from the host gene expression patterns. The observed expression patterns are corroborated by the published small RNA sequencing data.ConclusionsOur analysis reveals that the number of intronic miRNAs that do not rely on their host genes for expression is substantially higher than previously appreciated. At least one-third of the same-strand intronic miRNAs in C. elegans posses their own promoters and, thus, could be transcribed independently from their host genes. These findings provide a new insight into the regulation of miRNA genes and will be useful for the analysis of interactions between miRNAs and their host genes.
| S-EPMC2835999 | biostudies-literature
Project description:BackgroundMicroRNAs, post-transcriptional repressors of gene expression, play a pivotal role in gene regulatory networks. They are involved in core cellular processes and their dysregulation is associated to a broad range of human diseases. This paper focus on a minimal microRNA-mediated regulatory circuit, in which a protein-coding gene (host gene) is targeted by a microRNA located inside one of its introns.ResultsAutoregulation via intronic microRNAs is widespread in the human regulatory network, as confirmed by our bioinformatic analysis, and can perform several regulatory tasks despite its simple topology. Our analysis, based on analytical calculations and simulations, indicates that this circuitry alters the dynamics of the host gene expression, can induce complex responses implementing adaptation and Weber's law, and efficiently filters fluctuations propagating from the upstream network to the host gene. A fine-tuning of the circuit parameters can optimize each of these functions. Interestingly, they are all related to gene expression homeostasis, in agreement with the increasing evidence suggesting a role of microRNA regulation in conferring robustness to biological processes. In addition to model analysis, we present a list of bioinformatically predicted candidate circuits in human for future experimental tests.ConclusionsThe results presented here suggest a potentially relevant functional role for negative self-regulation via intronic microRNAs, in particular as a homeostatic control mechanism of gene expression. Moreover, the map of circuit functions in terms of experimentally measurable parameters, resulting from our analysis, can be a useful guideline for possible applications in synthetic biology.
| S-EPMC3534558 | biostudies-literature
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| S-EPMC1794378 | biostudies-literature
Project description:BackgroundWith the biomedical literature continually expanding, searching PubMed for information about specific genes becomes increasingly difficult. Not only can thousands of results be returned, but gene name ambiguity leads to many irrelevant hits. As a result, it is difficult for life scientists and gene curators to rapidly get an overall picture about a specific gene from documents that mention its names and synonyms.ResultsIn this paper, we present eGIFT (http://biotm.cis.udel.edu/eGIFT), a web-based tool that associates informative terms, called iTerms, and sentences containing them, with genes. To associate iTerms with a gene, eGIFT ranks iTerms about the gene, based on a score which compares the frequency of occurrence of a term in the gene's literature to its frequency of occurrence in documents about genes in general. To retrieve a gene's documents (Medline abstracts), eGIFT considers all gene names, aliases, and synonyms. Since many of the gene names can be ambiguous, eGIFT applies a disambiguation step to remove matches that do not correspond to this gene. Another additional filtering process is applied to retain those abstracts that focus on the gene rather than mention it in passing. eGIFT's information for a gene is pre-computed and users of eGIFT can search for genes by using a name or an EntrezGene identifier. iTerms are grouped into different categories to facilitate a quick inspection. eGIFT also links an iTerm to sentences mentioning the term to allow users to see the relation between the iTerm and the gene. We evaluated the precision and recall of eGIFT's iTerms for 40 genes; between 88% and 94% of the iTerms were marked as salient by our evaluators, and 94% of the UniProtKB keywords for these genes were also identified by eGIFT as iTerms.ConclusionsOur evaluations suggest that iTerms capture highly-relevant aspects of genes. Furthermore, by showing sentences containing these terms, eGIFT can provide a quick description of a specific gene. eGIFT helps not only life scientists survey results of high-throughput experiments, but also annotators to find articles describing gene aspects and functions.
| S-EPMC2929241 | biostudies-literature
Project description:BACKGROUND: Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. RESULTS: We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. CONCLUSIONS: Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.
| S-EPMC3376030 | biostudies-literature
Project description:BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.
| S-EPMC1187877 | biostudies-other
Project description:Conditional knockout technology is a powerful tool for investigating the spatiotemporal functions of target genes. However, generation of conditional knockout mice involves complicated breeding programs and considerable time. A recent study has shown that artificially designed microRNAs (amiRNAs), inserted into an intron of the constitutively expressed gene, induce knockdown of the targeted gene in mice, thus creating a simpler method to analyze the functions of target genes in oocytes. Here, to establish an oocyte-specific knockdown system, amiRNA sequences against enhanced green fluorescent protein (EGFP) were knocked into the intronic sites of the Zp3 gene. Knock-in mice were then bred with EGFP transgenic mice. Our results showed that Zp3-derived amiRNA successfully reduced EGFP fluorescence in the oocytes in a size-dependent manner. Importantly, knockdown of EGFP did not occur in somatic cells. Thus, we present our knockdown system as a tool for screening gene functions in mouse oocytes.
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Project description:Identifying the tissues in which a microRNA is expressed could enhance the understanding of the functions, the biological processes, and the diseases associated with that microRNA. However, the mechanisms of microRNA biogenesis and expression remain largely unclear and the identification of the tissues in which a microRNA is expressed is limited. Here, we present a machine learning based approach to predict whether an intronic microRNA show high co-expression with its host gene, by doing so, we could infer the tissues in which a microRNA is high expressed through the expression profile of its host gene. Our approach is able to achieve an accuracy of 79% in the leave-one-out cross validation and 95% on an independent testing dataset. We further estimated our method through comparing the predicted tissue specific microRNAs and the tissue specific microRNAs identified by biological experiments. This study presented a valuable tool to predict the co-expression patterns between human intronic microRNAs and their host genes, which would also help to understand the microRNA expression and regulation mechanisms. Finally, this framework can be easily extended to other species.
| S-EPMC2635472 | biostudies-literature
Project description:MotivationExpression databases, including the Gene Expression Omnibus and ArrayExpress, have experienced significant growth over the past decade and now hold hundreds of thousands of arrays from multiple species. Since most drugs are initially tested on model organisms, the ability to compare expression experiments across species may help identify pathways that are activated in a similar way in humans and other organisms. However, while several methods exist for finding co-expressed genes in the same species as a query gene, looking at co-expression of homologs or arbitrary genes in other species is challenging. Unlike sequence, which is static, expression is dynamic and changes between tissues, conditions and time. Thus, to carry out cross-species analysis using these databases, we need methods that can match experiments in one species with experiments in another species.ResultsTo facilitate queries in large databases, we developed a new method for comparing expression experiments from different species. We define a distance metric between the ranking of orthologous genes in the two species. We show how to solve an optimization problem for learning the parameters of this function using a training dataset of known similar expression experiments pairs. The function we learn outperforms previous methods and simpler rank comparison methods that have been used in the past for single species analysis. We used our method to compare millions of array pairs from mouse and human expression experiments. The resulting matches can be used to find functionally related genes, to hypothesize about biological response mechanisms and to highlight conditions and diseases that are activating similar pathways in both species.AvailabilitySupporting methods, results and a Matlab implementation are available from http://sb.cs.cmu.edu/ExpQ/.
| S-EPMC2944203 | biostudies-literature