We introduce a biophysical model of miRNA-target interaction and infer its parameters from Argonaute 2 cross-linking and immunoprecipitation data. We show that a substantial fraction of human miRNA target sites are noncanonical and that predicted target-site affinity correlates well with the extent of target destabilization. Our model provides a rigorous biophysical approach to miRNA target identification beyond ad hoc miRNA seed-based methods.[1]
Unlike tRNAs and microRNAs, both classes of snoRNAs, which direct two distinct types of chemical modifications of uracil residues, have proved to be surprisingly difficult to find in genomic sequences. Most computational approaches so far have explicitly used the fact that snoRNAs predominantly target ribosomal RNAs and spliceosomal RNAs. The target is specified by a short stretch of sequence complementarity between the snoRNA and its target.
MicroRNAs (miRNAs) are a class of endogenes derived from a precursor (pre-miRNA) and involved in post-transcriptional regulation. Experimental identification of novel miRNAs is difficult because they are often transcribed under specific conditions and cell types. Several computational methods were developed to detect new miRNAs starting from known ones or from deep sequencing data, and to validate their pre-miRNAs.
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in animals and plants. Comparative genomic computational methods have been developed to predict new miRNAs in worms, flies, and humans. Here, we present a novel single genome approach for the detection of miRNAs in Arabidopsis thaliana. This was initiated by producing a candidate miRNA-target data set using an algorithm called findMiRNA, which predicts potential miRNAs within candidate precursor sequences that have corresponding target sites within transcripts.
MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing.
MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample.
MicroRNAs (miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes.
RNA interference (RNAi) has become a powerful tool for genetic screening in Drosophila. At the Drosophila RNAi Screening Center (DRSC), we are using a library of over 21,000 double-stranded RNAs targeting known and predicted genes in Drosophila. This library is available for the use of visiting scientists wishing to perform full-genome RNAi screens.
Deciphering heterogeneous cellular networks with embedded modules is a great challenge of current systems biology. Experimental and computational studies construct complex networks of molecules that describe various aspects of the cell such as transcriptional regulation, protein interactions and metabolism. Groups of interacting genes and proteins reflect network modules that potentially share regulatory mechanisms and relate to common function. Here, we present GraphWeb, a public web server for biological network analysis and module discovery.
Standard DNA alignment programs are inadequate to manage the data produced by new generation DNA sequencers. To answer this problem, we developed PASS with the objective of improving execution time and sensitivity when compared with other available programs. PASS performs fast gapped and ungapped alignments of short DNA sequences onto a reference DNA, typically a genomic sequence. It is designed to handle a huge amount of reads such as those generated by Solexa, SOLiD or 454 technologies.
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:
