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DNA and RNA are the two types of genetic material. They carry the information that tells a cell what to do, kind of like blueprints. The strands are made of a special sugar, nitrogen bases, and phosphate groups. Most organisms are made of DNA, but a few viruses have RNA as their genetic material. The biological information contained in an organism is encoded in its DNA or RNA sequence. The RNA molecules are linear polymers have much more structural flexibility than DNA.
RNA is very similar to DNA, but differs in a few important structural details: in the cell, RNA is usually single-stranded, while DNA is usually double-stranded; RNA nucleotides contain ribose while DNA contains deoxyribose; and RNA has the base uracil rather than thymine that is present in DNA.
RNA is transcribed from DNA by enzymes called RNA polymerases and is generally further processed by other enzymes. RNA is central to protein synthesis. Here, a type of RNA called messenger RNA carries information from DNA to structures called ribosomes. These ribosomes are made from proteins and ribosomal RNAs, which come together to form a molecular machine that can read messenger RNAs and translate the information they carry into proteins. There are many RNAs with other roles - in particular regulating which genes are expressed, but also as the genomes of most viruses.
mRNA, tRNA and rRNA are the three main types of RNAs that regulate the key role of central dogma. Along with these RNA's there is another type called Regulatory RNA's, they can down regulate gene expression by being complementary to a part of a mRNA or a gene's DNA. Two types of small RNA molecules - microRNA (miRNA) and small interfering RNA (siRNA) are central to RNA interference. The interference process has an important role in defending cells against parasitic nucleotide sequences viruses and transposons but also in directing development as well as gene expression in general.
MicroRNAs (miRNAs) are small RNA molecules which have recently gained widespread attention as critical regulators in complex gene regulatory networks in eukaryotes. They down-regulate gene expression by binding to the 3' untranslated region (3'-UTR) of the target mRNA bearing complementary target sequences. MiRNAs have been reported to control a wide range of biological processes such as hematopoiesis, neurogenesis, cell cycle control, and oncogenesis, indicating that miRNAs are core elements of the complete gene regulatory network, together with transcription factors .These small RNA, processed from non-coding regions of the genome into ~23 nucleotide long single stranded RNA, have been shown to regulate translation of messenger RNA (mRNA) by binding to it and effecting target cleavage or translational block depending on the extent of sequence complementarity with the target . MicroRNAs were initially discovered in 1993, in a genetic screen for mutants that disrupt the timing of post-embryonic development in the nematode Caenorhabditis elegans, when let7 was discovered, and was found to be highly conserved in eukaryotes, it led to a surge in discovery of new microRNAs in a number of organisms including humans. Most known miRNAs are very well conserved in close species and some can be found across very large taxonomic groups, notably let-7 of C. elegans.
2.4 MicroRNA Biogenesis and mechanism of action:
miRNA genes are frequently expressed individually, but many exist in clusters of 2-7 genes with small intervening sequences. miRNA biogenesis in animals is a two-step process. In the first step, MicroRNA is transcribed as longer RNA molecule called pri-miRNA. The pri-miRNA is processed in the nucleus itself into hairpin RNA of 60 to 120 nucleotides by a protein complex consisting of the ribonuclease Drosha and an RNA binding protein Pasha. This hairpin RNA, known as pre-miRNA, is transported to the cytoplasm via exportin-5 dependent mechanism that is the second step. It is digested there by a dsRNA specific ribonuclease called Dicer to form the mature miRNA. Mature miRNA is bounded by a complex, similar to the RNA induced silencing complex (RISC) that participates in RNA interface (RNAi). The mature miRNA makes base pairing with mRNA where complementarities exist between them. This results in target degradation in plants and destabilization in animals. In general, miRNAs can regulate gene expression either by translational inhibition or by mRNA destabilization.
The way microRNA and their targets interact in animals and plants are different in certain aspects. The plant miRNA exhibits perfect or nearly perfect base pairing with the target but in the case of animals, the pairing is rather imperfect. This makes the microRNA target identification problem in animals more complex compared to that in plants. Also miRNAs in plants bind to their targets within coding regions cleaving at single sites whereas most of the miRNA binding sites in animals are in the 3′ un-translated regions (UTR).
Figure 1: overview of biogenesis and action of microRNAs
The miRNA binds to the mRNA and it either causes the mRNA cleavage or it inhibits the translation. mRNA cleavage mostly occurs in plants while translational repression occurs mostly in animals .Single mRNA can contain multiple miRNA targets for different miRNAs or for the same miRNA. It is also known that miRNAs are highly conserved among different species. In addition to the conserved miRNAs, there are lots of non conserved species specific miRNAs; these may control the specific characteristics that are unique to those species.
