VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA-AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery, a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA.

Potential of siRNA in COVID-19 therapy: Emphasis on in silico design and nanoparticles based delivery

Small interfering RNA (siRNA)-mediated mRNA degradation approach have imparted its eminence against several difficult-to-treat genetic disorders and other allied diseases. Viral outbreaks and resulting pandemics have repeatedly threatened public health and questioned human preparedness at the forefront of drug design and biomedical readiness. During the recent pandemic caused by the SARS-CoV-2, mRNA-based vaccination strategies have paved the way for a new era of RNA therapeutics. RNA Interference (RNAi) based approach using small interfering RNA may complement clinical management of the COVID-19. RNA Interference approach will primarily work by restricting the synthesis of the proteins required for viral replication, thereby hampering viral cellular entry and trafficking by targeting host as well as protein factors. Despite promising benefits, the stability of small interfering RNA in the physiological environment is of grave concern as well as site-directed targeted delivery and evasion of the immune system require immediate attention. In this regard, nanotechnology offers viable solutions for these challenges. The review highlights the potential of small interfering RNAs targeted toward specific regions of the viral genome and the features of nanoformulations necessary for the entrapment and delivery of small interfering RNAs. In silico design of small interfering RNA for different variants of SARS-CoV-2 has been discussed. Various nanoparticles as promising carriers of small interfering RNAs along with their salient properties, including surface functionalization, are summarized. This review will help tackle the real-world challenges encountered by the in vivo delivery of small interfering RNAs, ensuring a safe, stable, and readily available drug candidate for efficient management of SARS-CoV-2 in the future.

In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences

Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.

Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features

BACKGROUND

In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs.

RESULTS

Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data.

CONCLUSIONS

By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

SMRI: A New Method for siRNA Design for COVID-19 Therapy

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spread globally and became a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11390-021-0826-x.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

Evitar: designing anti-viral RNA therapies against future RNA viruses

MOTIVATION

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the threat of emerging respiratory viruses and has exposed the lack of availability of off-the-shelf therapeutics against new RNA viruses. Previous research has established the potential that siRNAs and RNA-targeting CRISPR have in combating known RNA viruses. However, the feasibility and tools for designing anti-viral RNA therapeutics against future RNA viruses have not yet been established.

RESULTS

We develop the Emerging-Virus-Targeting RNA (Evitar) pipeline for designing anti-viral siRNAs and CRISPR Cas13a guide RNA (gRNA) sequences. Within Evitar, we develop Greedy Algorithm with Redundancy and Similarity-weighted Greedy Algorithm with Redundancy to enhance the performance. Time simulations using known coronavirus genomes deposited as early as 10 years prior to the COVID-19 outbreak show that at least three SARS-CoV-2-targeting siRNAs are among the top 30 pre-designed siRNAs. In addition, among the top 19 pre-designed gRNAs, there are three SARS-CoV-2-targeting Cas13a gRNAs that could be predicted using information from 2011. Before-the-outbreak design is also possible against the MERS-CoV virus and the 2009-H1N1 swine flu virus. Designed siRNAs are further shown to suppress SARS-CoV-2 viral sequences using in vitro reporter assays. Our results support the utility of Evitar to pre-design anti-viral siRNAs/gRNAs against future viruses. Therefore, we propose the development of a collection consisting of roughly 30 pre-designed, safety-tested and off-the-shelf siRNA/CRISPR therapeutics that could accelerate responses to future RNA virus outbreaks.

AVAILABILITY AND IMPLEMENTATION

Codes are available at GitHub (https://github.com/dingyaozhang/Evitar).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MMV-db: vaccinomics and RNA-based therapeutics database for infectious hemorrhagic fever-causing mammarenaviruses

The recent viral outbreaks and the current pandemic situation urges us to timely address any emerging viral infections by designing therapeutic strategies. Multi-omics and therapeutic data are of great interest to develop early remedial interventions. This work provides a therapeutic data platform (Mammarenavirus (MMV)-db) for pathogenic mammarenaviruses with potential catastrophic effects on human health around the world. The database integrates vaccinomics and RNA-based therapeutics data for seven human pathogenic MMVs associated with severe viral hemorrhagic fever and lethality in humans. Protein-specific cytotoxic T lymphocytes, B lymphocytes, helper T-cell and interferon-inducing epitopes were mapped using a cluster of immune-omics-based algorithms and tools for the seven human pathogenic viral species. Furthermore, the physiochemical and antigenic properties were also explored to guide protein-specific multi-epitope subunit vaccine for each species. Moreover, highly efficacious RNAs (small Interfering RNA (siRNA), microRNA and single guide RNA (sgRNA)) after extensive genome-based analysis with therapeutic relevance were explored. All the therapeutic RNAs were further classified and listed on the basis of predicted higher efficacy. The online platform (http://www.mmvdb.dqweilab-sjtu.com/index.php) contains easily accessible data sets and vaccine designs with potential utility in further computational and experimental work. Conclusively, the current study provides a baseline data platform to secure better future therapeutic interventions against the hemorrhagic fever causing mammarenaviruses. Database URL: http://www.mmvdb.dqweilab-sjtu.com/index.php.

HantavirusesDB: Vaccinomics and RNA-based therapeutics database for the potentially emerging human respiratory pandemic agents

Hantaviruses are etiological agents of several severe respiratory illnesses in humans and their human-to-human transmission has been reported. To cope with any potential pandemic, this group of viruses needs further research and a data platform. Therefore, herein we developed a database "HantavirusesDB (HVdb)", where genomics, proteomics, immune resource, RNAi based therapeutics and information on the 3D structures of druggable targets of the Orthohantaviruses are provided on a single platform. The database allows the researchers to effectively map the therapeutic strategies by designing multi-epitopes subunit vaccine and RNA based therapeutics. Moreover, the ease of the web interface allow the users to retrieve specific information from the database. Because of the high quality and excellent functionality of the HVdb, therapeutic research of Hantaviruses can be accelerated, and data analysis might be a foundation to design better treatment strategies targeting the hantaviruses. The database is accessible at http://hvdb.dqweilab-sjtu.com/index.php.

Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm

Background

Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway.

Method

The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models.

Results

The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset.

Conclusion

We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver “STAT3In” (https://webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.

Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning

Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC₅₀ values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T²⁷⁴) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

Antiviral efficacy of short-hairpin RNAs and artificial microRNAs targeting foot-and-mouth disease virus

RNA interference (RNAi) is a well-conserved mechanism in eukaryotic cells that directs post-transcriptional gene silencing through small RNA molecules. RNAi has been proposed as an alternative approach for rapid and specific control of viruses including foot-and-mouth disease virus (FMDV), the causative agent of a devastating animal disease with high economic impact. The aim of this work was to assess the antiviral activity of different small RNA shuttles targeting the FMDV RNA-dependent RNA polymerase coding sequence (3D). Three target sequences were predicted within 3D considering RNA accessibility as a major criterion. The silencing efficacy of short-hairpin RNAs (shRNAs) and artificial microRNAs (amiRNAs) targeting the selected sequences was confirmed in fluorescent reporter assays. Furthermore, BHK-21 cells transiently expressing shRNAs or amiRNAs proved 70 to >95% inhibition of FMDV growth. Interestingly, dual expression of amiRNAs did not improve FMDV silencing. Lastly, stable cell lines constitutively expressing amiRNAs were established and characterized in terms of antiviral activity against FMDV. As expected, viral replication in these cell lines was delayed. These results show that the target RNA-accessibility-guided approach for RNAi design rendered efficient amiRNAs that constrain FMDV replication. The application of amiRNAs to complement FMDV vaccination in specific epidemiological scenarios shall be explored further.

CytomegaloVirusDb: Multi-omics knowledge database for cytomegaloviruses

Cytomegalovirus infection is a significant health concern and need further exploration in immunologic response mechanisms during primary and reactivated CMV infection. In this work, we evaluated the whole genomes and proteomes of different CMV species and developed an integrated open-access platform, CytomegaloVirusDb, a multi-Omics knowledge database for Cytomegaloviruses. The resource is categorized into the main sections "Genomics," "Proteomics," "Immune response," and "Therapeutics,". The database is annotated with the list of all CMV species included in the study, and available information is freely accessible at http://www.cmvdb.dqweilab-sjtu.com/index.php. Various parameters used in the analysis for each section were primarily based on the whole genome or proteome of each specie. The platform provided datasets are open to access for researchers to obtain CMV species-specific information. This will help further to explore the dynamics of CMV-specific immune response and therapeutics. This platform is a useful resource to aid in advancing research against Cytomegaloviruses.

Compelling Evidence for the Activity of Antiviral Peptides against SARS-CoV-2

Multiple outbreaks of epidemic and pandemic viral diseases have occurred in the last 20 years, including those caused by Ebola virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The emergence or re-emergence of such diseases has revealed the deficiency in our pipeline for the discovery and development of antiviral drugs. One promising solution is the extensive library of antimicrobial peptides (AMPs) produced by all eukaryotic organisms. AMPs are widely known for their activity against bacteria, but many possess additional antifungal, antiparasitic, insecticidal, anticancer, or antiviral activities. AMPs could therefore be suitable as leads for the development of new peptide-based antiviral drugs. Sixty therapeutic peptides had been approved by the end of 2018, with at least another 150 in preclinical or clinical development. Peptides undergoing clinical trials include analogs, mimetics, and natural AMPs. The advantages of AMPs include novel mechanisms of action that hinder the evolution of resistance, low molecular weight, low toxicity toward human cells but high specificity and efficacy, the latter enhanced by the optimization of AMP sequences. In this opinion article, we summarize the evidence supporting the efficacy of antiviral AMPs and discuss their potential to treat emerging viral diseases including COVID-19.

Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

HPVomics: An integrated resource for the human papillomavirus epitome and therapeutics

Human papillomaviruses (HPVs) belongs to the Papillomaviridae family, which is divided into high-risk (HR), and low-risk (LR) HPVs based on their disease-causing competence. HR-HPVs 16 and 18 are known to cause distinct carcinomas like cervical and head and neck, whereas LR-HPVs are commonly associated with the genital warts. We have developed an integrative platform; HPVomics dedicated to the potential therapeutic regimens targeting all HPV genes including oncoproteins E6, E7 and E5. We primarily focused on eighteen HR-HPVs and eleven LR-HPVs. It mainly deals with therapeutically imperative elements, i.e., vaccine epitopes, siRNAs, sgRNAs, and anti-viral peptides. Simultaneously, it also comprises of genome browser, whole-genome sequences and annotation of HPVs with searching and filtering capabilities. Moreover, we have also developed an integrated support vector machine (SVM) based computational algorithm "HPVepi" for the prediction of HPV epitome. We hope that HPVomics (http://bioinfo.imtech.res.in/manojk/hpvomics/) will assist the scientific community engaged in HPV research.

siRNA Design to Silence the 3'UTR Region of Zika Virus

The disease caused by the Zika virus (ZIKV) has positioned itself as one of the main public health problems in Mexico. One of the main reasons is it causes microcephaly and other birth defects. The transmission of ZIKV is through Aedes aegypti and Ae. albopictus mosquitoes, which are found in a larger space of the national territory. In addition, it can also be transmitted via blood transfusion, sexual relations, and maternal-fetal route. So far, there are no vaccines or specific treatments to deal with this infection. Currently, some new therapeutics such as small interfering RNAs (siRNAs) are able to regulate or suppress transcription in viruses. Therefore, in this project, an in silico siRNA was designed for the 3'UTR region of ZIKV via bioinformatics tools. The designed siRNA was synthesized and transfected into the C6/36 cell line, previously infected with ZIKV in order to assess the ability of the siRNA to inhibit viral replication. The designed siRNA was able to inhibit significantly (p < 0.05) ZIKV replication; this siRNA could be considered a potential therapeutic towards the disease that causes ZIKV and the medical problems generated.

