miRToolsGallery

Small RNAs (smRNAs) in plants, mainly microRNAs and small interfering RNAs, play important roles in both transcriptional and post-transcriptional gene regulation. The broad application of high-throughput sequencing technology has made routinely generation of bulk smRNA sequences in laboratories possible, thus has significantly increased the need for batch analysis tools. PsRobot is a web-based easy-to-use tool dedicated to the identification of smRNAs with stem-loop shaped precursors (such as microRNAs and short hairpin RNAs) and their target genes/transcripts.

Independent identification of genes in different organisms and assays has led to a multitude of names for each gene. This balkanization makes it difficult to use gene names to locate genomic resources, homologs in other species and relevant publications.

We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer.

Small non-coding RNAs (sRNAs) carry out a variety of biological functions and affect protein synthesis and protein activities in prokaryotes. Recently, numerous sRNAs and their targets were identified in Escherichia coli and in other bacteria. It is crucial to have a comprehensive resource concerning the annotation of small non-coding RNAs in microbial genomes.

Extracellular vesicles (EVs) are spherical bilayered proteolipids, harboring various bioactive molecules. Due to the complexity of the vesicular nomenclatures and components, online searches for EV-related publications and vesicular components are currently challenging.

Little is known about the extent to which individual microRNAs (miRNAs) regulate common processes of tumor biology across diverse cancer types. Using molecular profiles of >3,000 tumors from 11 human cancer types in The Cancer Genome Atlas, we systematically analyzed expression of miRNAs and mRNAs across cancer types to infer recurrent cancer-associated miRNA-target relationships. As we expected, the inferred relationships were consistent with sequence-based predictions and published data from miRNA perturbation experiments.