Tiratha Singh

Jaypee University Of Information Technology (Juit.Ac.In), Biotechnology and Bioinformatics, Faculty Member

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Address: Bhopal, MP, India and Shimla, HP, India

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Papers by Tiratha Singh

Pathway and Differential Gene Expression Studies for Colorectal Cancer

World Academy of Science, Engineering and Technology, International Journal of Bioengineering and Life Sciences, Feb 16, 2017

Computational Studies to Investigate Human DNA Repair Associated Malignancies with Special Relevance to Colorectal Cancer

Shodhganga, 2018

AlzGenPred: A CatBoost based method using network features to classify the Alzheimers Disease associated genes from the high throughput sequencing data

Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory ... more Background and Objective: AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing technologies, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic because all these algorithms are based on statistical techniques. Therefore, AlzGenPred is developed to identify the AD-associated genes from a large set of data. Methods: To develop the AlzGenPred, we have compiled a benchmark dataset consisting of 1086 AD and non-AD genes and used them as positive and negative datasets. We have generated several features including the fused features and evaluated them through machine learning methods. Then hyperparameter tuning approach was also applied and the final model was selected. The proposed method was validated by using the AlzGene and transcriptomics datasets and proposed as a standalone tool. Results: T...

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PMTPred: Machine Learning Based Prediction of Protein Methyltransferases using the Composition of k-spaced Amino Acid Pairs

Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a ... more Protein methyltransferases (PMTs) are a group of enzymes that help to catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and are able to methylate various substrates with DNA, RNA, protein, and smallmolecule secondary metabolites. Dysregulation of methyltransferases is involved in different types of human cancers. However, in light of the well-recognized significance of PMTs, it becomes crucial to have reliable and fast methods for identifying these proteins. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated and prediction models were develped using different machine-learning methods. A ten-fold cross-validation technique was used for model training. The SVM-based CKSAAP model gave the best prediction and achieved the highest accuracy of 87.94% with balance sensitivity (88.8%) and specificity (87.11%) with MCC of 0.759 an...

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Editorial Foreword

International journal for computational biology, Apr 1, 2015

Repurposing of drugs against methyltransferase as potential Zika virus therapies

Scientific Reports, May 15, 2023

In recent years, the outbreak of infectious disease caused by Zika Virus (ZIKV) has posed a major... more In recent years, the outbreak of infectious disease caused by Zika Virus (ZIKV) has posed a major threat to global public health, calling for the development of therapeutics to treat ZIKV disease. Several possible druggable targets involved in virus replication have been identified. In search of additional potential inhibitors, we screened 2895 FDA-approved compounds using Non-Structural Protein 5 (NS5) as a target utilizing virtual screening of in-silco methods. The top 28 compounds with the threshold of binding energy −7.2 kcal/mol value were selected and were cross-docked on the three-dimensional structure of NS5 using AutoDock Tools. Of the 2895 compounds screened, five compounds (Ceforanide, Squanavir, Amcinonide, Cefpiramide, and Olmesartan_Medoxomil) ranked highest based on filtering of having the least negative interactions with the NS5 and were selected for Molecular Dynamic Simulations (MDS) studies. Various parameters such as RMSD, RMSF, Rg, SASA, PCA and binding free energy were calculated to validate the binding of compounds to the target, ZIKV-NS5. The binding free energy was found to be −114.53, −182.01, −168.19, −91.16, −122.56, and −150.65 kJ mol −1 for NS5-SFG, NS5-Ceforanide, NS5-Squanavir, NS5-Amcinonide, NS5-Cefpiramide, and NS5-Ol_Me complexes respectively. The binding energy calculations suggested Cefpiramide and Olmesartan_Medoxomil (Ol_Me) as the most stable compounds for binding to NS5, indicating a strong rationale for their use as lead compounds for development of ZIKV inhibitors. As these drugs have been evaluated on pharmacokinetics and pharmacodynamics parameters only, in vitro and in vivo testing and their impact on Zika viral cell culture may suggest their clinical trials on ZIKV patients. RNA viruses are molecular pathogens that cause diseases at a rapid pace, making them the fastest-growing type of pathogen. These viral pathogens show significant genetic variability and opt for distinct replication strategies to replicate within cells 1. ZIKV is an arthropod-borne member of the Flaviviridae family that spreads through the bite of an infected Aedes mosquito species such as Aedes africanus, A. luteocephalus, A. aegypti, and A. albopictus. It was first discovered in parts of Africa in 1947, and its outbreak was seen in other geographic areas such as the Far East, Europe, North America, the Caribbean, and Central/South America 2. ZIKV causes mild symptoms similar to those of other flavivirus-borne diseases, such as fever, muscular discomfort, headache, joint pain, exhaustion, and conjunctivitis. However, the 2015 outbreak in Egypt, the Pacific Islands, and Brazil drew the attention of the scientific community because of the high infection rate and its association with life-threatening diseases such as Neonatal Microcephaly (NM) and adult Guillain-Barre syndrome (GBS) 3 .

