We present miRSeqNovel, an R based workflow for miRNA sequencing data analysis. miRSeqNovel can process both colorspace (SOLiD) and basespace (Illumina/Solexa) data by different mapping algorithms. It finds differentially expressed miRNAs and gives conservative prediction of novel miRNA candidates with customized parameters. miRSeqNovel is freely available at http://sourceforge.net/projects/mirseq/files.[1]
MicroRNAs are very recently discovered small noncoding RNAs, responsible for negative regulation of gene expression. Members of this endogenous family of small RNA molecules have been found implicated in many genetic disorders. Each microRNA targets tens to hundreds of genes. Experimental validation of target genes is a time- and cost-intensive procedure. Therefore, prediction of microRNA targets is a very important problem in computational biology.
Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.
Cancer classification has been a crucial topic of research in cancer treatment. In the last decade, messenger RNA (mRNA) expression profiles have been widely used to classify different types of cancers. With the discovery of a new class of small non-coding RNAs; known as microRNAs (miRNAs), various studies have shown that the expression patterns of miRNA can also accurately classify human cancers. Therefore, there is a great demand for the development of machine learning approaches to accurately classify various types of cancers using miRNA expression data.
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-seq and RNAcompete, usually suffer from the false negative issue. Here, we develop a deep boosting based machine learning approach, called DeBooster, to accurately model the binding sequence preferences and identify the corresponding binding targets of RBPs from CLIP-seq data.
The interaction of miRNA and lncRNA is known to be important for gene regulations. However, not many computational approaches have been developed to analyse known interactions and predict the unknown ones. Given that there are now more evidences that suggest that lncRNA-miRNA interactions are closely related to their relative expression levels in the form of a titration mechanism, we analyzed the patterns in large-scale expression profiles of known lncRNA-miRNA interactions.
Accurate prediction of sRNA targets plays a key role in determining sRNA functions. Here we introduced two mathematical models, sRNATargetNB and sRNATargetSVM, for prediction of sRNA targets using Nai ve Bayes method and support vector machines (SVM), respectively. The training dataset was composed of 46 positive samples (real sRNA-targets interaction) and 86 negative samples (no interaction between sRNA and targets). The leave-one-out cross-validation (LOOCV) classification accuracy was 91.67% for sRNATargetNB, and 100.00% for sRNATargetSVM.
ESPSearch is a computer program for rapidly identifying nucleic acid or amino acid sequences of any length within any source sequence from promoters to entire genomes to protein libraries. ESPSearch utilizes a user-constructed database to identify many sequences simultaneously, including target sequences with wildcards and mismatches and user-specified patterns of those recognized sequences.
Contamination of a cancer tissue by the surrounding benign (non-cancerous) tissue is a concern for molecular cancer diagnostics. This is because an observed molecular signature will be distorted by the surrounding benign tissue, possibly leading to an incorrect diagnosis. One example is molecular identification of the primary tumor site of metastases because biopsies of metastases typically contain a significant amount of benign tissue.
MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their level of expression, has developed a huge interest in the scientific community. One of the leading technologies for extracting miRNA data from biological samples is the miRNA Affymetrix platform.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
Updates History:
