Dataset Information

Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci.

ABSTRACT: MOTIVATION:miRNAs are potent regulators of gene expression and modulate multiple cellular processes in physiology and pathology. Deregulation of miRNAs expression has been found in various cancer types, thus, miRNAs may be potential targets for cancer therapy. However, the mechanisms through which miRNAs are regulated in cancer remain unclear. Therefore, the identification of transcriptional factor-miRNA crosstalk is one of the most update aspects of the study of miRNAs regulation. RESULTS:In the present study we describe the development of a fast and user-friendly software, named infinity, able to find the presence of DNA matrices, such as binding sequences for transcriptional factors, on ~65kb (kilobase) of 939 human miRNA genomic sequences, simultaneously. Of note, the power of this software has been validated in vivo by performing chromatin immunoprecipitation assays on a subset of new in silico identified target sequences (CCAAT) for the transcription factor NF-Y on colon cancer deregulated miRNA loci. Moreover, for the first time, we have demonstrated that NF-Y, through its CCAAT binding activity, regulates the expression of miRNA-181a, -181b, -21, -17, -130b, -301b in colon cancer cells. CONCLUSIONS:The infinity software that we have developed is a powerful tool to underscore new TF/miRNA regulatory networks. AVAILABILITY AND IMPLEMENTATION:Infinity was implemented in pure Java using Eclipse framework, and runs on Linux and MS Windows machine, with MySQL database. The software is freely available on the web at https://github.com/bio-devel/infinity. The website is implemented in JavaScript, PHP and HTML with all major browsers supported.

SUBMITTER: Falcone E

PROVIDER: S-EPMC4833383 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci.

Falcone Emmanuela E Grandoni Luca L Garibaldi Francesca F Manni Isabella I Filligoi Giancarlo G Piaggio Giulia G Gurtner Aymone A

PloS one 20160415 4

<h4>Motivation</h4>miRNAs are potent regulators of gene expression and modulate multiple cellular processes in physiology and pathology. Deregulation of miRNAs expression has been found in various cancer types, thus, miRNAs may be potential targets for cancer therapy. However, the mechanisms through which miRNAs are regulated in cancer remain unclear. Therefore, the identification of transcriptional factor-miRNA crosstalk is one of the most update aspects of the study of miRNAs regulation.<h4>Re ...[more]

PMID: 27082112

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Project description:The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Dataset Information

Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci.

Publications

Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci.

Similar Datasets

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