Single nucleotide polymorphisms (SNPs) are, together with copy number variation, the primary source of variation in the human genome. SNPs are associated with altered response to drug treatment, susceptibility to disease and other phenotypic variation. Furthermore, during genetic screens for disease-associated mutations in groups of patients and control individuals, the distinction between disease causing mutation and polymorphism is often unclear.
microRNAs are small RNA molecules that inhibit the translation of target genes. microRNA binding sites are located in the untranslated regions as well as in the coding domains. We describe TmiRUSite and TmiROSite scripts developed using python as tools for the extraction of nucleotide sequences for miRNA binding sites with their encoded amino acid residue sequences. The scripts allow for retrieving a set of additional sequences at left and at right from the binding site. The scripts presents all received data in table formats that are easy to analyse further.
Several events of insertional mutagenesis in pre-clinical and clinical gene therapy studies have created intense interest in assessing the genomic insertion profiles of gene therapy vectors. For the construction of such profiles, vector-flanking sequences detected by inverse PCR, linear amplification-mediated-PCR or ligation-mediated-PCR need to be mapped to the host cell's genome and compared to a reference set.
We study microRNA (miRNA) bindings to metastable RNA secondary structures close to minimum free energy conformations in the context of single nucleotide polymorphisms (SNPs) and messenger RNA (mRNA) concentration levels, i.e. whether features of miRNA bindings to metastable conformations could provide additional information supporting the differences in expression levels of the two sequences defined by a SNP.
The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed.
RNA plays a critical role in gene expression and its regulation. RNA binding proteins (RBPs), in turn, are important regulators of RNA. Thanks to the availability of large scale data for RBP binding motifs and in vivo binding sites results in the form of eCLIP experiments, it is now possible to computationally predict RBP binding sites across the whole genome.
We describe MotifMap-RNA, an extension of MotifMap which predicts binding sites for RBP motifs across human and mouse genomes and allows large scale querying of predicted binding sites.
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).
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