Figure 2: A: Plant miRNAs exhibit extensive complementarity to their targets, but animal miRNAs generally do not. B: Various configurations for miRNA-target duplexes: one near-perfect binding site for one miRNA (upper left), one strong site for one miRNA (lower left), multiple modest sites for one miRNA (upper right), and multiple modest sites for multiple miRNAs (lower right).
2.6 [isn't it 2.5??] Integrative approach to modeling microRNA mediated host-virus interaction:
The evidences demonstrating that both host and virus encoded microRNAs that interact with host and virus miRNA transcripts respectively, in addition to their roles in regulating their own transcripts, microRNA also mediated host virus interaction too. [what does this mean??] The challenge would be to integrate bioinformatics with gene expression and proteomics data. This would not only enable them to design novel diagnostic and therapeutic strategies to combat deadly viruses, but also empower researchers to understand basic biological processes involved in latency and oncogenic transformation mediated by viruses.
Figure 3: Over view of microRNA mediated host-pathogen Interaction
2.7 MicroRNA Target prediction:
Prediction of miRNA targets is more challenging in animals because of the imperfect complementarity of miRNAs to their targets. The detection methods are mainly classified in to Experimental method and sequence based method.
1. Experimental methods
The most extended experimental technique for determining miRNA targets is the transfection of mimic miRNAs or miRNA inhibitors. The effects on the expression levels of the mRNAs and proteins are measured by using transcriptomic and proteomic tools (qRT-PCR, microarrays, RNA-seq, western blot, SILAC, 2D-DIGE). However, with this technique it is not possible to distinguish indirect and direct interactions. Adding reporters or labels to miRNAs or the 3'UTR of transcripts of interest during transfection focus the experiment on direct interactions as done in LAMP or luciferase report assays. Other direct methods for miRNA target prediction are based on the immunoprecipitation of RISC complexes such us Argonaute bound miRNA-mRNA molecules. Each experimental technique has its own reliability. Due to this, combining different experimental tools is a good method to ensure the authenticity of a miRNA target.
2. Sequence-based methods
Despite the wide range of experimental tools for miRNA target validation available, the lack of
high-throughput and low-cost methods have enforced the development of computational techniques. These are based on experimentally determined rules of miRNA targeting:
(i) Sequence complementarity between the 30-UTR of the mRNAs and the 'seed region' of the miRNA (nucleotides 2-7),
(ii) Possible functional target sites along the coding sequence and 50-UTR of the mRNA,
(iii) Conservation of some of the miRNA target sites between related species
(iv) The target site accessibility due to the RNA secondary structure (i.e. free energy costs to unfold the mRNA secondary structure surrounding the target site and free energy of the miRNA-target pairing).
Although the methods that use these rules are far from perfect, the putative lists of targets generated by computational methods entangle a considerable reduction of experimental work as they significantly reduce the number of interactions that must undergo validation.
2.7.1 MicoRNA target prediction approaches classification:
1. Complementarity searching based methods
2. Thermodynamic based methods
3. Machine learning methods
1 .Complementarity searching based methods
These types methods identify initial potential targets using complementarity searching algorithms and then improve them by using other features like thermodynamics, binding site structure and conservation. Stark and coworkers initially implemented this strategy for predicting miRNA targets in Drosophila melanogaster. miRanda, TargetScan and PicTar follows the same strategy.
2. Thermodynamic based methods
This category uses the favorable thermodynamic structure as an initial indicator and then uses other properties of miRNA-mRNA interaction for filter miRNA targets. DIANA-microT and RNAHybrid fall in this category.
3. Machine learning methods
The 3'UTRs have some common motifs of short length, some of these are complementary to the seed region of known miRNA, and others might be the target of unknown miRNAs. This method of analyzing whole genome to predict miRNA targets can only be applied to predict conserved targets. Machine learning methods like SVM, HMM and ANN are also used to predict miRNA targets, the performance of machine learning algorithms is affected by shortage of training data. miTarget classifier using the support vector machine. ?????
2.7.2 Current MicroRNA target prediction tools:
miRanda considers all the known miRNAs of D. melanogaster. The algorithm encompasses three phases, in the first phase miRNAs are matched against the 3'UTR regions of all possible targets. The method does not rely on seed matches directly but privileges [wrong usage] complementarity at the 5'end of the miRNA by using a scaling factor for scores computed in this region, and incorporates some position-specific empirical rules. The second phase consists in computing the thermodynamic stability of the miRNA: target duplex, and the third and final phase is an assessment of the evolutionary conservation of miRNA-target associations across two additional species. Finally, using a randomization procedure, the authors estimated the false positive rate and showed that it is reduced if one considers only mRNAs with multiple target sites. ???The same approach was later used to predict targets in humans and other vertebrates. The false-positive rate is between 24% and 39% with the basic parameter settings.
2 .TargetScan and TargetSacnS
This method requires perfect complementarity to the seed region of a miRNA. After checking complementarity in the seed region, it then checks the complementarity in other regions. These matches are then extended to target sites involving the entire miRNA, allowing for G:U pairs, and using a folding algorithm to predict the secondary structure of the heteroduplex. To each putative target, a folding free energy value is assigned, and a Z-score is calculated based on the number of matches predicted in the same target transcript and respective free energies. The candidate transcripts for each organism are ranked by Z-score, and the process is repeated for each organism in the set. Cut-off values for rank and Z-score are given, and the final candidate set is composed of targets that respect the established limits for all orthologous transcripts.???? The estimated false-positive rate is between 22% and 31%. TargetScanS is the improved version of TargetScan.
PICTAR is a combinatorial method that identifies individual miRNA target sites by searching near-perfect seeds defined as a stretch of ~7-nt starting at position 1 or 2 from the 5'end of the miRNA. These target sites are then filtered with respect to the MFE of the heteroduplexes and to whether these sites fall into overlapping positions across the aligned orthologous sequences. The target sites that pass both these filters are termed anchors. Sequences that show a user-defined minimum number of anchors are then ranked using an HMM maximum likelihood score. This score is computed considering all segmentations of the target sequence into target sites and background, thus accounting for the synergistic effect of multiple binding sites for a single miRNA or several miRNAs co-regulating the same transcript.????
This method was used to predict new miRNA targets in H. sapiens. The search method considered two hypotheses about miRNA: mRNA regulatory associations: (i) they should be conserved high-affinity interactions; (ii) they should be structurally restrained due to the enzymology of the miRISC complex. The first observation resulted in an algorithm to compute the thermodynamic stability of imperfect miRNA: mRNA pairings. The second hypothesis led to the speculation that the structural restraints might be reduced to a set of general rules. In order to identify these rules, the authors performed a series of experiments whereby some putative target site sequences were cloned onto a reporter construct. These rules were then used to filter the initial set of candidates. The results obtained with these experiments once again underlined the importance of near-perfect complementarity on the first few nucleotides at the 5'end of the miRNA.
5 .Machine learning approaches
TARGETBOOST is a machine-learning method that combines genetic programming with boosting. Instead of relying on criteria based on sequence complementarity, thermodynamic stability or evolutionary conservation, it tries to learn the hidden rules of miRNA: target site hybridization. The genetic programming component consists in spawning and evolving a series of pattern sequences which try do describe the general properties of miRNA target sites, namely the existence of a nucleus of consecutive paired bases (seed) or a bulge of unpaired nucleotides. Each of these pattern sequences is a classifier, and they are all combined using the boosting technique that gives each classifier a weight depending on its performance on the training set. Additional filters can be added to this procedure, like the verification of evolutionary conservation or the existence of multiple target sites in the same 3'UTR.
Other machine learning approaches using the popular SVM framework have been proposed. These approaches try to generalize from a modest set of experimentally verified positive and negative examples. An example is MITARGET which uses an SVM considering structural features of the 50'and 30'half of ????????the hybridization site, thermodynamic features and positional features.
2.8 Integrated resources for MicroRNA Target -interactions:
miRecords is a new resource for animal miRNA-target interactions, it consists of mainly two components. The Validated Targets component is a large, high-quality database of experimentally validated miRNA targets resulting from meticulous manual literature curation. As the largest known collection of experimental validated miRNA targets, it emphasizes systematic and structured documentation of experimental support of miRNA-target interactions. This database not only serves the experimental researchers by providing the lists of confirmed targets of the miRNAs of their interest, but also provides a large and high-quality dataset that will facilitate the development of the next-generation miRNA target prediction programs. The predicted targets component of miRecords store predicted miRNA targets produced by 11 miRNA target prediction programs. Currently, this database includes 1135 records of validated miRNA-target interactions between 301 miRNAs and 902 target genes.
TarBase , houses a manually curated collection of experimentally tested miRNA targets, in human, mouse, fruit fly, worm, and zebrafish. Simple analyses of the data have raised interesting considerations about the features used for target prediction and the experimental techniques used for support. TarBase will not only be useful for biologists interested in miRNA function, but also for bioinformaticians interested in using the most comprehensive set of supported targets currently available to train and test a new cohort of machine-learning methods for target prediction. It contains positive tested and negative tested miRNA targets. Each positive target site has information about miRNA and its gene, the nature of experiment and source from where data was extracted. It is also linked with other databases such as Gene Ontology (GO) and UCSC Genome Browser. Currently it has 1333entries for all species.
MiRDB is an online database system based on miRNA target prediction and functional annotation, it follows a design strategy is to present the annotations by associating them with miRNA precursors. This is an effective way to study miRNA gene structures in the genome; it also presents a functional catalog whose pages are organized by mature miRNAs wiki strategy for miRNA annotations new functional data are constantly generated from high throughput experiments,?????? such as sequencing and microarrays. These data, typically, are processed with automated bioinformatics pipelines. Besides high-throughput experiments, there are many other ''traditional'' biological experiments focusing on the functions of one or few genes. It has 2295 miRNA with targets.
miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs.
