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mirExplorer

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

microRNAs (miRNAs) represent an abundant group of small regulatory non-coding RNAs in eukaryotes. The emergence of Next-generation sequencing (NGS) technologies has allowed the systematic detection of small RNAs (sRNAs) and de novo sequencing of genomes quickly and with low cost. As a result, there is an increased need to develop fast miRNA prediction tools to annotate miRNAs from various organisms with a high level of accuracy, using the genome sequence or the NGS data. Several miRNA predictors have been proposed to achieve this purpose.

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miRNA-SNiPer

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNA) are a class of non-coding RNAs important in posttranscriptional regulation of target genes. Previous studies have proven that genetic variability of miRNA genes (miR-SNP) has an impact on phenotypic variation and disease susceptibility in human, mice and some livestock species. MicroRNA gene polymorphisms could therefore represent biomarkers for phenotypic traits also in other animal species.

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Bioinformatics Resource Manager

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users' data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability.

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NRDR

Submitted by ChenLiang on Thu, 10/20/2016 - 20:36

Large-scale transcriptome projects have shown that the number of RNA transcripts not coding for proteins (non-coding RNAs) is much larger than previously recognized. High-throughput technologies, coupled with bioinformatics approaches, have produced increasing amounts of data, highlighting the role of non-coding RNAs (ncRNAs) in biological processes. Data generated by these studies include diverse non-coding RNA classes from organisms of different kingdoms, which were obtained using different experimental and computational assays.

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miRdentify

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

During recent years, miRNAs have been shown to play important roles in the regulation of gene expression. Accordingly, much effort has been put into the discovery of novel uncharacterized miRNAs in various organisms. miRNAs are structurally defined by a hairpin-loop structure recognized by the two-step processing apparatus, Drosha and Dicer, necessary for the production of mature ~ 22-nucleotide miRNA guide strands.

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miRNA-deKmer

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

The microRNA (miRNA), a small non-coding RNA molecule, plays an important role in transcriptional and post-transcriptional regulation of gene expression. Its abnormal expression, however, has been observed in many cancers and other disease states, implying that the miRNA molecules are also deeply involved in these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops).

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MirID

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs play important roles in most biological processes, including cell proliferation, tissue differentiation, and embryonic development, among others. They originate from precursor transcripts (pre-miRNAs), which contain phylogenetically conserved stem-loop structures. An important bioinformatics problem is to distinguish the pre-miRNAs from pseudo pre-miRNAs that have similar stem-loop structures. We present here a novel method for tackling this bioinformatics problem.

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HTSmix

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.

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Automatic learning of pre-miRNAs from different species

Submitted by ChenLiang on Thu, 04/06/2017 - 17:26

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower.

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SAMMate

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.

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