CoronaVR: A Computational Resource and Analysis of Epitopes and Therapeutics for Severe Acute Respiratory Syndrome Coronavirus-2

In December 2019, the Chinese city of Wuhan was the center of origin of a pneumonia-like disease outbreak with an unknown causative pathogen. The CDC, China, managed to track the source of infection to a novel coronavirus (2019-nCoV; SARS-CoV-2) that shares approximately 79.6% of its genome with SARS-CoV. The World Health Organization (WHO) initially declared COVID-19 as a Public Health Emergency of International Concern (PHEIC) and later characterized it as a global pandemic on March 11, 2020. Due to the novel nature of this virus, there is an urgent need for vaccines and therapeutics to control the spread of SARS-CoV-2 and its associated disease, COVID-19. Global efforts are underway to circumvent its further spread and treat COVID-19 patients through experimental vaccine formulations and therapeutic interventions, respectively. In the absence of any effective therapeutics, we have devised h bioinformatics-based approaches to accelerate global efforts in the fight against SARS-CoV-2 and to assist researchers in the initial phase of vaccine and therapeutics development. In this study, we have performed comprehensive meta-analyses and developed an integrative resource, “CoronaVR” (http://bioinfo.imtech.res.in/manojk/coronavr/). Predominantly, we identified potential epitope-based vaccine candidates, siRNA-based therapeutic regimens, and diagnostic primers. The resource is categorized into the main sections “Genomes,” “Epitopes,” “Therapeutics,” and Primers.” The genome section harbors different components, viz, genomes, a genome browser, phylogenetic analysis, codon usage, glycosylation sites, and structural analysis. Under the umbrella of epitopes, sub-divisions, namely cross-protective epitopes, B-cell (linear/discontinuous), T-cell (CD4⁺/CD8⁺), CTL, and MHC binders, are presented. The therapeutics section has different sub-sections like siRNA, miRNAs, and sgRNAs. Further, experimentally confirmed and designed diagnostic primers are earmarked in the primers section. Our study provided a set of shortlisted B-cell and T-cell (CD4⁺ and CD8⁺) epitopes that can be experimentally tested for their incorporation in vaccine formulations. The list of selected primers can be used in testing kits to identify SARS-CoV-2, while the recommended siRNAs, sgRNAs, and miRNAs can be used in therapeutic regimens. We foresee that this resource will help in advancing the research against coronaviruses.

Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma

INTRODUCTION

Recently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor's state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.

MATERIALS AND METHODS

In this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.

RESULTS

Single gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62-0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84-1), 0.76 (95% CI: 0.66-0.85) and 0.72 (95% CI: 0.61-0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91-1).

CONCLUSION

We identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

NipahVR: a resource of multi-targeted putative therapeutics and epitopes for the Nipah virus

Nipah virus (NiV) is an emerging and priority pathogen from the Paramyxoviridae family with a high fatality rate. It causes various diseases such as respiratory ailments and encephalitis and poses a great threat to humans and livestock. Despite various efforts, there is no approved antiviral treatment available. Therefore, to expedite and assist the research, we have developed an integrative resource NipahVR (http://bioinfo.imtech.res.in/manojk/nipahvr/) for the multi-targeted putative therapeutics and epitopes for NiV. It is structured into different sections, i.e. genomes, codon usage, phylogenomics, molecular diagnostic primers, therapeutics (siRNAs, sgRNAs, miRNAs) and vaccine epitopes (B-cell, CTL, MHC-I and -II binders). Most decisively, potentially efficient therapeutic regimens targeting different NiV proteins and genes were anticipated and projected. We hope this computational resource would be helpful in developing combating strategies against this deadly pathogen. Database URL: http://bioinfo.imtech.res.in/manojk/nipahvr/.

Computational Identification of piRNAs Using Features Based on RNA Sequence, Structure, Thermodynamic and Physicochemical Properties

Rationale

PIWI-interacting RNAs (piRNAs) are a recently-discovered class of small non-coding RNAs (ncRNAs) with a length of 21-35 nucleotides. They play a role in gene expression regulation, transposon silencing, and viral infection inhibition. Once considered as "dark matter" of ncRNAs, piRNAs emerged as important players in multiple cellular functions in different organisms. However, our knowledge of piRNAs is still very limited as many piRNAs have not been yet identified due to lack of robust computational predictive tools.

Methods

To identify novel piRNAs, we developed piRNAPred, an integrated framework for piRNA prediction employing hybrid features like k-mer nucleotide composition, secondary structure, thermodynamic and physicochemical properties. A non-redundant dataset (D³³⁴⁹ or D^1684p+1665n) comprising 1684 experimentally verified piRNAs and 1665 non-piRNA sequences was obtained from piRBase and NONCODE, respectively. These sequences were subjected to the computation of various sequence-structure based features in binary format and trained using different machine learning techniques, of which support vector machine (SVM) performed the best.

Results

During the ten-fold cross-validation approach (10-CV), piRNAPred achieved an overall accuracy of 98.60% with Mathews correlation coefficient (MCC) of 0.97 and receiver operating characteristic (ROC) of 0.99. Furthermore, we achieved a dimensionality reduction of feature space using an attribute selected classifier.

Conclusion

We obtained the highest performance in accurately predicting piRNAs as compared to the current state-of-the-art piRNA predictors. In conclusion, piRNAPred would be helpful to expand the piRNA repertoire, and provide new insights on piRNA functions.

Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients

The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP ( http://webs.iiitd.edu.in/raghava/cancerspp/ ).

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

Functional features defining the efficacy of cholesterol-conjugated, self-deliverable, chemically modified siRNAs

Progress in oligonucleotide chemistry has produced a shift in the nature of siRNA used, from formulated, minimally modified siRNAs, to unformulated, heavily modified siRNA conjugates. The introduction of extensive chemical modifications is essential for conjugate-mediated delivery. Modifications have a significant impact on siRNA efficacy through interference with recognition and processing by RNAi enzymatic machinery, severely restricting the sequence space available for siRNA design. Many algorithms available publicly can successfully predict the activity of non-modified siRNAs, but the efficiency of the algorithms for designing heavily modified siRNAs has never been systematically evaluated experimentally. Here we screened 356 cholesterol-conjugated siRNAs with extensive modifications and developed a linear regression-based algorithm that effectively predicts siRNA activity using two independent datasets. We further demonstrate that predictive determinants for modified and non-modified siRNAs differ substantially. The algorithm developed from the non-modified siRNAs dataset has no predictive power for modified siRNAs and vice versa. In the context of heavily modified siRNAs, the introduction of chemical asymmetry fully eliminates the requirement for thermodynamic bias, the major determinant for non-modified siRNA efficacy. Finally, we demonstrate that in addition to the sequence of the target site, the accessibility of the neighboring 3′ region significantly contributes to siRNA efficacy.

An Integrated Chikungunya Virus Database to Facilitate Therapeutic Analysis: ChkVDb

Background:

Chikungunya infection flare-ups have manifested in nations of Africa, Asia, and Europe including Indian and Pacific seas. It causes fever and different side effects include muscle torment, migraine, sickness, exhaustion and rash. It has turned into another, startling general medical issue in numerous tropical African and Asian countries and is presently being viewed as a genuine risk. No antiviral treatment or vaccine is yet available for this ailment. The current treatment is centered just on mitigating its side effects.

Objective:

The objective was to encourage the study on this viral pathogen, by the development of a database dedicated to Chikungunya Virus, that annotates and unifies the related data from various resources. associations while known disease-lncRNA associations are required only.

Method:

It undertook a consolidated approach for Chikungunya Virus genomic, proteomic, phylogenetics and therapeutic learning, involving the entire genome sequences and their annotation utilizing different in silico tools. Annotation included the information for CpG Island, usage bias, codon context and phylogenetic analysis at both the genome and proteome levels.

Results:

This database incorporates information of 41 strains of virus causing Chikungunya infection that can be accessed conveniently as well as downloaded effortlessly. Therapeutics section of this database contains data about B and T cell Epitopes, siRNAs and miRNAs that can be used as potential therapeutic targets.

Conclusion:

This database can be explored by specialists and established researchers around the world to assist their research on this non-treatable virus. It is a public database available from “www.chkv.in”.</P>

A review on current status of antiviral siRNA

Viral diseases like influenza, AIDS, hepatitis, and Ebola cause severe epidemics worldwide. Along with their resistant strains, new pathogenic viruses continue to be discovered so creating an ongoing need for new antiviral treatments. RNA interference is a cellular gene‐silencing phenomenon in which sequence‐specific degradation of target mRNA is achieved by means of complementary short interfering RNA (siRNA) molecules. Short interfering RNA technology affords a potential tractable strategy to combat viral pathogenesis because siRNAs are specific, easy to design, and can be directed against multiple strains of a virus by targeting their conserved gene regions. In this review, we briefly summarize the current status of siRNA therapy for representative examples from different virus families. In addition, other aspects like their design, delivery, medical significance, bioinformatics resources, and limitations are also discussed.

vhfRNAi: a web-platform for analysis of host genes involved in viral infections discovered by genome wide RNAi screens

Knockdown of host genes using high-throughput genome-wide RNA interference screens has identified numerous host factors that affect viral infections, which would be helpful in understanding host-virus interactions. We have developed a vhfRNAi web resource based on genome-wide RNAi experiments for viruses. It contains experimental details of 12 249 entries (host factors + restriction factors) for 18 viruses. Simultaneously, this resource encompasses analysis of overlapping genes, genome wide association studies, gene ontology (GO), pathogen interacting proteins, interaction networks and pathway enrichment. Using overlap analysis, it was found that Influenza A virus shared overlapping host genes with the majority of viruses including Hepatitis C virus and Dengue virus 2. In the genome wide association studies analysis, 429 diseases/traits were mapped, of which obesity-related traits were the most common. GO analysis revealed that the major categories belonged to metabolic processes, molecule transport, signal transduction, proteolysis, etc. In the pathogen interacting protein analysis, protein interaction data from different resources can be explored for further understanding of host-virus biology. By pathway enrichment analysis, a total of 8955 genes were mapped on 303 pathways with most of the hits coming from metabolic pathways. We have found 491 genes that are not essential for the host but essential for the virus and can be targeted to inhibit the virus. These may be explored as potential candidates for drug targets. The resource is freely accessible at and will be useful in understanding host-virus biology as well as identification of targets for the development of antiviral therapeutics.

HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC₅₀ and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.

Inhibition of dengue virus infection by small interfering RNAs that target highly conserved sequences in the NS4B or NS5 coding regions

Dengue fever is one of the most common viral infections in the world. Although a vaccine against dengue virus (DENV) has been approved in several countries, this disease is still considered a public health priority worldwide. The ability of three small interfering RNAs (FG-siRNAs) targeting conserved sequences in the NS4B and NS5 regions of the DENV genome to inhibit DENV replication was tested in vitro in both Vero and C6/36 cells. The FG-siRNAs were effective against DENV-1, -3, and -4, but not DENV-2. A fourth siRNA specifically targeting the NS5 region of the DENV-2 genome (SG-siRNA) was designed and tested against two different DENV-2 strains, showing high levels of inhibition in both mammalian and insect cells.

Time-series oligonucleotide count to assign antiviral siRNAs with long utility fit in the big data era

Oligonucleotides are key elements of nucleic acid therapeutics such as small interfering RNAs (siRNAs). Influenza and Ebolaviruses are zoonotic RNA viruses mutating very rapidly, and their sequence changes must be characterized intensively to design therapeutic oligonucleotides with long utility. Focusing on a total of 182 experimentally validated siRNAs for influenza A, B and Ebolaviruses compiled by the siRNA database, we conducted time-series analyses of occurrences of siRNA targets in these viral genomes. Reflecting their high mutation rates, occurrences of target oligonucleotides evidently fluctuate in viral populations and often disappear. Time-series analysis of the one-base changed sequences derived from each original target identified the oligonucleotide that shows a compensatory increase and will potentially become the ‘awaiting-type oligonucleotide’; the combined use of this oligonucleotide with the original can provide therapeutics with long utility. This strategy is also useful for assigning diagnostic reverse transcription-PCR primers with long utility.

