Efficient use of accessibility in microRNA target prediction

Considering accessibility of the 3′UTR is believed to increase the precision of microRNA target predictions. We show that, contrary to common belief, ranking by the hybridization energy or by the sum of the opening and hybridization energies, used in currently available algorithms, is not an efficient way to rank predictions. Instead, we describe an algorithm which also considers only the accessible binding sites but which ranks predictions according to over-representation. When compared with experimentally validated and refuted targets in the fruit fly and human, our algorithm shows a remarkable improvement in precision while significantly reducing the computational cost in comparison with other free energy based methods. In the human genome, our algorithm has at least twice higher precision than other methods with their default parameters. In the fruit fly, we find five times more validated targets among the top 500 predictions than other methods with their default parameters. Furthermore, using a common statistical framework we demonstrate explicitly the advantages of using the canonical ensemble instead of using the minimum free energy structure alone. We also find that ‘naïve’ global folding sometimes outperforms the local folding approach.     

Improving performance of mammalian microRNA target prediction

Background  

MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.     

Assessing the utility of thermodynamic features for microRNA target prediction under relaxed seed and no conservation requirements

Background

Many computational microRNA target prediction tools are focused on several key features, including complementarity to 5′seed of miRNAs and evolutionary conservation. While these features allow for successful target identification, not all miRNA target sites are conserved and adhere to canonical seed complementarity. Several studies have propagated the use of energy features of mRNA:miRNA duplexes as an alternative feature. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.

Methodology/Principal Findings

We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide sites of AGO protein interaction. This trend is confirmed on datasets that assay the effect of miRNAs on mRNA and protein expression changes, and a simple linear regression model leads to significant correlation of predicted versus observed expression change. Compared to 6-mer seed matches as baseline, application of our energy-based model leads to ∼3–5-fold enrichment on highly down-regulated targets, and allows for prediction of strictly imperfect targets with enrichment above baseline.

Conclusions/Significance

In conclusion, our results indicate significant promise for energy-based miRNA target prediction that includes a broader range of targets without having to use conservation or impose stringent seed match rules.     

A quick reality check for microRNA target prediction

The regulation of protein abundance by microRNA (miRNA)-mediated repression of mRNA translation is a rapidly growing area of interest in biochemical research. In animal cells, the miRNA seed sequence does not perfectly match that of the mRNA it targets, resulting in a large number of possible miRNA targets and varied extents of repression. Several software tools are available for the prediction of miRNA targets, yet the overlap between them is limited. Jovanovic et al. have developed and applied a targeted, quantitative approach to validate predicted miRNA target proteins. Using a proteome database, they have set up and tested selected reaction monitoring assays for approximately 20% of more than 800 predicted let-7 targets, as well as control genes in Caenorhabditis elegans. Their results demonstrate that such assays can be developed quickly and with relative ease, and applied in a high-throughput setup to verify known and identify novel miRNA targets. They also show, however, that the choice of the biological system and material has a noticeable influence on the frequency, extent and direction of the observed changes. Nonetheless, selected reaction monitoring assays, such as those developed by Jovanovic et al., represent an attractive new tool in the study of miRNA function at the organism level.     

HOCTAR database: a unique resource for microRNA target prediction

An Antarctic strain (NJ-7) of Chlorella vulgaris possesses the same 18S rRNA sequence as that of a temperate strain (UTEX259), but shows significantly higher freezing tolerance than the latter. Suppression subtractive hybridization (SSH) was performed to identify genes of intensified expression in NJ-7 relative to UTEX259. Among the genes identified, Ccor1 and Ccor2, co-organized in the same gene cluster Ccor1-Ccor2-Ccor1-Ccor2, showed much higher expression levels in NJ-7 than in UTEX259 at both 20°C and 4°C. As detected by Northern blot and Western blot analyses, the two genes were cold-inducible in NJ-7 but almost not expressed in UTEX259. Their encoded products are predicted to share 55.7% identity to each other and possess physicochemical characteristics similar to that of late embryogenesis abundant (LEA) proteins in plants. The purified recombinant Ccor1 and Ccor2 showed high heat-stability and could act as cryoprotectants to lactate dehydrogenase in vitro. Based on their expression patterns and protein characteristics, we propose that Ccor1 and Ccor2 are two novel LEA proteins and are related to the greatly enhanced freezing tolerance in the Antarctic strain.     

miRNA及其靶位点多态性的研究进展

微RNA(microRNA,miRNA)是一类进化上保守、长度为21～23nt的非编码单链小RNA,参与个体发育、器官形成、细胞增殖、分化和细胞凋亡等生物学过程,并在其中发挥重要的调节作用。近年来研究发现,miRNA及其靶位点的多态将引起不同类型的疾患。文章主要从miRNA及其靶位点的多态类型,以及由多态性引起的相关疾病等方面来阐述miRNA的最新进展。     

TargetSpy: a supervised machine learning approach for microRNA target prediction

Background  

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites.     