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Application of Deep Learning in Biological Big Data Analysis

IGI Global eBooks, 2021

The meaningful data extraction from the biological big data or omics data is a remaining challeng... more The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.

Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer’s disease

Journal of Biomolecular Structure & Dynamics, Feb 21, 2019

Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized b... more Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and characterized by brain cell death, memory loss and is the most common form of dementia. Although AD has devastating effects, however, drugs which can treat the AD remain limited. The cyclin-dependent kinase 5 (CDK5) has been recognized as being involved in the pathological hyperphosphorylation of tau protein, which leads to the formation of neurofibrillary tangles (NFTs). We utilized the structure-based virtual screening (SBVS) approach to find the potential inhibitors against HsCDK5. The natural compound subset from the ZINC database (n = 167,741) was retrieved and screened by using SBVS method. From here, we have predicted 297 potent inhibitors. These 297 compounds were evaluated through their pharmacokinetic properties by ADMET (absorption, distribution, metabolism, elimination/excretion and toxicity) descriptors. Finally, 17 compounds were selected and used for re-docking. After the refinement by molecular docking and by using drug-likeness analysis, we have identified four potential inhibitors (ZINC85877721, ZINC96114862, ZINC96115616 and ZINC96116231). All these four ligands were employed for 100 ns MDS study. From the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Rg, number of hydrogen bonds, solvent accessible surface area (SASA), principal component analysis (PCA) and binding free energy analysis we have found that out of four inhibitors ZINC85877721 and ZINC96116231 showed good binding free energy of −198.84 and −159.32 kJ.mol−1, respectively, and also good in other structural analyses. Both compounds displayed excellent pharmacological and structural properties to be the drug candidates. Collectively, these findings recommend that two compounds have great potential to be a promising agent against AD to reduce the CDK5 induced hyperphosphorylation and could be considered as therapeutic agents for the AD. Communicated by Ramaswamy H. Sarma

Curative anti-typhoid effect of Detarium microcarpum Guill. & Perr. (Leguminosae) hydroethanolic extract root bark based-on in vivo and molecular docking analyses

Journal of Ethnopharmacology, May 1, 2023

Comprehensive Analysis Of Differentially Expressed mRNAs and miRNAs In Breast Cancer

Diabetic peripheral neuropathy is a common complication associated with diabetes mellitus with a ... more Diabetic peripheral neuropathy is a common complication associated with diabetes mellitus with a pathogenesis that is incompletely understood. By regulating RNA silencing and posttranscriptional gene expression, microRNAs participate in various biological processes and human diseases. However, the relationship between microRNAs and the progress of diabetic peripheral neuropathy still lacks a thorough exploration. Here we used microarray microRNA and mRNA expression profiling to analyze the microRNAs and mRNAs which are aberrantly expressed in dorsal root ganglia from streptozotocin-induced diabetic rats. We found that 37 microRNAs and 1357 mRNAs were differentially expressed in comparison to non-diabetic samples. Bioinformatics analysis indicated that 399 gene ontology terms and 29 Kyoto Encyclopedia of Genes and Genomes pathways were significantly enriched in diabetic rats. Additionally, a microRNA-gene network evaluation identified rno-miR-330-5p, rno-miR-17-1-3p and rno-miR-346 as important players for network regulation. Finally, quantitative real-time polymerase chain reaction analysis was used to confirm the microarray results. In conclusion, this study provides a systematic perspective of microRNA and mRNA expression in dorsal root ganglia from diabetic rats, and suggests that dysregulated microRNAs and mRNAs may be important promotors of peripheral neuropathy. Our results may be the underlying framework of future studies regarding the effect of the aberrantly expressed genes on the pathophysiology of diabetic peripheral neuropathy.

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Analysis of Mass Spectrometry Data: Significance Analysis of Microarrays for Seldi-MS Data in Proteomics

International Journal for Computational Biology, 2015

Mass Spectrometry (MS) has arguably become thecore technology in proteomics. MALDI and SELDI-TOFt... more Mass Spectrometry (MS) has arguably become thecore technology in proteomics. MALDI and SELDI-TOFtechniques enable the study biological fluids, e.g. human blood.Analysis of these samples can lead to discover new biomarkerswhich can ease the diagnostic and prognostic of several diseases,e.g. various cancers. In this work, we focus on MS data fromSELDI-TOF experiments. We begin with a preprocessing step inorder to remove noises due to the acquisition process of the data.Then, we apply the differential analysis to a SELDI-MS data,using the Significance Analysis of Microarray (SAM) methodimplemented in Matlab. Results using the SAM method arecompared with those obtained by the conventional t-test andAnalysis Of Variance (ANOVA) in order to evaluate its efficacyand its performance. As a result, we demonstrate that the SAMmethod can be adapted for effective significance analysis ofSELDI-MS data. It is deemed powerful and provides betterresults that totes. An easy-to-use application is developed withMatlab for mass spectrometry data analysis from raw spectra todifferential analysis, including the SAM method.

