miRNeye
MicroRNAs (miRNAs) are key regulators of biological processes. To define miRNA function in the eye, it is essential to determine a high-resolution profile of their spatial and temporal distribution.
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Expression Profiles
RandA
Non-coding RNAs (ncRNA) account for a large portion of the transcribed genomic output. This diverse family of untranslated RNA molecules play a crucial role in cellular function. The use of 'deep sequencing' technology (also known as 'next generation sequencing') to infer transcript expression levels in general, and ncRNA specifically, is becoming increasingly common in molecular and clinical laboratories.
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iSRAP
Small non-coding RNAs have been significantly recognized as the key modulators in many biological processes, and are emerging as promising biomarkers for several diseases. These RNA species are transcribed in cells and can be packaged in extracellular vesicles, which are small vesicles released from many biotypes, and are involved in intercellular communication.
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GBM-BioDP
Validation of clinical biomarkers and response to therapy is a challenging topic in cancer research. An important source of information for virtual validation is the datasets generated from multi-center cancer research projects such as The Cancer Genome Atlas project (TCGA). These data enable investigation of genetic and epigenetic changes responsible for cancer onset and progression, response to cancer therapies, and discovery of the molecular profiles of various cancers.
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LNCediting
RNA editing is a widespread post-transcriptional mechanism that can make a single base change on specific nucleotide sequence in an RNA transcript. RNA editing events can result in missense codon changes and modulation of alternative splicing in mRNA, and modification of regulatory RNAs and their binding sites in noncoding RNAs. Recent computational studies accurately detected more than 2 million A-to-I RNA editing sites from next-generation sequencing (NGS).
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TRiP
microRNAs (miRNAs) are endogenous short RNAs that mediate vast networks of post-transcriptional gene regulation. Although computational searches and experimental profiling provide evidence for hundreds of functional targets for individual miRNAs, such data rarely provide clear insight into the phenotypic consequences of manipulating miRNAs in vivo. We describe a genome-wide collection of 165 Drosophila miRNA transgenes and find that a majority induced specific developmental defects, including phenocopies of mutants in myriad cell-signaling and patterning genes.
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OvMark
Ovarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.
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gbCRC
Colorectal cancer (CRC) is a cancer of growing incidence that associates with a high mortality rate worldwide. There is a poor understanding of the heterogeneity of CRC with regard to causative genetic mutations and gene regulatory mechanisms. Previous studies have identified several susceptibility genes in small-scale experiments. However, the information has not been comprehensively and systematically compiled and interpreted. In this study, we constructed the gbCRC, the first literature-based gene resource for investigating CRC-related human genes.
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expmicro
MicroRNAs (miRNAs) are single-stranded non-coding RNAs shown to plays important regulatory roles in a wide range of biological processes and diseases. The functions and regulatory mechanisms of most of miRNAs are still poorly understood in part because of the difficulty in identifying the miRNA regulatory targets. To this end, computational methods have evolved as important tools for genome-wide target screening.
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MCMG
MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power.
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