Small RNA (sRNA) Sequencing technology has revealed that microRNAs (miRNAs) are capable of exhibiting frequent variations from their canonical sequences, generating multiple variants: the isoforms of miRNAs (isomiRs). However, integrated tool to precisely detect and systematically annotate isomiRs from sRNA sequencing data is still in great demand. Here, we present an online tool, DeAnnIso (Detection and Annotation of IsomiRs from sRNA sequencing data).
MicroRNAs (miRNAs) represent an important class of small non-coding RNAs regulating gene expression in eukaryotes. Present algorithms typically rely on genomic data to identify miRNAs and require extensive installation procedures. Niche model organisms lacking genomic sequences cannot be analyzed by such tools. Here we introduce the MIRPIPE application enabling rapid and simple browser-based miRNA homology detection and quantification.
Long noncoding RNAs (lncRNAs) are noncoding transcripts longer than 200 nucleotides, which show evidence of pervasive transcription and participate in a plethora of cellular regulatory processes. Although several noncoding transcripts have been functionally annotated as lncRNAs within the genome, not all have been proven to fulfill the criteria for a functional regulator and further analyses have to be done in order to include them in a functional cohort.
Post-transcriptional processing events related to short RNAs are often reflected in their read profile patterns emerging from high-throughput sequencing data. MicroRNA arm switching across different tissues is a well-known example of what we define as differential processing. Here, short RNAs from the nine cell lines of the ENCODE project, irrespective of their annotation status, were analyzed for genomic loci representing differential or coherent processing. We observed differential processing predominantly in RNAs annotated as miRNA, snoRNA or tRNA.
Some viruses have been reported to transcribe microRNAs, implying complex relationships between the host and the pathogen at the post-transcriptional level through microRNAs in virus-infected cells. Although many computational algorithms have been developed for microRNA target prediction, few have been designed exclusively to find cellular or viral mRNA targets of viral microRNAs in a user-friendly manner. To address this, we introduce the viral microRNA host target (vHoT) database for predicting interspecies interactions between viral microRNA and host genomes.
An ensemble classifier approach for microRNA precursor (pre-miRNA) classification was proposed based upon combining a set of heterogeneous algorithms including support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF), then aggregating their prediction through a voting system. Additionally, the proposed algorithm, the classification performance was also improved using discriminative features, self-containment and its derivatives, which have shown unique structural robustness characteristics of pre-miRNAs.
MicroRNAs (miRNA) are small regulatory, noncoding RNA molecules that are transcribed as primary miRNAs (pri-miRNA) from eukaryotic genomes. At least in plants, their regulatory activity is mediated through base-pairing with protein-coding messenger RNAs (mRNA) followed by mRNA degradation or translation repression. We describe NOVOMIR, a program for the identification of miRNA genes in plant genomes.
miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them. While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system has witnessed limited development. Most of them have been centered around exact complementarity match. Very few of them considered other factors like multiple target sites and role of flanking regions.
RNA sequencing (RNA-seq) is emerging as a critical approach in biological research. However, its high-throughput advantage is significantly limited by the capacity of bioinformatics tools. The research community urgently needs user-friendly tools to efficiently analyze the complicated data generated by high throughput sequencers.
Understanding the biological roles of microRNAs (miRNAs) is a an active area of research that has produced a surge of publications in PubMed, particularly in cancer research. Along with this increasing interest, many open-source bioinformatics tools to identify existing and/or discover novel miRNAs in next-generation sequencing (NGS) reads become available. While miRNA identification and discovery tools are significantly improved, the development of miRNA differential expression analysis tools, especially in temporal studies, remains substantially challenging.
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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