BIOINFORMATICS REVIEW- JUNE 2017

JUNE 2017 VOL 3 ISSUE6

“Science is fun. Science is curiosity.

We all have natural curiosity.

Science is a process of investigating.

It's posing questions and coming up with a method. It's delving in.�

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Bioinformatics Challenges and Advances in RNA interference

Sally Ride

Why To Study Evolution?

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Contents

June 2017

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Topics Editorial....

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03 Phylogenetics Why to study Evolution?

07

05

Transcriptomics

Bioinformatics Challenges and Advances in RNA interference 11

04

Pharmacology

Systems pharmacology and drug development

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FOUNDER TARIQ ABDULLAH EDITORIAL EXECUTIVE EDITOR TARIQ ABDULLAH FOUNDING EDITOR MUNIBA FAIZA SECTION EDITORS FOZAIL AHMAD ALTAF ABDUL KALAM MANISH KUMAR MISHRA SANJAY KUMAR NABAJIT DAS REPRINTS AND PERMISSIONS You must have permission before reproducing any material from Bioinformatics Review. Send E-mail requests to info@bioinformaticsreview.com. Please include contact detail in your message.

BACK ISSUE Bioinformatics Review back issues can be downloaded in digital format from bioinformaticsreview.com at $5 per issue. Back issue in print format cost $2 for India delivery and $11 for international delivery, subject to availability. Pre-payment is required CONTACT PHONE +91. 991 1942-428 / 852 7572-667 MAIL Editorial: 101 FF Main Road Zakir Nagar, Okhla New Delhi IN 110025 STAFF ADDRESS To contact any of the Bioinformatics Review staff member, simply format the address as firstname@bioinformaticsreview.com PUBLICATION INFORMATION Volume 1, Number 1, Bioinformatics Reviewâ„¢ is published monthly for one year (12 issues) by Social and Educational Welfare Association (SEWA)trust (Registered under Trust Act 1882). Copyright 2015 Sewa Trust. All rights reserved. Bioinformatics Review is a trademark of Idea Quotient Labs and used under license by SEWA trust. Published in India

EDITORIAL

Bioinformatics Review (BiR): Bridging Between The Two Worlds Informatics and Biology are two sciences which are as different from each other as possible. One runs on the core concept of variation and another on strict reasoning. But still, these two have combined in a most natural way under the realm of “Bioinformatics”. For a biologist today it’s difficult to imagine a world without all biological databases and further no branch to decipher the huge enigma that it brings. Bioinformatics Review (BiR) journal is a platform to discover the latest happenings in this melting pot of two varied fields.

Dr. Roopam Sharma

Honorary Editor

The era of “omics” kick-started with the drafting of Human Genome Project (HGP) in 2003. Since then, a number of technological advancements especially, NGS has been generating mind-boggling data for the knowledge banks. Latest inventions like single-cell transcriptomics or metagenomics of most unusual habitats show how the evolution of technological advancements is directly resulting in breakthroughs in biological sciences.

Among various areas of biology which has benefited from these advancements is Pathology. In fact, deciphering the molecular and genetic basis of diseases in humans was the guiding force behind human genome sequencing Project. Bioinformatics has led to an impressive increase in recognition of possible pathogenic factors in varied systems, so much so that new techniques are being devised to increase the speed to actually test these factors in the wet lab. If we consider computationally, smaller but ever-changing genomes and transcriptomes of these pathogens, make them a much suitable candidate to test out many hypotheses for Bioinformatics studies. Effector Bioinformatics involves building custom pipelines for distinct species based on characteristics of effectors and size of the genome involved. These can be based on

Letters and responses: info@bioinformaticsreview.com

EDITORIAL

Homology or feature extraction or both, e.g. discovery of RXLR motifs in Oomycete effectors allowed many more effectors to be identified. This collaboration of two sciences for plant pathology has led to the development of many general use platforms like Broad-Fungal Genome Initiative, EuPathDB, PhytoPath and so on, but there is much need of developing specified resources like PHIbase for specific areas like effector biology. The use of machinelearning techniques like artificial neural network approach (which is actually based on biological neural networks) really shows how the two branches are so distinct yet so intertwined. All in all, it’s a brave new world where artificial communication is not only stimulating but also helping us understand the communication (between host and pathogen) going within the realm of life.