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon.com/demo/.).     

miRDB: a microRNA target prediction and functional annotation database with a wiki interface

MicroRNAs (miRNAs) are short noncoding RNAs that are involved in the regulation of thousands of gene targets. Recent studies indicate that miRNAs are likely to be master regulators of many important biological processes. Due to their functional importance, miRNAs are under intense study at present, and many studies have been published in recent years on miRNA functional characterization. The rapid accumulation of miRNA knowledge makes it challenging to properly organize and present miRNA function data. Although several miRNA functional databases have been developed recently, this remains a major bioinformatics challenge to miRNA research community. Here, we describe a new online database system, miRDB, on miRNA target prediction and functional annotation. Flexible web search interface was developed for the retrieval of target prediction results, which were generated with a new bioinformatics algorithm we developed recently. Unlike most other miRNA databases, miRNA functional annotations in miRDB are presented with a primary focus on mature miRNAs, which are the functional carriers of miRNA-mediated gene expression regulation. In addition, a wiki editing interface was established to allow anyone with Internet access to make contributions on miRNA functional annotation. This is a new attempt to develop an interactive community-annotated miRNA functional catalog. All data stored in miRDB are freely accessible at http://mirdb.org.     

Secondary structure prediction and in vitro accessibility of mRNA as tools in the selection of target sites for ribozymes

We have investigated the relative merits of two commonly used methods for target site selection for ribozymes: secondary structure prediction (MFold program) and in vitro accessibility assays. A total of eight methylated ribozymes with DNA arms were synthesized and analyzed in a transient co-transfection assay in HeLa cells. Residual expression levels ranging from 23 to 72% were obtained with anti-PSKH1 ribozymes compared to cells transfected with an irrelevant control ribozyme. Ribozyme efficacy depended on both ribozyme concentration and the steady state expression levels of the target mRNA. Allylated ribozymes against a subset of the target sites generally displayed poorer efficacy than their methylated counterparts. This effect appeared to be influenced by in vivo accessibility of the target site. Ribozymes designed on the basis of either selection method displayed a wide range of efficacies with no significant differences in the average activities of the two groups of ribozymes. While in vitro accessibility assays had limited predictive power, there was a significant correlation between certain features of the predicted secondary structure of the target sequence and the efficacy of the corresponding ribozyme. Specifically, ribozyme efficacy appeared to be positively correlated with the presence of short stem regions and helices of low stability within their target sequences. There were no correlations with predicted free energy or loop length.     

Atom-wise statistics and prediction of solvent accessibility in proteins

In this work, we explore a novel method to broaden the scope of sequence-based predictions of solvent accessibility or accessible surface area (ASA) to the atomic level. All 167 heavy atoms from the 20 types of amino acid residues in proteins have been studied. An analysis of ASA distribution of these atomic groups in different proteins has been performed and rotamer-style libraries have been developed. We observe that the ASA of some atomic groups (e.g., backbone C and N atoms) can be estimated from the sequence environment within a mean absolute error of 2-3 angstroms(2). However, some side chain atoms such as CG in Pro, NH1 in Arg and NE2 in Gln show a strong variability making it more difficult to estimate their ASA from sequence environment. In general, the prediction of ASA becomes more difficult for atomic positions at the side chain extremities of long amino acid residues (aromatic side chain terminals being the exception). Several atomic groups are frequently exposed to solvent. Some of them have a bimodal distribution, suggesting two stable conformations in terms of their solvent exposure. More detailed understanding and prediction of solvent accessibility, i.e., at an atomic level is expected to help in bioinformatics approaches to structure prediction, functional relevance of atomic solvent accessibilities and other interaction analyses.     

PredAcc: prediction of solvent accessibility

SUMMARY: PredAcc is a tool for predicting the solvent accessibility of protein residues from the sequence at different relative accessibility levels (0-55%). The prediction rate varies between 70. 7% (for 25% relative accessibility) and 85.7% (for 0% relative accessibility). Amino acids are predicted in four categories: almost certainly hidden and almost certainly exposed with a given a posteriori prediction error, probably hidden and probably exposed otherwise. AVAILABILITY: http://condor.urbb.jussieu.fr/PredAccCfg.html CONTACT: tuffery@urbb.jussieu.fr     

Towards computational prediction of microRNA function and activity

While it has been established that microRNAs (miRNAs) play key roles throughout development and are dysregulated in many human pathologies, the specific processes and pathways regulated by individual miRNAs are mostly unknown. Here, we use computational target predictions in order to automatically infer the processes affected by human miRNAs. Our approach improves upon standard statistical tools by addressing specific characteristics of miRNA regulation. Our analysis is based on a novel compendium of experimentally verified miRNA-pathway and miRNA-process associations that we constructed, which can be a useful resource by itself. Our method also predicts novel miRNA-regulated pathways, refines the annotation of miRNAs for which only crude functions are known, and assigns differential functions to miRNAs with closely related sequences. Applying our approach to groups of co-expressed genes allows us to identify miRNAs and genomic miRNA clusters with functional importance in specific stages of early human development. A full list of the predicted mRNA functions is available at http://acgt.cs.tau.ac.il/fame/.