miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.

/pdf/mirbase-integrating-microrna-annotation-and-deep-sequencing-3rcas34b0z.pdf

miRBase: integrating microRNA annotation and deep-sequencing data

https://www.gene-quantification.de/dweep-et-al-mirwalk-2011.pdf

miRWalk - Database: Prediction of possible miRNA binding sites by walking the genes of three genomes

Early studies have shown how aberrantly expressed microRNAs are a hallmark of several diseases like cancer. MicroRNA expression profiling was shown to be associated with tumour development, progression and response to therapy, suggesting their possible use as diagnostic, prognostic and predictive biomarkers. Moreover, based on the increasing number of studies demonstrating that microRNAs can function as potential oncogenes or oncosuppressor genes, with the goal to improve disease response and increase cure rates, miRNA-based anticancer therapies have recently been exploited, either alone or in combination with current targeted therapies. The advantage of using microRNA approaches is based on its ability to concurrently target multiple effectors of pathways involved in cell differentiation, proliferation and survival. Here, we review our current knowledge about the involvement of microRNAs in cancer, and their potential as diagnostic, prognostic and therapeutic tools.

https://www.embopress.org/doi/pdf/10.1002/emmm.201100209

MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

https://link.springer.com/content/pdf/10.1186%2Fgb-2010-11-8-r90.pdf

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

Small non-coding RNAs (sncRNAs) are potential vectors at the interface between genes and environment. We found that traumatic stress in early life altered mouse microRNA (miRNA) expression, and behavioral and metabolic responses in the progeny. Injection of sperm RNAs from traumatized males into fertilized wild-type oocytes reproduced the behavioral and metabolic alterations in the resulting offspring.

/pdf/implication-of-sperm-rnas-in-transgenerational-inheritance-2foybd5oaz.pdf

Implication of sperm RNAs in transgenerational inheritance of the effects of early trauma in mice

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

/pdf/diana-microt-web-server-elucidating-microrna-functions-352zw9792e.pdf

DIANA-microT web server: elucidating microRNA functions through target prediction

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

/pdf/accurate-microrna-target-prediction-correlates-with-protein-2zfscmqk97.pdf

Accurate microRNA target prediction correlates with protein repression levels

We present gmblock, a block-level storage sharing system over Myrinet which uses an optimized I/O path to transfer data directly between the storage medium and the network, bypassing the host CPU and main memory bus of the storage server. It is device driver independent and retains the protection and isolation features of the OS. We evaluate the performance of a prototype gmblock server and find that: (a) the proposed techniques eliminate memory and peripheral bus contention, increasing remote I/O bandwidth significantly, in the order of 20---200% compared to an RDMA-based approach, (b) the impact of remote I/O to local computation becomes negligible, (c) the performance characteristics of RAID storage combined with limited NIC resources reduce performance. We introduce synchronized send operations to improve the degree of disk to network I/O overlapping. We deploy the OCFS2 shared-disk filesystem over gmblock and show gains for various application benchmarks, provided I/O scheduling can eliminate the disk bottleneck due to concurrent access.

http://www.cslab.ece.ntua.gr/~vkoukis/papers/jcc-gmblock-20100526.pdf

Evangelos Koukis

Papers

DIANA-microT web server: elucidating microRNA functions through target prediction

Accurate microRNA target prediction correlates with protein repression levels

GMBlock: Optimizing data movement in a block-level storage sharing system over Myrinet