ASPsiRNA: A Resource of ASP-siRNAs Having Therapeutic Potential for Human Genetic Disorders and Algorithm for Prediction of Their Inhibitory Efficacy

Allele-specific siRNAs (ASP-siRNAs) have emerged as promising therapeutic molecules owing to their selectivity to inhibit the mutant allele or associated single-nucleotide polymorphisms (SNPs) sparing the expression of the wild-type counterpart. Thus, a dedicated bioinformatics platform encompassing updated ASP-siRNAs and an algorithm for the prediction of their inhibitory efficacy will be helpful in tackling currently intractable genetic disorders. In the present study, we have developed the ASPsiRNA resource (http://crdd.osdd.net/servers/aspsirna/) covering three components viz (i) ASPsiDb, (ii) ASPsiPred, and (iii) analysis tools like ASP-siOffTar. ASPsiDb is a manually curated database harboring 4543 (including 422 chemically modified) ASP-siRNAs targeting 78 unique genes involved in 51 different diseases. It furnishes comprehensive information from experimental studies on ASP-siRNAs along with multidimensional genetic and clinical information for numerous mutations. ASPsiPred is a two-layered algorithm to predict efficacy of ASP-siRNAs for fully complementary mutant (Eff^mut) and wild-type allele (Eff^wild) with one mismatch by ASPsiPred^SVM and ASPsiPred^matrix, respectively. In ASPsiPred^SVM, 922 unique ASP-siRNAs with experimentally validated quantitative Eff^mut were used. During 10-fold cross-validation (10nCV) employing various sequence features on the training/testing dataset (T737), the best predictive model achieved a maximum Pearson’s correlation coefficient (PCC) of 0.71. Further, the accuracy of the classifier to predict Eff^mut against novel genes was assessed by leave one target out cross-validation approach (LOTOCV). ASPsiPred^matrix was constructed from rule-based studies describing the effect of single siRNA:mRNA mismatches on the efficacy at 19 different locations of siRNA. Thus, ASPsiRNA encompasses the first database, prediction algorithm, and off-target analysis tool that is expected to accelerate research in the field of RNAi-based therapeutics for human genetic diseases.

AVCpred: an integrated web server for prediction and design of antiviral compounds

Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.

ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis

Current Zika virus (ZIKV) outbreaks that spread in several areas of Africa, Southeast Asia, and in pacific islands is declared as a global health emergency by World Health Organization (WHO). It causes Zika fever and illness ranging from severe autoimmune to neurological complications in humans. To facilitate research on this virus, we have developed an integrative multi-omics platform; ZikaVR (http://bioinfo.imtech.res.in/manojk/zikavr/), dedicated to the ZIKV genomic, proteomic and therapeutic knowledge. It comprises of whole genome sequences, their respective functional information regarding proteins, genes, and structural content. Additionally, it also delivers sophisticated analysis such as whole-genome alignments, conservation and variation, CpG islands, codon context, usage bias and phylogenetic inferences at whole genome and proteome level with user-friendly visual environment. Further, glycosylation sites and molecular diagnostic primers were also analyzed. Most importantly, we also proposed potential therapeutically imperative constituents namely vaccine epitopes, siRNAs, miRNAs, sgRNAs and repurposing drug candidates.

