2,090 research outputs found

    PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers

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    BACKGROUND: Long thought "relics" of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene-parent gene relationships without leveraging other homologous genes/pseudogenes. RESULTS: We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four "flavors" of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a "one stop shop" for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. CONCLUSIONS: Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike

    miRPathDB 2.0: a novel release of the miRNA Pathway Dictionary Database

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    Since the initial release of miRPathDB, tremendous progress has been made in the field of microRNA (miRNA) research. New miRNA reference databases have emerged, a vast amount of new miRNA candidates has been discovered and the number of experimentally validated target genes has increased considerably. Hence, the demand for a major upgrade of miRPathDB, including extended analysis functionality and intuitive visualizations of query results has emerged. Here, we present the novel release 2.0 of the miRNA Pathway Dictionary Database (miRPathDB) that is freely accessible at https://mpd.bioinf.uni-sb.de/. miRPathDB 2.0 comes with a ten-fold increase of pre-processed data. In total, the updated database provides putative associations between 27 452 (candidate) miRNAs, 28 352 targets and 16 833 pathways for Homo sapiens, as well as interactions of 1978 miRNAs, 24 898 targets and 6511 functional categories for Mus musculus. Additionally, we analyzed publications citing miRPathDB to identify common use-cases and further extensions. Based on this evaluation, we added new functionality for interactive visualizations and down-stream analyses of bulk queries. In summary, the updated version of miRPathDB, with its new custom-tailored features, is one of the most comprehensive and advanced resources for miRNAs and their target pathways

    iGPSe: A Visual Analytic System for Integrative Genomic Based Cancer Patient Stratification

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    Background: Cancers are highly heterogeneous with different subtypes. These subtypes often possess different genetic variants, present different pathological phenotypes, and most importantly, show various clinical outcomes such as varied prognosis and response to treatment and likelihood for recurrence and metastasis. Recently, integrative genomics (or panomics) approaches are often adopted with the goal of combining multiple types of omics data to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Results: In this paper we present a visual analytic system called Interactive Genomics Patient Stratification explorer (iGPSe) which significantly reduces the computing burden for biomedical researchers in the process of exploring complicated integrative genomics data. Our system integrates unsupervised clustering with graph and parallel sets visualization and allows direct comparison of clinical outcomes via survival analysis. Using a breast cancer dataset obtained from the The Cancer Genome Atlas (TCGA) project, we are able to quickly explore different combinations of gene expression (mRNA) and microRNA features and identify potential combined markers for survival prediction. Conclusions: Visualization plays an important role in the process of stratifying given population patients. Visual tools allowed for the selection of possibly features across various datasets for the given patient population. We essentially made a case for visualization for a very important problem in translational informatics.Comment: BioVis 2014 conferenc

    Computational frameworks for microRNA functional analysis of inter-kingdom and indirect targeting.

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    Genes are DNA sequences that encode the information needed to synthesize molecules necessary for the function of the cell. Some genes are called protein-coding genes because they have the code required to manufacture proteins. The expression of a certain gene means its product (protein) is produced. Although some genes are not protein-coding, they regulate the gene expression of other protein-coding genes. Of these, microRNAs (miRNAs) are small RNA molecules that inhibit the expression of other genes by binding to their mRNA transcripts. miRNAs have been shown to be linked to several biological processes like development and diseases like cancer. Recently, researchers have hypothesized that miRNAs are involved in the regulation of the expression of genes from other species. Although tools to predict miRNA target genes are available in the case when miRNAs and target genes belong to the same species, to our knowledge there are no available tools to predict inter-kingdom miRNA target genes (miRNA and target genes belong to two different kingdoms). To address this limitation, we developed an efficient tool to predict potential gut bacterial genes targeted by miRNAs from edible plants. We successfully predicted ginger miRNAs that target two genes from a gut bacterial strain called Lactobacillus rhamnosus GG. To maintain the efficiency of our tool while using a larger number of miRNAs and bacterial strains, we used a hash-table to index the sequences of bacterial genes. To predict the function of a miRNA, we start by compiling the list of direct target genes (ones with binding sites) and then we search for biological process in which these genes are enriched. This approach does not include other genes affected by the miRNA but do not necessarily have a physical binding site (indirect targets). An example of an indirect target is the gene that doesn’t have a binding site and is regulated through other direct targets like transcription factors. To overcome this limitation, we developed miRinGO an interactive web application to include these indirect targets in the functional analysis. Our approach showed better performance compared to the existing approach in predicting biological processes known to be targeted by certain miRNAs

