Long non-coding RNAs (lncRNAs) represent a class of potent regulators of gene expression that are found in a wide array of eukaryotes; however, our knowledge about these molecules in plants is still very limited. In particular, a number of model plant species still lack comprehensive data sets of lncRNAs and their annotations, and very little is known about their biological roles. To meet these shortcomings, we created an online database of lncRNAs in 10 model plant species.
Plant microRNA prediction tools that use small RNA-sequencing data are emerging quickly. These existing tools have at least one of the following problems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their databases; (iv) hard to install or use. We developed miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species.
Along with computational approaches, NGS led technologies have caused a major impact upon the discoveries made in the area of miRNA biology, including novel miRNAs identification. However, to this date all microRNA discovery tools compulsorily depend upon the availability of reference or genomic sequences. Here, for the first time a novel approach, miReader, has been introduced which could discover novel miRNAs without any dependence upon genomic/reference sequences.
The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project.
The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means.
Small interfering RNA (siRNA) technology has vast potential for functional genomics and development of therapeutics. However, it faces many obstacles predominantly instability of siRNAs due to nuclease digestion and subsequently biologically short half-life. Chemical modifications in siRNAs provide means to overcome these shortcomings and improve their stability and potency. Despite enormous utility bioinformatics resource of these chemically modified siRNAs (cm-siRNAs) is lacking.
Small RNAs play an important role in plant development, stress responses, and epigenetic regulation, primarily through their role in transcriptional and post-transcriptional silencing of specific target genes and loci. Most if not all plants utilize these small RNA signaling networks. We have developed a deep-sequencing based dataset of plant small RNAs, based on the hypothesis that comparisons among the complex pool of small RNAs from diverse plants will identify novel types of conserved, regulated, or species-specific molecules.
Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs.
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.
MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require "read count" to be included with the input sequences, which restricts their use to deep-sequencing data.
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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