Detection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites.

Identification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species.

Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) and NOD2 are cytosolic pattern recognition receptors playing pivotal roles in innate immune signaling. NOD1 and NOD2 recognize bacterial peptidoglycan derivatives iE-DAP and MDP, respectively and undergoes conformational alternation and ATP-dependent self-oligomerization of NACHT domain followed by downstream signaling. Lack of structural adequacy of NACHT domain confines our understanding about the NOD-mediated signaling mechanism. Here, we predicted the structure of NACHT domain of both NOD1 and NOD2 from model organism zebrafish (Danio rerio) using computational methods. Our study highlighted the differential ATP binding modes in NOD1 and NOD2. In NOD1, γ-phosphate of ATP faced toward the central nucleotide binding cavity like NLRC4, whereas in NOD2 the cavity was occupied by adenine moiety. The conserved 'Lysine' at Walker A formed hydrogen bonds (H-bonds) and Aspartic acid (Walker B) formed electrostatic interaction with ATP. At Sensor 1, Arg328 of NOD1 exhibited an H-bond with ATP, whereas corresponding Arg404 of NOD2 did not. 'Proline' of GxP motif (Pro386 of NOD1 and Pro464 of NOD2) interacted with adenine moiety and His511 at Sensor 2 of NOD1 interacted with γ-phosphate group of ATP. In contrast, His579 of NOD2 interacted with the adenine moiety having a relatively inverted orientation. Our findings are well supplemented with the molecular interaction of ATP with NLRC4, and consistent with mutagenesis data reported for human, which indicates evolutionary shared NOD signaling mechanism. Together, this study provides novel insights into ATP binding mechanism, and highlights the differential ATP binding modes in zebrafish NOD1 and NOD2.

Genome wide association study (GWAS) was conducted for 14 agronomic traits in wheat following widely used single locus single trait (SLST) approach, and two recent approaches viz. multi locus mixed model (MLMM), and multi-trait mixed model (MTMM). Association panel consisted of 230 diverse Indian bread wheat cultivars (released during 1910-2006 for commercial cultivation in different agro-climatic regions in India). Three years phenotypic data for 14 traits and genotyping data for 250 SSR markers (distributed across all the 21 wheat chromosomes) was utilized for GWAS. Using SLST, as many as 213 MTAs (p ≤ 0.05, 129 SSRs) were identified for 14 traits, however, only 10 MTAs (~9%; 10 out of 123 MTAs) qualified FDR criteria; these MTAs did not show any linkage drag. Interestingly, these genomic regions were coincident with the genomic regions that were already known to harbor QTLs for same or related agronomic traits. Using MLMM and MTMM, many more QTLs and markers were identified; 22 MTAs (19 QTLs, 21 markers) using MLMM, and 58 MTAs (29 QTLs, 40 markers) using MTMM were identified. In addition, 63 epistatic QTLs were also identified for 13 of the 14 traits, flag leaf length (FLL) being the only exception. Clearly, the power of association mapping improved due to MLMM and MTMM analyses. The epistatic interactions detected during the present study also provided better insight into genetic architecture of the 14 traits that were examined during the present study. Following eight wheat genotypes carried desirable alleles of QTLs for one or more traits, WH542, NI345, NI170, Sharbati Sonora, A90, HW1085, HYB11, and DWR39 (Pragati). These genotypes and the markers associated with important QTLs for major traits can be used in wheat improvement programs either using marker-assisted recurrent selection (MARS) or pseudo-backcrossing method.

Herbicide resistance (HR) is a major concern for the agricultural producers as well as environmentalists. Resistance to commonly used herbicides are conferred due to mutation(s) in the genes encoding herbicide target sites/proteins (GETS). Identification of these genes through wet-lab experiments is time consuming and expensive. Thus, a supervised learning-based computational model has been proposed in this study, which is first of its kind for the prediction of seven classes of GETS. The cDNA sequences of the genes were initially transformed into numeric features based on the k-mer compositions and then supplied as input to the support vector machine. In the proposed SVM-based model, the prediction occurs in two stages, where a binary classifier in the first stage discriminates the genes involved in conferring the resistance to herbicides from other genes, followed by a multi-class classifier in the second stage that categorizes the predicted herbicide resistant genes in the first stage into any one of the seven resistant classes. Overall classification accuracies were observed to be ~89% and >97% for binary and multi-class classifications respectively. The proposed model confirmed higher accuracy than the homology-based algorithms viz., BLAST and Hidden Markov Model. Besides, the developed computational model achieved ~87% accuracy, while tested with an independent dataset. An online prediction server HRGPred ( http://cabgrid.res.in:8080/hrgpred ) has also been established to facilitate the prediction of GETS by the scientific community.

Multi-drug resistant in Mycobacterium tuberculosis (M.tb) is considered as major bottleneck in the treatment and cure of tuberculosis (TB). Several anti-tubercular drugs fail in its efficacy due to drug-resistant M.tb developed mechanism for resistance. So, research across globe has been carried out to develop effective anti-TB drugs to improve the treatment of these strains. Traditional drug development methods have been proved unsuccessful as it fails to develop a broad-spectrum drug due to lack of structure based approach. Several studies have been conducted in this regard and identified several drug target sites that influence drug-resistant M.tb strains. In this study, the attempt was to study the interaction between the protein Arabinosyltransferase C with the two existing drugs (Ethambutol and Isoniazid) and five modified molecules derived from Ethambutol by calculating their binding affinity and mode of binding through molecular docking study using AutoDock 4. From the comparison study of the existing drug (EMB and INH) and the five proposed modified molecules (Emb1, Emb2, Emb3, Emb4 and Emb5), it is analysed that Emb1 and Emb3 with binding affinities -5.77 kcal/mol and -5.13 kcal/mol respectively can be considered as potential inhibitors of Arabinosyltransferase C in Mycobacterium tuberculosis which is responsible for cell wall synthesis. The facts provided may be further verified experimentally for future drug discovery process to make a stand against tuberculosis and contribute an advance research for worthy antimycobacterial strategies.

Foot-and-mouth disease (FMD) endangers a large number of livestock populations across the globe being a highly contagious viral infection in wild and domestic cloven-hoofed animals. It adversely affects the socioeconomic status of millions of households. Vaccination has been used to protect animals against FMD virus (FMDV) to some extent but the effectiveness of available vaccines has been decreased due to high genetic variability in the FMDV genome. Another key aspect that the current vaccines are not favored is they do not provide the ability to differentiate between infected and vaccinated animals. Thus, RNA interference (RNAi) being a potential strategy to control virus replication, has opened up a new avenue for controlling the viral transmission. Hence, an attempt has been made here to establish the role of RNAi in therapeutic developments for FMD by computationally identifying (i) microRNA (miRNA) targets in FMDV using target prediction algorithms, (ii) targetable genomic regions in FMDV based on their dissimilarity with the host genome and, (iii) plausible anti-FMDV miRNA-like simulated nucleotide sequences (SNSs). The results revealed 12 mature host miRNAs that have 284 targets in 98 distinct FMDV genomic sequences. Wet-lab validation for anti-FMDV properties of 8 host miRNAs was carried out and all were observed to confer variable magnitude of antiviral effect. In addition, 14 miRBase miRNAs were found with better target accessibility in FMDV than that of Bos taurus. Further, 8 putative targetable regions having sense strand properties of siRNAs were identified on FMDV genes that are highly dissimilar with the host genome. A total of 16 SNSs having > 90% identity with mature miRNAs were also identified that have targets in FMDV genes. The information generated from this study is populated at http://bioinformatics.iasri.res.in/fmdisc/ to cater the needs of biologists, veterinarians and animal scientists working on FMD.

Finger millet (Eleusine coracana L.) is an important dry-land cereal in Asia and Africa because of its ability to provide assured harvest under extreme dry conditions and excellent nutritional properties. However, the genetic improvement of the crop is lacking in the absence of suitable genomic resources for reliable genotype-phenotype associations. Keeping this in view, a diverse global finger millet germplasm collection of 113 accessions was evaluated for 14 agro-morphological characters in two environments viz. ICAR-Vivekananda Institute of Hill Agriculture, Almora (E1) and Crop Research Centre (CRC), GBPUA&T, Pantnagar (E2), India. Principal component analysis and cluster analysis of phenotypic data separated the Indian and exotic accessions into two separate groups. Previously generated SNPs through genotyping by sequencing (GBS) were used for association mapping to identify reliable marker(s) linked to grain yield and its component traits. The marker trait associations were determined using single locus single trait (SLST), multi-locus mixed model (MLMM) and multi-trait mixed model (MTMM) approaches. SLST led to the identification of 20 marker-trait associations (MTAs) (p value<0.01 and <0.001) for 5 traits. While advanced models, MLMM and MTMM resulted in additional 36 and 53 MTAs, respectively. Nine MTAs were common out of total 109 associations in all the three mapping approaches (SLST, MLMM and MTMM). Among these nine SNPs, five SNP sequences showed homology to candidate genes of Oryza sativa (Rice) and Setaria italica (Foxtail millet), which play an important role in flowering, maturity and grain yield. In addition, 67 and 14 epistatic interactions were identified for 10 and 7 traits at E1 and E2 locations, respectively. Hence, the 109 novel SNPs associated with important agro-morphological traits, reported for the first time in this study could be precisely utilized in finger millet genetic improvement after validation.

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135) from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum), allicin (A. sativum), cinnamaldehyde (Cinnamomum cassia), curcumin (Curcuma longa), gallotannin (active component of green tea and red wine), isoorientin (Anthopterus wardii), isovitexin (A. wardii), neral (Melissa officinalis), and vitexin (A. wardii) have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments.

Identification of splice sites is essential for annotation of genes. Though existing approaches have achieved an acceptable level of accuracy, still there is a need for further improvement. Besides, most of the approaches are species-specific and hence it is required to develop approaches compatible across species.

Antimicrobial peptides (AMPs) are important components of the innate immune system that have been found to be effective against disease causing pathogens. Identification of AMPs through wet-lab experiment is expensive. Therefore, development of efficient computational tool is essential to identify the best candidate AMP prior to the in vitro experimentation. In this study, we made an attempt to develop a support vector machine (SVM) based computational approach for prediction of AMPs with improved accuracy. Initially, compositional, physico-chemical and structural features of the peptides were generated that were subsequently used as input in SVM for prediction of AMPs. The proposed approach achieved higher accuracy than several existing approaches, while compared using benchmark dataset. Based on the proposed approach, an online prediction server iAMPpred has also been developed to help the scientific community in predicting AMPs, which is freely accessible at http://cabgrid.res.in:8080/amppred/. The proposed approach is believed to supplement the tools and techniques that have been developed in the past for prediction of AMPs.

Insecticide resistance is a major challenge for the control program of insect pests in the fields of crop protection, human and animal health etc. Resistance to different insecticides is conferred by the proteins encoded from certain class of genes of the insects. To distinguish the insecticide resistant proteins from non-resistant proteins, no computational tool is available till date. Thus, development of such a computational tool will be helpful in predicting the insecticide resistant proteins, which can be targeted for developing appropriate insecticides.

The nucleotide-binding and oligomerization domain (NOD)-containing protein 1 (NOD1) plays the pivotal role in host-pathogen interface of innate immunity and triggers immune signalling pathways for the maturation and release of pro-inflammatory cytokines. Upon the recognition of iE-DAP, NOD1 self-oligomerizes in an ATP-dependent fashion and interacts with adaptor molecule receptor-interacting protein 2 (RIP2) for the propagation of innate immune signalling and initiation of pro-inflammatory immune responses. This interaction (mediated by NOD1 and RIP2) helps in transmitting the downstream signals for the activation of NF-κB signalling pathway, and has been arbitrated by respective caspase-recruitment domains (CARDs). The so-called CARD-CARD interaction still remained contradictory due to inconsistent results. Henceforth, to understand the mode and the nature of the interaction, structural bioinformatics approaches were employed. MD simulation of modelled 1:1 heterodimeric complexes revealed that the type-Ia interface of NOD1CARD and the type-Ib interface of RIP2CARD might be the suitable interfaces for the said interaction. Moreover, we perceived three dynamically stable heterotrimeric complexes with an NOD1:RIP2 ratio of 1:2 (two numbers) and 2:1. Out of which, in the first trimeric complex, a type-I NOD1-RIP2 heterodimer was found interacting with an RIP2CARD using their type-IIa and IIIa interfaces. However, in the second and third heterotrimer, we observed type-I homodimers of NOD1 and RIP2 CARDs were interacting individually with RIP2CARD and NOD1CARD (in type-II and type-III interface), respectively. Overall, this study provides structural and dynamic insights into the NOD1-RIP2 oligomer formation, which will be crucial in understanding the molecular basis of NOD1-mediated CARD-CARD interaction in higher and lower eukaryotes.

Circadian rhythms regulate several physiological and developmental processes of plants. Hence, the identification of genes with the underlying circadian rhythmic features is pivotal. Though computational methods have been developed for the identification of circadian genes, all these methods are based on gene expression datasets. In other words, we failed to search any sequence-based model, and that motivated us to deploy the present computational method to identify the proteins encoded by the circadian genes.