    Emerging concepts of miRNA therapeutics: from cells to clinic

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    MicroRNAs (miRNAs) are very powerful genetic regulators, as evidenced by the fact that a single miRNA can direct entire cellular pathways via interacting with a broad spectrum of target genes. This property renders miRNAs as highly interesting therapeutic tools to restore cell functions that are altered as part of a disease phenotype. However, this strength of miRNAs is also a weakness because their cellular effects are so numerous that off-target effects can hardly be avoided. In this review, we point out the main challenges and the strategies to specifically address the problems that need to be surmounted in the push toward a therapeutic application of miRNAs. Particular emphasis is given to approaches that have already found their way into clinical studies

    A new advance in alternative splicing databases: from catalogue to detailed analysis of regulation of expression and function of human alternative splicing variants

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    <p>Abstract</p> <p>Background</p> <p>Most human genes produce several transcripts with different exon contents by using alternative promoters, alternative polyadenylation sites and alternative splice sites. Much effort has been devoted to describing known gene transcripts through the development of numerous databases. Nevertheless, owing to the diversity of the transcriptome, there is a need for interactive databases that provide information about the potential function of each splicing variant, as well as its expression pattern.</p> <p>Description</p> <p>After setting up a database in which human and mouse splicing variants were compiled, we developed tools (1) to predict the production of protein isoforms from these transcripts, taking account of the presence of open reading frames and mechanisms that could potentially eliminate transcripts and/or inhibit their translation, i.e. nonsense-mediated mRNA decay and microRNAs; (2) to support studies of the regulation of transcript expression at multiple levels, including transcription and splicing, particularly in terms of tissue specificity; and (3) to assist in experimental analysis of the expression of splicing variants. Importantly, analyses of all features from transcript metabolism to functional protein domains were integrated in a highly interactive, user-friendly web interface that allows the functional and regulatory features of gene transcripts to be assessed rapidly and accurately.</p> <p>Conclusion</p> <p>In addition to identifying the transcripts produced by human and mouse genes, fast DB <url>http://www.fast-db.com</url> provides tools for analyzing the putative functions of these transcripts and the regulation of their expression. Therefore, fast DB has achieved an advance in alternative splicing databases by providing resources for the functional interpretation of splicing variants for the human and mouse genomes. Because gene expression studies are increasingly employed in clinical analyses, our web interface has been designed to be as user-friendly as possible and to be readily searchable and intelligible at a glance by the whole biomedical community.</p

    starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data

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    MicroRNAs (miRNAs) represent an important class of small non-coding RNAs (sRNAs) that regulate gene expression by targeting messenger RNAs. However, assigning miRNAs to their regulatory target genes remains technically challenging. Recently, high-throughput CLIP-Seq and degradome sequencing (Degradome-Seq) methods have been applied to identify the sites of Argonaute interaction and miRNA cleavage sites, respectively. In this study, we introduce a novel database, starBase (sRNA target Base), which we have developed to facilitate the comprehensive exploration of miRNA–target interaction maps from CLIP-Seq and Degradome-Seq data. The current version includes high-throughput sequencing data generated from 21 CLIP-Seq and 10 Degradome-Seq experiments from six organisms. By analyzing millions of mapped CLIP-Seq and Degradome-Seq reads, we identified ∼1 million Ago-binding clusters and ∼2 million cleaved target clusters in animals and plants, respectively. Analyses of these clusters, and of target sites predicted by 6 miRNA target prediction programs, resulted in our identification of approximately 400 000 and approximately 66 000 miRNA-target regulatory relationships from CLIP-Seq and Degradome-Seq data, respectively. Furthermore, two web servers were provided to discover novel miRNA target sites from CLIP-Seq and Degradome-Seq data. Our web implementation supports diverse query types and exploration of common targets, gene ontologies and pathways. The starBase is available at http://starbase.sysu.edu.cn/
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