Similarity clustering of next-generation sequences (NGS) is an important computational problem to study the population sizes of DNA/RNA molecules and to reduce the redundancies in NGS data. Currently, most sequence clustering algorithms are limited by their speed and scalability, and thus cannot handle data with tens of millions of reads.
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.
MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs.
Chemical modifications have been extensively exploited to circumvent shortcomings in therapeutic applications of small interfering RNAs (siRNAs). However, experimental designing and testing of these siRNAs or chemically modified siRNAs (cm-siRNAs) involves enormous resources. Therefore, in-silico intervention in designing cm-siRNAs would be of utmost importance. We developed SMEpred workbench to predict the efficacy of normal siRNAs as well as cm-siRNAs using 3031 heterogeneous cm-siRNA sequences from siRNAmod database.
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.
In silico generated search for microRNAs (miRNAs) has been driven by methods compiling structural features of the miRNA precursor hairpin, as well as to some degree combining this with the analysis of RNA-seq profiles for which the miRNA typically leave the drosha/dicer fingerprint of 1-2 ~22 nt blocks of reads corresponding to the mature and star miRNA. In complement to the previous methods, we present a study where we systematically exploit these patterns of read profiles.
MicroRNAs (miRNAs) are important regulators of gene expression. The recent advances in high-throughput sequencing (HTS) technique have greatly facilitated large-scale detection of the miRNAs. However, thoroughly discovery of novel miRNAs from the available HTS data sets remains a major challenge. In this study, we observed that Dicer-mediated cleavage sites for the processing of the miRNA precursors could be mapped by using degradome sequencing data in both animals and plants.
MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity.
BACKGROUND: RNA inverse folding is the problem of finding one or more sequences that fold into a user-specified target structure s 0, i.e. whose minimum free energy secondary structure is identical to the target s 0. Here we consider the ensemble of all RNA sequences that have low free energy with respect to a given target s 0. RESULTS: We introduce the program RNAdualPF, which computes the dual partition function Z (∗), defined as the sum of Boltzmann factors exp(-E(a,s 0)/RT) of all RNA nucleotide sequences a compatible with target structure s 0.
MiRNA are about 22nt long small noncoding RNAs that post transcriptionally regulate gene expression in animals, plants and protozoa. Confident identification of MiRNA-Target Interactions (MTI) is vital to understand their function. Currently, several integrated computational programs and databases are available for animal miRNAs, the mechanisms of which are significantly different from plant miRNAs.
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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