In this issue, BiR focusses on reviews related to some of the very basic techniques which have been used in computational biology and its applications in various biological studies. We look forward to continued support from our readers and contributors. For suggestions and feedback, do write to us at info@bioinformaticsreview.com

PHYLOGENETICS

Why to study Evolution?

Image Credit: Google Images

“Understanding evolution is also central to the advancement of medicine. Indeed, the entire field of “evolutionary medicine” is devoted to using the principles of evolution to study and treat human illness and disease.”

nderstanding evolution is critical for understanding biology. As the preeminent scientist Theodosius Dobzhansky stated, “Nothing in biology makes sense except in the light of evolution.” Evolution is the only scientific explanation for the diversity of life. It explains the striking similarities among vastly different forms of life, the changes that occur within populations, and the development of new life forms.

Excluding evolution from the science curricula or compromising its treatment deprives students of this fundamental and unifying scientific concept to explain the natural world.

U

The principles of evolution underlie improvements in crops, livestock, and farming methods. Natural selection accounts for the rise in pesticide resistance among

agricultural pests and informs the design of new technologies to protect crops from insects and disease. Scientists are applying lessons from evolutionary biology to environmental conservation: plants and bacteria adapted to polluted environments are being used to replenish lost vegetation and to clean up toxic environments. Species from microbes to mammals adapt to climate change; studying the mechanism and rate of these changes can help conservation experts formulate appropriate measures to protect species facing extinction.

disease. Concepts such as adaptation and mutation inform therapies and strategies to combat pathogens, including influenza.

Models developed by evolutionary biologists have shed light on genetic variation that may account for an increased risk of Alzheimer’s and coronary heart disease.

Knowing the evolutionary relationships among species allows scientists to choose appropriate organisms for the study of diseases, such as HIV. Scientists are even using the principles of natural selection to identify new drugs for detecting and treating diseases such as cancer.

Understanding evolution is also central to the advancement of medicine. Indeed, the entire field of “evolutionary medicine” is devoted to using the principles of evolution to study and treat human illness and

Studying evolution is an excellent way for students to learn about the process of scientific inquiry.

Evolution offers countless and diverse examples of the ways scientists gather and analyze Bioinformatics Review | 7

information, test competing hypotheses, and ultimately come to a consensus about explanations for natural phenomena. Understanding science is essential for making informed decisions and has become increasingly important for innovation and competitiveness in the 21st-

century workplace. It is critical, therefore, that students receive a sound science education including evolution.

Removing evolution from the science classroom or allowing it to be compromised not only deprives students of a fundamental tenet of

biology and medicine, but it will undermine their understanding of how scientific knowledge is amassed.

Bioinformatics Review | 8

PHARMACOLOGY

Systems pharmacology and drug development

Image Credit: Google Images

“The systems pharmacology has an enormous impact on drug development and drug usage which can provide means to develop drugs for the diseases such as type 2 diabetes, cancer, other metabolic and immune disorders which are not susceptible to simple treatment approaches involving the use of antibiotics.�

s

ystems pharmacology is an emerging area in the field of medicinal chemistry and pharmacology which utilizes systems network to understand drug action at the organ and organism level. It applies the computational and experimental systems biology approaches to pharmacology, which includes network analyzes at multiple biological organization levels facilitating the understanding of both therapeutic and adverse effects of the drugs. Nearly a decade ago, the term systems pharmacology was used to define the drug action in a specific organ system such as reproductive pharmacology [1], but to date, it has been expanded to different organ and organism levels [2].

Network approaches are useful in organizing large biological datasets and obtaining useful information. A network represents the relationship between the nodes, which represents proteins [3], genes [4], drugs [5], and diseases [6,7]. The nodes are connected through the edges which represent proteinprotein interaction [3], drug-drug interaction [8], and so on (in the context of drug action).

The computation tools provide network analysis methods for identifying potential drug targets, which can later be used to develop drugs. Network analysis methods provide the number of proteins getting affected by targeting a particular protein in the network and help to identify whether the protein participates in motif or not [9]. These

methods can be used to predict the positive and negative effects of the potential drug. For example, a protein with high connectivity may cause lethal effects, and the disease genes with low connectivity are lethal but may lead to disease phenotype [10]. The positive and negative effects of the potential drugs can be studied by analyzing the proximity of a protein to another protein which is involved in the long QT interval (a measure of the time between the start of the Q wave and the end of the T wave in the heart's electrical cycle) event. A network algorithm applying MFPT (mean first passage time) can be used to identify the protein that may lead to long QT interval [11]. The nearest neighbor method can be used which assesses the distance by measuring the

Bioinformatics Review | 9

shortest path between the protein of interest to any other known proteins leading to long QT event.

Another advantage of using systems network in the context of the targets involved in therapeutic and adverse actions. Network systems can identify physiologically relevant targets and determine the other nodes with which these targets show their action. Similarly, there are some databases available which store information related to all drugs and their effects such as DrugBank [8]. A database, FDA Adverse Events Reporting System (AERS) is a publicly available database which stores drug-induced adverse events in the patients using one or more drugs. It helps in building organ-level and organism-level networks through which the concurrence of therapeutics and related adverse phenotype can be identified.

The network analysis can identify the potential drug targets at the macromolecular level, they need to be filtered by structural criteria to determine their ability at the atomiclevel. The development of new drugs requires deep insight into the structures of the targets and the drugs which help to study the atomic-level interactions.

The binding pockets of the target are analyzed in order to determine the

best fit pocket for the drug, in some cases when the binding pockets do not accept the drug, then the structure of the drug is modified.

This is a well-studied area in pharmacology which uses computer aided drug designing (CADD). This helps in either activation or inhibition of the target depending on the need. Further, the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties can be predicted.

The systems pharmacology has an enormous impact on drug development and drug usage which can provide means to develop drugs for the diseases such as type 2 diabetes, cancer, other metabolic and immune disorders which are not susceptible to simple treatment approaches involving the use of antibiotics.

References 1.

Brunton, L., Lazo, J., & Parker, K. G. (2005).

Gilman's The Pharmacological Basis of Therapeutics. 11th.

2.

Zhao, S., & Iyengar, R. (2012). Systems pharmacology: network analysis to identify multiscale mechanisms of drug action.

Annual review of pharmacology and toxicology, 52, 505-521.

3.

Stark, C., Breitkreutz, B. J., ChatrAryamontri, A., Boucher, L., Oughtred, R., Livstone, M. S., ... & Reguly, T. (2011). The BioGRID interaction database: 2011 update.

Nucleic acids research, 39(suppl 1), D698D704.

4.

Davidson, J. R., Zhang, W., Connor, K. M., Ji, J., Jobson, K., Lecrubier, Y., ... & Stein, D. J. (2010). Review: A psychopharmacological treatment algorithm for generalised anxiety disorder (GAD).

Journal of Psychopharmacology, 24(1), 3-26.

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Zhao, S., & Li, S. (2010). Network-based relating pharmacological and genomic spaces for drug target identification. PloS one, 5(7), e11764.

6.

Wang, X., Gulbahce, N., & Yu, H. (2011).

Network-based methods for human disease gene prediction. Briefings in functional genomics, 10(5), 280-293.

7.

Vidal, M., Cusick, M. E., & Barabasi, A. L. (2011). Interactome networks and human disease. Cell, 144(6), 986-998.

8.

Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., ... & Hassanali, M. (2008). DrugBank: a knowledgebase for drugs, drug actions and drug targets.

Nucleic acids research, 36(suppl 1), D901D906.

9.

Ma'ayan, A., Jenkins, S. L., Neves, S., Hasseldine, A., Grace, E., Dubin-Thaler, B., ... & Kershenbaum, A. (2005). Formation of regulatory patterns during signal propagation in a mammalian cellular network. Science, 309(5737), 1078-1083.

10. Wang, X., Gulbahce, N., & Yu, H. (2011).

Network-based methods for human disease gene prediction. Briefings in functional genomics, 10(5), 280-293.