Solid tumor is generally observed in tissues of epithelial or endothelial cells of lung, breast, prostate, pancreases, colorectal, stomach, and bladder, where several genes transcription is regulated by the microRNAs (miRNAs). Argonaute (AGO) protein is a family of protein which assists in miRNAs to bind with mRNAs of the target genes. Hence, study of the binding mechanism between AGO protein and miRNAs, and also with miRNAs-mRNAs duplex is crucial for understanding the RNA silencing mechanism. In the current work, 64 genes and 23 miRNAs have been selected from literatures, whose deregulation is well established in seven types of solid cancer like lung, breast, prostate, pancreases, colorectal, stomach, and bladder cancer. In silico study reveals, miRNAs namely, miR-106a, miR-21, and miR-29b-2 have a strong binding affinity towards PTEN, TGFBR2, and VEGFA genes, respectively, suggested as important factors in RNA silencing mechanism. Furthermore, interaction between AGO protein (PDB ID-3F73, chain A) with selected miRNAs and with miRNAs-mRNAs duplex were studied computationally to understand their binding at molecular level. The residual interaction and hydrogen bonding are inspected in Discovery Studio 3.5 suites. The current investigation throws light on understanding miRNAs based gene silencing mechanism in solid cancer.

Identification of splice sites is imperative for prediction of gene structure. Machine learning-based approaches (MLAs) have been reported to be more successful than the rule-based methods for identification of splice sites. However, the strings of alphabets should be transformed into numeric features through sequence encoding before using them as input in MLAs. In this study, we evaluated the performances of 8 different sequence encoding schemes i.e., Bayes kernel, density and sparse (DS), distribution of tri-nucleotide and 1st order Markov model (DM), frequency difference distance measure (FDDM), paired-nucleotide frequency difference between true and false sites (FDTF), 1st order Markov model (MM1), combination of both 1st and 2nd order Markov model (MM1 + MM2) and 2nd order Markov model (MM2) in respect of predicting donor and acceptor splice sites using 5 supervised learning methods (ANN, Bagging, Boosting, RF and SVM). The encoding schemes and machine learning methods were first evaluated in 4 species i.e., A. thaliana, C. elegans, D. melanogaster and H. sapiens, and then performances were validated with another four species i.e., Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum and Trypanosoma brucei. In terms of ROC (receiver-operating-characteristics) and PR (precision-recall) curves, FDTF encoding approach achieved higher accuracy followed by either MM2 or FDDM. Further, SVM was found to achieve higher accuracy (in terms of ROC and PR curves) followed by RF across encoding schemes and species. In terms of prediction accuracy across species, the SVM-FDTF combination was optimum than other combinations of classifiers and encoding schemes. Further, splice site prediction accuracies were observed higher for the species with low intron density. To our limited knowledge, this is the first attempt as far as comprehensive evaluation of sequence encoding schemes for prediction of splice sites is concerned. We have also developed an R-package EncDNA (https://cran.r-project.org/web/packages/EncDNA/index.html) for encoding of splice site motifs with different encoding schemes, which is expected to supplement the existing nucleotide sequence encoding approaches. This study is believed to be useful for the computational biologists for predicting different functional elements on the genomic DNA.

Localization of messenger RNAs (mRNAs) plays a crucial role in the growth and development of cells. Particularly, it plays a major role in regulating spatio-temporal gene expression. The in situ hybridization is a promising experimental technique used to determine the localization of mRNAs but it is costly and laborious. It is also a known fact that a single mRNA can be present in more than one location, whereas the existing computational tools are capable of predicting only a single location for such mRNAs. Thus, the development of high-end computational tool is required for reliable and timely prediction of multiple subcellular locations of mRNAs. Hence, we develop the present computational model to predict the multiple localizations of mRNAs.

MicroRNAs (miRNAs) are one kind of non-coding RNA, play vital role in regulating several physiological and developmental processes. Subcellular localization of miRNAs and their abundance in the native cell are central for maintaining physiological homeostasis. Besides, RNA silencing activity of miRNAs is also influenced by their localization and stability. Thus, development of computational method for subcellular localization prediction of miRNAs is desired. In this work, we have proposed a computational method for predicting subcellular localizations of miRNAs based on principal component scores of thermodynamic, structural properties and pseudo compositions of di-nucleotides. Prediction accuracy was analyzed following fivefold cross validation, where ~ 63-71% of AUC-ROC and ~ 69-76% of AUC-PR were observed. While evaluated with independent test set, > 50% localizations were found to be correctly predicted. Besides, the developed computational model achieved higher accuracy than the existing methods. A user-friendly prediction server "miRNALoc" is freely accessible at https://cabgrid.res.in:8080/mirnaloc/ , by which the user can predict localizations of miRNAs.