miRmap: Comprehensive prediction of microRNA target repression strength

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirma

miRmap web: comprehensive microRNA target prediction online

MicroRNAs (miRNAs) posttranscriptionally repress the expression of protein-coding genes. Based on the partial complementarity between miRNA and messenger RNA pairs with a mandatory so-called ‘seed' sequence, many thousands of potential targets can be identified. Our open-source software library, miRmap, ranks these potential targets with a biologically meaningful criterion, the repression strength. MiRmap combines thermodynamic, evolutionary, probabilistic and sequence-based features, which cover features from TargetScan, PITA, PACMIT and miRanda. Our miRmap web application offers a user-friendly and feature-rich resource for browsing precomputed miRNA target predictions for model organisms, as well as for predicting and ranking targets for user-submitted sequences. MiRmap web integrates sorting, filtering and exporting of results from multiple queries, as well as providing programmatic access, and is available at http://mirmap.ezlab.or

miROrtho: computational survey of microRNA genes

MicroRNAs (miRNAs) are short, non-protein coding RNAs that direct the widespread phenomenon of post-transcriptional regulation of metazoan genes. The mature ∼22-nt long RNA molecules are processed from genome-encoded stem-loop structured precursor genes. Hundreds of such genes have been experimentally validated in vertebrate genomes, yet their discovery remains challenging, and substantially higher numbers have been estimated. The miROrtho database (http://cegg.unige.ch/mirortho) presents the results of a comprehensive computational survey of miRNA gene candidates across the majority of sequenced metazoan genomes. We designed and applied a three-tier analysis pipeline: (i) an SVM-based ab initio screen for potent hairpins, plus homologs of known miRNAs, (ii) an orthology delineation procedure and (iii) an SVM-based classifier of the ortholog multiple sequence alignments. The web interface provides direct access to putative miRNA annotations, ortholog multiple alignments, RNA secondary structure conservation, and sequence data. The miROrtho data are conceptually complementary to the miRBase catalog of experimentally verified miRNA sequences, providing a consistent comparative genomics perspective as well as identifying many novel miRNA genes with strong evolutionary suppor

linc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells

Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size. Here, we show that two putative lincRNAs (linc-mipep, also called lnc-rps25, and linc-wrb) encode micropeptides with homology to the vertebrate-specific chromatin architectural protein, Hmgn1, and demonstrate that they are required for development of vertebrate-specific brain cell types. Specifically, we show that NMDA receptor-mediated pathways are dysregulated in zebrafish lacking these micropeptides and that their loss preferentially alters the gene regulatory networks that establish cerebellar cells and oligodendrocytes - evolutionarily newer cell types that develop postnatally in humans. These findings reveal a key missing link in the evolution of vertebrate brain cell development and illustrate a genetic basis for how some neural cell types are more susceptible to chromatin disruptions, with implications for neurodevelopmental disorders and disease

Computational prediction of microRNA targets: thermodynamic, probabilistic and evolutionary models parameterized by genome-scale experimental data

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the vast majority of messenger RNAs (mRNAs). Base-pairing of the so-called miRNA "seed" region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may employ thermodynamic, evolutionary, probabilistic, or sequence-based features. We developed an open source software library, miRmap, which for the first time comprehensively covers all four approaches using eleven predictor features, three of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan

MiRmap: comprehensive prediction of microRNA target repression strength

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA 'seed' region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap

Repression of arginase-2 expression in dendritic cells by microRNA-155 is critical for promoting T cell proliferation

Arginine, a semiessential amino acid implicated in diverse cellular processes, is a substrate for two arginases-Arg1 and Arg2-having different expression patterns and functions. Although appropriately regulated Arg1 expression is critical for immune responses, this has not been documented for Arg2. We show that Arg2 is the dominant enzyme in dendritic cells (DCs) and is repressed by microRNA-155 (miR155) during their maturation. miR155 is known to be strongly induced in various mouse and human DC subsets in response to diverse maturation signals, and miR155-deficient DCs exhibit an impaired ability to induce Ag-specific T cell responses. By means of expression profiling studies, we identified Arg2 mRNA as a novel miR155 target in mouse DCs. Abnormally elevated levels of Arg2 expression and activity were observed in activated miR155-deficient DCs. Conversely, overexpression of miR155 inhibited Arg2 expression. Bioinformatic and functional analyses confirmed that Arg2 mRNA is a direct target of miR155. Finally, in vitro and in vivo functional assays using DCs exhibiting deregulated Arg2 expression indicated that Arg2-mediated arginine depletion in the extracellular milieu impairs T cell proliferation. These results indicate that miR155-induced repression of Arg2 expression is critical for the ability of DCs to drive T cell activation by controlling arginine availability in the extracellular environment