11. Berger, S. I., Ma’ayan, A., & Iyengar, R. (2010).

Systems pharmacology of arrhythmias. Science signaling, 3(118), ra30.

Bioinformatics Review | 10

TRANSCRIPTOMICS

Bioinformatics Challenges and Advances in RNA interference Image Credit: Stock Photos

“RNA interference was discovered by Andrew Fire and Craig C. Mello in 1998 and jointly received the Nobel Prize for physiology and medicine in 2006 for the discovery of “RNA interference” [6]. RNA interference or RNAi is a posttranscriptional gene silencing (PTGS) mechanism in eukaryotes whose role is important in various stages of life or growth conditions.”

R

NA interference is a posttranscriptional gene regulatory mechanism to down-regulate the gene expression either by mRNA degradation or by mRNA translation inhibition. The mechanism involves a small partially complementary RNA against the target gene. To perform the action, it also requires a class of dedicated proteins to process these primary RNAs into mature microRNAs. The guide sequence determines the specificity of the miRNA. Therefore, the knowledge of the guide sequence is crucial for predicting its targets and also exploiting the sequence to create a new regulatory circuit. In this short review, we will briefly discuss the

role and challenges in miRNA research for unveiling the target prediction by bioinformatics and to foster our understanding and applications of RNA interference.

of gene expression. In higher organisms, these mechanisms have evolved into complex and multitiered but well-structured systems to regulate gene expression.

Regulation of gene expression is the key component to any cell or organism. Each process, starting with birth to development and growth requires regulation of genes at a given time. The regulation of promoter activation by posttranscriptional regulation by regulatory RNAs [1], regulatory elements within the UTR [2], mRNA degradation by ribonucleases [3, 4] or by protein degradation [5] provide comprehensive regulation

RNA interference was discovered by Andrew Fire and Craig C. Mello in 1998 and jointly received the Nobel Prize for physiology and medicine in 2006 for the discovery of “RNA interference” [6]. RNA interference or RNAi is a post-transcriptional gene silencing (PTGS) mechanism in eukaryotes whose role is important in various stages of life or growth conditions. It can modulate the stability of mRNA or inhibit its translation.

These effector

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molecules are named depending on their origin. While short interfering RNA (siRNA) are exogenously synthesized mature RNA or viral encoded RNA, microRNA (miRNAs) are endogenously encoded RNAs [7] and are required for the basic physiological function of the cell.

These RNAs have unique secondary structure and exhibit regulatory functions exclusively. While the siRNAs can have perfect complementarity to their target, miRNAs share only partially complementary with their target, therefore, allowing outputs different from that of siRNA. The identification of new miRNAs and their targets are important as they not only control basic physiological function but are also associated with various human diseases. For example, miR-600 has been found to control cell proliferation, migration, and invasion in human CRC cell lines [8]. Besides studies on human cell lines, miRNA investigations are also focused extensively on insects, and plants development [9-11].

Synthesis and mechanism of action While miRNAs are synthesized during various stages of growth, siRNA could be viral or synthetic in origin. Short hairpin RNA (shRNAs) are the class of regulatory RNA

similar to miRNA in design, structure, and processing [6, 7, 12], and are expressed from a plasmid or through viral vectors. Although regulatory RNAs could be of any type, their mechanism of action in all cases remains same, because the siRNA or shRNA are designed such a way that they can integrate into the host RNAi pathway.

called the RNA interference silencing complex (RISC), which can recognize complementary target sequence on 3' untranslated region (3'UTR) of the mRNA. A 2-8 nucleotide sequence at 5’end called seed sequence provides the specificity to the target mRNA. RISC complex either degrades the bound mRNA or block translation [12, 14, 15] (Fig 1).

A miRNA is transcribed as a primary RNA (pri-miRNA) of large size inside the nucleus. pri-miRNA with overhangs at both 5' and 3' sequence forms a hairpin loop and a stem structure (Fig 1). These structures serve as a template for initial processing by a specific riboendonucleases, resulting in a 60-70 nucleotide transcript with a hairpin structure known as pre-miRNA. PremiRNA is transported from the nucleus to the cytoplasm, where a second endonuclease reaction cleaves off the hairpin loop from premiRNA, into a short double-stranded RNA product (miR-miR*) [6, 13]. The resulting miR-miR* duplex is partially complementary and contains 5' phosphate and 3' overhang of two nucleotides. The strands of this duplex are called “Guide strand” and “Passenger-strand.”

Argonaut protein shortens the passenger strand, and the miR-guide strand is retained (Fig1). The guide-miRNA along with Argonaut complex is

Fig 1- Biogenesis of microRNA and target repression.

Identification RNAs/miRNAs

of

regulatory

Initial studies of miRNA discoveries were based on genetic screening [16]. The screening methods were time-consuming and less efficient. In recent years, the most common methods for detection and identification of novel miRNA include RNA sequencing (RNA-Seq), transposon mutagenesis [17], Watson-Crick base pairing with probes (northern hybridization, inSitu hybridization, and RT-PCR), cloning and splinted ligation.

Sequencing methods use robust and high throughput technology. While RNA sequencing provides direct identification of novel transcripts, other methods of hybridization need prior knowledge of miRNA sequence.

For experimental detection, use of locked nucleic acid (LNA) probes improves hybridization

Bioinformatics Review | 12

efficiency and specificity [18] and helps in the validation of the predicted transcripts. Different methods have their advantages and disadvantages. For example, besides detecting the miRNA using northern hybridization, quantitation can also be performed by direct detection.

Additionally, PCR-based detection is favored for low copy templates, which is enabled by many log amplifications. miRNA identification and target prediction: the role of bioinformatics Although predicting novel RNAi genes through a computational approach presents a challenge.

However, the methods are good for shortlisting the putative candidates that can be verified through experimental methods. The criteria used for candidate miRNA prediction are based on the comparative genomics with related organisms, genetic loci, the size of the transcript, the structure of primary RNA and free energy of the structure. Report of various strategies used for prediction of candidate micro-RNA-like microconserved elements (MCE) could be one of the many approaches [19].

The efficiency of prediction can be achieved through several modifications in the algorithms, as

well as using the experimental evidence. A list of such tools has been reviewed by Salim and Chandra [20]. Some of these tools are standalone and can function independently whereas others are based on web services or require other platforms. While all these tools used for prediction and validation of targets bring fewer publications, a huge number of research publications comes from experimental data (Fig 2), suggesting exponential growth in RNA interference research.

Fig 2 Growth in miRNA research as compared to that of miRNA/miRNA target prediction.

Some bioinformatics analysis includes unraveling counterparts and critical components of the RNAi pathway in various organisms. The genes involved in miRNA pathway of many organisms have been experimentally identified, and the information can be accessed from a registry that was constructed by sequencing small RNAs (~22 nt) and mapping them to the genome [13].

The miRNA sequence can occur in intergenic regions, in introns of protein-coding regions or exons and introns of noncoding genes. Through bioinformatics tools, the miRNA genes can be identified by using the known biases in targeting strands.

This reduces the unwieldy work of analyzing the whole genome of the organism, although, it could be difficult to achieve easily in the case of miRNA, as there are significant mismatches between the miRNA single strand and the cognate mRNA. For this purpose, there are programs like the MiRscan57 and MiRseeker [21] that use the conserved regions of the stem-loop structures to identify novel miRNA genes across species. Since miRNAs work jointly with other members of pathways, the study of tissue specificity of genes can help in reviewing miRNA and their targets.

Genome sequences also provide ways to predict miRNAs, but verification of expression need to be done by sequencing of RNA species or by other methods of choice.

For RNA interference, a strong binding of the 5' end (the first eight base pairs) of the mature miRNA to the target 3' UTR sequence is essential. Silencing efficiency is affected and reduced by G:U wobble pairing in the interacting region.

Algorithms and models have been developed for miRNA target interactions, based on experimental evidence. Since the microarray experiments show that miRNAs exhibit tissue-specific expression patterns, correlation of mRNA with miRNA expression can help narrow

Bioinformatics Review | 13

down the predicted targets.