SMEpred workbench: A web server for predicting efficacy of chemicallymodified siRNAs

Chemical modifications have been extensively exploited to circumvent shortcomings in therapeutic applications of small interfering RNAs (siRNAs). However, experimental designing and testing of these siRNAs or chemically modified siRNAs (cm-siRNAs) involves enormous resources. Therefore, in-silico intervention in designing cm-siRNAs would be of utmost importance. We developed SMEpred workbench to predict the efficacy of normal siRNAs as well as cm-siRNAs using 3031 heterogeneous cm-siRNA sequences from siRNAmod database. These include 30 frequently used chemical modifications on different positions of either siRNA strand. Support Vector Machine (SVM) was employed to develop predictive models utilizing various sequence features namely mono-, di-nucleotide composition, binary pattern and their hybrids. We achieved highest Pearson Correlation Coefficient (PCC) of 0.80 during 10-fold cross validation and similar PCC value in independent validation. We have provided the algorithm in the 'SMEpred' pipeline to predict the normal siRNAs from the gene or mRNA sequence. For multiple modifications, we have assembled 'MultiModGen' module to design multiple modifications and further process them to evaluate their predicted efficacies. SMEpred webserver will be useful to scientific community engaged in use of RNAi-based technology as well as for therapeutic development. Web server is available for public use at following URL address: http://bioinfo.imtech.res.in/manojk/smepred .

ge-CRISPR - An integrated pipeline for the prediction and analysis of sgRNAs genome editing efficiency for CRISPR/Cas system

Genome editing by sgRNA a component of CRISPR/Cas system emerged as a preferred technology for genome editing in recent years. However, activity and stability of sgRNA in genome targeting is greatly influenced by its sequence features. In this endeavor, a few prediction tools have been developed to design effective sgRNAs but these methods have their own limitations. Therefore, we have developed "ge-CRISPR" using high throughput data for the prediction and analysis of sgRNAs genome editing efficiency. Predictive models were employed using SVM for developing pipeline-1 (classification) and pipeline-2 (regression) using 2090 and 4139 experimentally verified sgRNAs respectively from Homo sapiens, Mus musculus, Danio rerio and Xenopus tropicalis. During 10-fold cross validation we have achieved accuracy and Matthew's correlation coefficient of 87.70% and 0.75 for pipeline-1 on training dataset (T(1840)) while it performed equally well on independent dataset (V(250)). In pipeline-2 we attained Pearson correlation coefficient of 0.68 and 0.69 using best models on training (T(3169)) and independent dataset (V(520)) correspondingly. ge-CRISPR (http://bioinfo.imtech.res.in/manojk/gecrispr/) for a given genomic region will identify potent sgRNAs, their qualitative as well as quantitative efficiencies along with potential off-targets. It will be useful to scientific community engaged in CRISPR research and therapeutics development.

AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50)

Peptide-based antiviral therapeutics has gradually paved their way into mainstream drug discovery research. Experimental determination of peptides' antiviral activity as expressed by their IC50 values involves a lot of effort. Therefore, we have developed "AVP-IC50 Pred," a regression-based algorithm to predict the antiviral activity in terms of IC50 values (μM). A total of 759 non-redundant peptides from AVPdb and HIPdb were divided into a training/test set having 683 peptides (T(683)) and a validation set with 76 independent peptides (V(76)) for evaluation. We utilized important peptide sequence features like amino-acid compositions, binary profile of N8-C8 residues, physicochemical properties and their hybrids. Four different machine learning techniques (MLTs) namely Support vector machine, Random Forest, Instance-based classifier, and K-Star were employed. During 10-fold cross validation, we achieved maximum Pearson correlation coefficients (PCCs) of 0.66, 0.64, 0.56, 0.55, respectively, for the above MLTs using the best combination of feature sets. All the predictive models also performed well on the independent validation dataset and achieved maximum PCCs of 0.74, 0.68, 0.59, 0.57, respectively, on the best combination of feature sets. The AVP-IC50 Pred web server is anticipated to assist the researchers working on antiviral therapeutics by enabling them to computationally screen many compounds and focus experimental validation on the most promising set of peptides, thus reducing cost and time efforts. The server is available at http://crdd.osdd.net/servers/ic50avp.

Unraveling the web of viroinformatics: computational tools and databases in virus research

The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article.