Bharat Panwar

lncRNome: a comprehensive knowledgebase of human long noncoding RNAs

The advent of high-throughput genome scale technologies has enabled us to unravel a large amount of the previously unknown transcriptionally active regions of the genome. Recent genome-wide studies have provided annotations of a large repertoire of various classes of noncoding transcripts. Long noncoding RNAs (lncRNAs) form a major proportion of these novel annotated noncoding transcripts, and presently known to be involved in a number of functionally distinct biological processes. Over 18 000 transcripts are presently annotated as lncRNA, and encompass previously annotated classes of noncoding transcripts including large intergenic noncoding RNA, antisense RNA and processed pseudogenes. There is a significant gap in the resources providing a stable annotation, cross-referencing and biologically relevant information. lncRNome has been envisioned with the aim of filling this gap by integrating annotations on a wide variety of biologically significant information into a comprehensive knowledgebase. To the best of our knowledge, lncRNome is one of the largest and most comprehensive resources for lncRNAs. Database URL: http://genome.igib.res.in/lncRNome

Prediction and classification of ncRNAs using structural information

BackgroundEvidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts.ResultsIn this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models.ConclusionsThis work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon (http://crdd.osdd.net/raghava/rnacon).

MiRmine: A database of human miRNA expression profiles

Motivation: MicroRNAs (miRNAs) are small non‐coding RNAs that are involved in post‐transcriptional regulation of gene expression. In this high‐throughput sequencing era, a tremendous amount of RNA‐seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. Results: We used 304 high‐quality microRNA sequencing (miRNA‐seq) datasets from NCBI‐SRA and calculated expression profiles for different tissues and cell‐lines. In each miRNA‐seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell‐line. This user‐friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell‐line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. Availability and Implementation: http://guanlab.ccmb.med.umich.edu/mirmine Contact: bharatpa@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

BIAdb: A curated database of benzylisoquinoline alkaloids

BackgroundBenzylisoquinoline is the structural backbone of many alkaloids with a wide variety of structures including papaverine, noscapine, codeine, morphine, apomorphine, berberine, protopine and tubocurarine. Many benzylisoquinoline alkaloids have been reported to show therapeutic properties and to act as novel medicines. Thus it is important to collect and compile benzylisoquinoline alkaloids in order to explore their usage in medicine.DescriptionWe extract information about benzylisoquinoline alkaloids from various sources like PubChem, KEGG, KNApSAcK and manual curation from literature. This information was processed and compiled in order to create a comprehensive database of benzylisoquinoline alkaloids, called BIAdb. The current version of BIAdb contains information about 846 unique benzylisoquinoline alkaloids, with multiple entries in term of source, function leads to total number of 2504 records. One of the major features of this database is that it provides data about 627 different plant species as a source of benzylisoquinoline and 114 different types of function performed by these compounds. A large number of online tools have been integrated, which facilitate user in exploring full potential of BIAdb. In order to provide additional information, we give external links to other resources/databases. One of the important features of this database is that it is tightly integrated with Drugpedia, which allows managing data in fixed/flexible format.ConclusionsA database of benzylisoquinoline compounds has been created, which provides comprehensive information about benzylisoquinoline alkaloids. This database will be very useful for those who are working in the field of drug discovery based on natural products. This database will also serve researchers working in the field of synthetic biology, as developing medicinally important alkaloids using synthetic process are one of important challenges. This database is available from http://crdd.osdd.net/raghava/biadb/.

Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains.

BackgroundAminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis.ResultsIn this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique.ConclusionsWe have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models using selected dipeptide composition demonstrates that certain types of dipeptide are preferred in aaRSs. We were able to identify PROSITE domains that are preferred in aaRSs and their classes, providing interesting insights into tRNA synthetases. The method developed in this study will be useful for researchers studying aaRS enzymes and tRNA biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/icaars/.

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information

BackgroundThe vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure.ResultsIn this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets.ConclusionsThis study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

Computational Inferences of the Functions of Alternative/Noncanonical Splice Isoforms Specific to HER2+/ER–/PR– Breast Cancers, a Chromosome 17 C-HPP Study

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The main objective is to identify and evaluate functionality of a set of specific noncanonical isoforms expressed in HER2-neu positive, estrogen receptor negative (ER-), and progesterone receptor negative (PR-) breast cancers (HER2+/ER-/PR- BC), an aggressive subtype of breast cancers that cause significant morbidity and mortality. We identified 11 alternative splice isoforms that were differentially expressed in HER2+/ER-/PR- BC compared to normal mammary, triple negative breast cancer and triple positive breast cancer tissues (HER2+/ER+/PR+). We used a stringent criterion that differentially expressed noncanonical isoforms (adjusted p value < 0.05) and have to be expressed in all replicates of HER2+/ER-/PR- BC samples, and the trend in differential expression (up or down) is the same in all comparisons. Of the 11 protein isoforms, six were overexpressed in HER2+/ER-/PR- BC. We explored possible functional roles of these six proteins using several complementary computational tools. Biological processes including cell cycle events and glycolysis were linked to four of these proteins. For example, glycolysis was the top ranking functional process for DMXL2 isoform 3, with a fold change of 27 compared to just two for the canonical protein. No previous reports link DMXL2 with any metabolic processes; the canonical protein is known to participate in signaling pathways. Our results clearly indicate distinct functions for the six overexpressed alternative splice isoforms, and these functions could be specific to HER2+/ER-/PR- tumor progression. Further detailed analysis is warranted as these proteins could be explored as potential biomarkers and therapeutic targets for HER2+/ER-/PR- BC patients.

Prediction of uridine modifications in tRNA sequences

BackgroundIn past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived.ResultsA three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987.ConclusionsThe hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd.osdd.net/raghava/trnamod/) has been developed, which predicts UMs from both tRNA sequences and whole genome.

Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthews correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/).

Predicting sub-cellular localization of tRNA synthetases from their primary structures

Since endo-symbiotic events occur, all genes of mitochondrial aminoacyl tRNA synthetase (AARS) were lost or transferred from ancestral mitochondrial genome into the nucleus. The canonical pattern is that both cytosolic and mitochondrial AARSs coexist in the nuclear genome. In the present scenario all mitochondrial AARSs are nucleus-encoded, synthesized on cytosolic ribosomes and post-translationally imported from the cytosol into the mitochondria in eukaryotic cell. The site-based discrimination between similar types of enzymes is very challenging because they have almost same physico-chemical properties. It is very important to predict the sub-cellular location of AARSs, to understand the mitochondrial protein synthesis. We have analyzed and optimized the distinguishable patterns between cytosolic and mitochondrial AARSs. Firstly, support vector machines (SVM)-based modules have been developed using amino acid and dipeptide compositions and achieved Mathews correlation coefficient (MCC) of 0.82 and 0.73, respectively. Secondly, we have developed SVM modules using position-specific scoring matrix and achieved the maximum MCC of 0.78. Thirdly, we developed SVM modules using N-terminal, intermediate residues, C-terminal and split amino acid composition (SAAC) and achieved MCC of 0.82, 0.70, 0.39 and 0.86, respectively. Finally, a SVM module was developed using selected attributes of split amino acid composition (SA-SAAC) approach and achieved MCC of 0.92 with an accuracy of 96.00%. All modules were trained and tested on a non-redundant data set and evaluated using fivefold cross-validation technique. On the independent data sets, SA-SAAC based prediction model achieved MCC of 0.95 with an accuracy of 97.77%. The web-server ‘MARSpred’ based on above study is available at http://www.imtech.res.in/raghava/marspred/.