Moreover, based on the patterns of expression, the miRNAs and mRNAs can be clustered. Several online analysis tools for miRNA target prediction have been documented in recent past [13, 20, 22-24]. Together with the miRNA identification, the prediction of their promoters is an ever-present challenge, although many underlying assumptions like homologies, UTR location of target sites, can help in identifying good targets. Therefore, caution must be taken for false positive predictions [25] as the coverage of all possible sites, or complete sequence may not be possible, some of which may be unique to certain species and may not follow the rules [22, 23]. siRNA/shRNA design and target prediction The siRNAs originate from transgenes, viruses, and transposons and form perfect complementary double strand RNA precursors (dsRNAs) [6, 21]. Due to the perfect complementarity, they can silence the same gene from which they originate. The siRNA precursor generates multiple siRNAs that can be transmitted among cells [23, 24].

Processing of siRNA requires their dicing and sorting by Argonaut proteins.

The siRNAs get incorporated into RISC and silence

genes that are complementary to the RISC incorporated strand.

Because of the strand bias showed

by RISC, the strand which is more unstable, or whose end is easier to open, is taken up by the RISC [12, 14]. Also, efficient silencing also depends on the base composition along the siRNA active strand. However, the siRNAs can also show off-target effects and silence unintended targets. Thus, a rational design of siRNA is critical.

A unique siRNA selection program, siRNA Scanner, automatically selects siRNA from the given RNA sequences.

This program is developed in PERL (Practical Extract Report Language, 5.8.8.6 Build 820), uses a fuzzy logic-based system to calculate siRNA qualities and is accessible in a command line interface [26]. Another program, siRNA scanner ensures minimum user interaction and faster algorithm, thus, is fit for selecting siRNA for gene expression studies.

The program also evaluates each possible siRNA candidate extensively and checks for all potential candidates from the given template sequence. siDirect (http://design.RNAi.jp/) is an online software for computing highly effective siRNA sequences with target-specificity for RNAi in

mammalian systems [27]. siDirect software prevents off-target effects that the most BLAST programs may not achieve. siRNAs for some common genes are easily available and are routinely used in experiments to silence genes. To avoid the off-target effects of siRNA constructs, they must be checked rigorously for being unique on nonredundant mRNA database (excluding splice variants). The other pre-requisites for the siRNA design are: Less than 30–60 percent GC content or the sequences 4 As, 4 Ts, 4 Gs or 4 Cs are eliminated and with TT overhangs on the 3' ends of each strand [4, 6, 12, 14]. There are several websites that follow these criteria and allow the design of siRNAs. Short hairpin RNAs or shRNAs are artiďŹ cial constructs that can be inserted into a genome and expressed like endogenous genes [6, 7, 12, 14]. The primary transcripts fold back to form a dsRNA, mimicking native pre-RNA structure.

This is directly accepted and used by the RISC to find target mRNA. The context of a known miRNA and siRNA-based designs can be used to construct these shRNAs. Several shRNAs libraries are now commercially available [21, 26, 28].

The libraries allow large-scale studies with siRNAs that are

Bioinformatics Review | 14

otherwise prohibitively expensive.

To start with, all the shRNAs that can putatively silence a gene of choice are picked from the library and are injected into the cell. Following the cell growth for a few generations, the shRNAs that have depleted are investigated. Finally, the mRNA targets of the depleted shRNAs are sought, and further studies are planned.

There are several bioinformatics limitations associated with the use of shRNA. For a more accurate analysis, understanding outcomes of library screening are essential, which can be achieved by simulation of shRNA libraries when used in populations of cells [29]. A central repository of shRNA constructs like the RNAi Codex [29, 30], which can help track results, identify the performance patterns of shRNA facilitate in finding constructs from a variety of sources.

Also, microarray probes need to be designed for the more efficient study of shRNA expression in cell populations. Finally, techniques that can analyze results of silencing of genes either singly or in groups need to be developed.