Sung-Chou Li constructed a user-friendly web interface to present the viral candidate miRNAs. The classification and arrangement of the viral candidates are according to the taxonomy table of NCBI, so users can query the viral miRNAs more easily. Users may query the putative miRNAs from a specific virus by using the hierarchical menu or by using the simple search function. A keyword search can be performed, but users are recommended to use it with the GenBank identifier or RefSeq accession number for better search result.
It is a database for host miRNA targets on virus genomes and virus transcripts. The database comprises known host miRNAs, known viral miRNAs, known host miRNA targets on viruses and putative host miRNA targets on viruses. The proposed resource can provide sufficient and effective information for the investigation about the interaction between host miRNAs and viral genes. The information about the tissue preferences of viruses is effective to combine the tissue specificity of host miRNAs for further analysis of miRNA targets on viruses in ViTa. The prospective works of the database are as follows: (i) more expression profiles for host miRNA genes will be supported; (ii) combinatorial miRNA regulation to viral gene expression can be identified by computational analysis and miRNA-related gene expression profiles; and (iii) the association between virus and disease will be explored by clinical data collection.
miRGator is an integrated database of microRNA -associated gene expression, target prediction, disease association and genomic annotation, which aims to facilitate functional investigation of miRNAs. The recent version of miRGator v2.0 contains information about (i) human miRNA expression profiles under various experimental conditions, (ii) paired expression profiles of mRNAs and miRNAs, (iii) gene expression profiles under miRNA-perturbation, (iv) known/predicted miRNA targets and (v) miRNA-disease associations. In total, >8000 miRNA expression profiles, ~300 miRNA-perturbed gene expression profiles and ~2000 mRNA expression profiles are compiled with manually curated annotations on disease, tissue type and perturbation. Additionally, miRNA expression and disease-phenotype profiles revealed miRNA pairs whose expression was regulated in parallel in various experimental and disease conditions.
2.9 In silico prediction of viral mRNA targets of viral miRNAs:
There are different computational algorithms now existing to predict candidate miRNA targets; few have been designed exclusively to find cellular or viral mRNA targets of viral miRNAs.
miRiam is not based on a user-friendly web application but instead requires the Python interpreter to run the program. miRiam, is a novel program based on both thermodynamics features and empirical constraints, to predict viral miRNAs/human targets interactions. It exploits target mRNA secondary structure accessibility and interaction rules, inferred from validated miRNA/mRNA pairs. A set of genes involved in apoptosis and cell-cycle regulation was identified as target for the studies. This choice was supported by the knowledge that DNA tumor viruses interfere with the above processes in humans. miRNAs were selected from two cancer-related viruses, Epstein-Barr Virus (EBV) and Kaposi-Sarcoma-Associated Herpes Virus (KSHV). The results suggest that during viral infection, besides the protein-based host regulation mechanism, a post-transcriptional level interference may exist.
RepTar is a new database offering miRNA target predictions of several binding types, identified by recently developed modular algorithm RepTar. It is based on identification of repetitive elements in 3' UTRs and is independent of both evolutionary conservation and conventional binding patterns. The modularity of RepTar enables the prediction of targets with conventional seed sites as well as rarer targets with non-conventional sites, such as sites with seed wobbles (G-U pairing in the seed region), 3'compensatory sites and the newly discovered centered sites. Furthermore, RepTar's independence of conservation enables the prediction of cellular targets of the less evolutionarily conserved viral miRNAs. Thus, the RepTar database contains genome-wide predictions of human and mouse miRNAs as well as predictions of cellular targets of human and mouse viral miRNAs. These predictions are presented in a user-friendly database, which allows browsing through the putative sites as well as conducting simple and advanced queries including data intersections of various types.
Viral miRNA host target (vHoT) database, a web-based tool that can search for mRNA targets of viral miRNAs. It allows interspecies analysis and can thus be used to predict both cellular mRNA targets and viral mRNA targets of virus-derived miRNAs. The current version of vHoT can predict the targets of 271 viral miRNAs within the human, mouse, rat, rhesus monkey, cow, and virus genomes. Either a user-specified set of genes or the whole genome of an organism can be used for prediction. Five widely used algorithms that are known to be relatively accurate for target prediction, TargetScan, miRanda, RNAhybrid, DIANA-microT and PITA were customized and used as the search engines of vHoT, and the user can specify the key parameters of these algorithms to fine-tune search results.
2.10 Aim and scope of the study:
Design an algorithm for predicting viral miRNA targets.
Implement the algorithm as an R program/ package.