What the future holds There has been a constant need to develop new algorithms and tools

for designing efficient and specific siRNAs [21, 26, 31]. One few of the important on-going developments includes formulating algorithms to find off-targets of siRNAs design for genome-wide studies. It is also essential to maintain a unified data source that presents optimal conditions as well as the design failures of siRNAs used for validation or experimentation. Easy access to such data library would definitely help in refining algorithms and would save much of effort that could otherwise go waste by designing non-functional siRNAs. The information gained by studies on siRNA and miRNA designs can also be used to improvise on the other studies. Using siRNAs in experiments to mimic the effects of drugs, a field that exhibits immense potential because it could reduce the cost of large-scale screening.

Many programs have been developed to predict the target of miRNA and design siRNAs. These algorithms assess the sequence similarity between miRNA and target RNA and the free energy change that occurs during the interaction. For target predictions, the seed sequences are looked upon the untranslated region (UTR) of mRNA. In humans, most of the seed sequence is found to be located at

the 3' end of the mRNA where it can bind and lead to either degradation of translation inhibition of the mRNA. In the list of web applications reviewed by Akhtar et al. [32], one can find a wellcategorized list of tools and databases to study or different aspects of miRNA. These are for validation of miRNA (DIANATarBase, miRTarBase, miRecords) [33], correlating miRNA expression (StarBase, MiRonTop) metabolic pathways (EiMMO) regulator network (MiGator), transcription factor interactions (TransmiR) and prediction of cellular target of host and viral miRNA are few examples from the list.

Computational analysis can correct some experimental biases that may exist while screening the experimental data. For this purpose, Datta et al., [34] have developed a web-based platform named Comprehensive Analysis of RNAi Data (CARD) for integrated analysis and visualization of RNAi screen data. Other tools like R and Python code9 seed analysis predict offtarget siRNAs by testing whether siRNAs with shared seed sequence have significantly higher/lower scores compared with the rest [34].

The likely off-targeted genes can be identified by MATLAB code and webservice

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(http://www.flyrnai.org/gess/) [34].

RNAiCut Identifies optimal threshold for hit-selection by integrating screen data with PPI networks [35].

Expression filter in the algorithm is used for the flagging and filtering of the genes based on their expression status.

A completely automated search program for siRNAs, inSiDE, contains a comprehensive set of rules for selecting of siRNA candidates (mentioned earlier in the “siRNAs” section) can be applied and allows for significantly high specificity [36].

After selecting potential siRNA candidates with the optimal functional properties, putative unspecific matches, which can cause cross-hybridization, are checked in the databases containing a unique entry for each gene. These truly nonredundant databases are constructed from the genome annotations (Ensembl) which is followed by examining other factors such as the presence of single nucleotide polymorphisms, intron/exon boundaries, transcript specificity, before the design of siRNAs [35, 36].

SPICE is another web-based tool that supports RNAi screening through comprehensive inquiry and automates several challenging tasks during evaluation of the short RNAs

obtained by RNAi screening. This tool identifies the shRNA sequence in the input sequence, the target genes as well as the biological information in the database, and finally, prepare search result files that can be accessed locally. The system facilitates RNAi screening, which requires sequence analysis after screening [37].

In addition to carrying out statistical analyses and experimental follow-up studies of RNAi screen data, the application of various bioinformatics approaches can greatly aid in distinguishing between highconfidence and low-confidence screen hits. One common approach is to analyze the data in aggregate using a gene-set enrichment or related algorithms, such as using the DAVID database (the database for annotation, visualization, and integrated discovery) from the US National Institutes of Health, COMPLEAT and other software to detect gene ontology terms, protein complexes or signaling pathways that are enriched in the screen hits compared to controls [38]. These results can be used to confirm low confidence hits, such as hits with borderline statistical scores or hits for which not all RNAi reagents targeting the gene were positive and, conversely, to rule out hits that are the sole representatives of a

category, which might suggest that they are false positives.

Discussion While the RNAi effector molecules function in the regulation of a variety of biological processes and can have an immense impact on various applications, much information about their mechanisms with respect to several physiological and regulatory functions still remain to be clarified.

High-throughput methods have significantly advanced our knowledge of these small RNAs, and bioinformatics tools play a significant role in parallel to address the different aspects of RNAi research. Today, a vast range of tools are available for prediction of miRNA gene and target, network regulation and for siRNA design. While the tools can deliver to the best of their capacity, the predictions need to be linked and validated with experimental evidence.

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