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Computational approach for assessing the involvement of SMYD2 protein in human cancers using TCGA data

Journal of Genetic Engineering and Biotechnology

Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate... more Background SMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate the lysine residue of various histone and nonhistone cancer-related proteins and plays a critical role in tumorigenesis. Although emerging evidence supports the association of SMYD2 in the progression of cancers, but its definitive effect is not yet clear. Therefore, further study of the gene in relation with cancer progression needs to be conducted. In the current study, investigators used TCGA data to determine the potential carcinogenic effect of SMYD2 in 11 cancer types. The transcriptional expression, survival rate, mutations, enriched pathways, and Gene Ontology of the SMYD2 were explored using different bioinformatics tools and servers. In addition, we also examined the correlation between SMYD2 gene expression and immunocyte infiltration in multiple cancer types. Results Findings revealed that higher expression of SMYD2 was significantly correlated with cancer incidents. In CESC...

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Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene

Journal of Biomolecular Structure & Dynamics, Jun 29, 2023

Erratum to: Mining NGS transcriptomes for miRNAs and dissecting their role in regulating growth, development, and secondary metabolites production in different organs of a medicinal herb, Picrorhiza kurroa

Planta, Feb 24, 2015

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Tau Pathology

Advances in medical diagnosis, treatment, and care (AMDTC) book series, 2019

A number of neurodegenerative disorders (NDs) are usually referred as tauopathies and characteriz... more A number of neurodegenerative disorders (NDs) are usually referred as tauopathies and characterized by the disappearance or disintegration of tau protein from microtubules. Alzheimer's disease (AD), Pick's disease (PiD), Parkinson's disease (PD) are directly or indirectly associated with tauopathy. Tau is a protein which is usually associated with microtubule. Microtubules are the backbone of neurons, and tau provides a support to microtubule stability. Hyperphosphorylation of tau leads to its separation from microtubule, consequently forming neurofibrillary tangles and resulting in a condition of dementia. Therapeutic implication on tauopathy is symptomatic as there is no exact regulation mechanism known till date. This chapter helps in the comprehensive study of biomarkers and pathways involved in tauopathy to decipher the complexity of the system, resulting in candidate drug target for the management of NDs.

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Identification of small molecules against the NMDAR: an insight from virtual screening, density functional theory, free energy landscape and molecular dynamics simulation-based findings

Network modeling analysis in health informatics and bioinformatics, Aug 23, 2022

An integrative approach to develop computational pipeline for drug-target interaction network analysis

Scientific Reports, Jul 6, 2018

Understanding the general principles governing the functioning of biological networks is a major ... more Understanding the general principles governing the functioning of biological networks is a major challenge of the current era. Functionality of biological networks can be observed from drug and target interaction perspective. All possible modes of operations of biological networks are confined by the interaction analysis. Several of the existing approaches in this direction, however, are datadriven and thus lack potential to be generalized and extrapolated to different species. In this paper, we demonstrate a systems pharmacology pipeline and discuss how the network theory, along with gene ontology (GO) analysis, co-expression analysis, module reconstruction , pathway mapping and structure level analysis can be used to decipher important properties of biological networks with the aim to propose lead molecule for the therapeutic interventions of various diseases.

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Computational analysis for functional and evolutionary aspects of BACE-1 and associated Alzheimer's related proteins

International Journal of Computational Intelligence Studies, 2012

Alzheimer's disease (AD) is an irremediable, heterogeneous, progressive brain disease that slowly... more Alzheimer's disease (AD) is an irremediable, heterogeneous, progressive brain disease that slowly destroys memory and judgment skills. We studied the protein-protein interactions of BACE-1 (β-secretase-1) and select some important proteins for further annotation. MSA for homologs to BACE-1 has been generated to identify functional motifs. To elucidate the structural, functional and evolutionary conservation of these motifs, we performed analysis through ConSeq and ConSurf servers. We found some specific information and clues of interest based on combinational phylogenetic and pattern finding analysis, which can be further targeted for the design of new inhibitors and will help in the inhibition of β-secretase. Motifs include the active site of the molecule and are found functionally and evolutionarily conserved. This study will also help in elucidating regulation mechanism of proteins found from interaction studies and will help in the design of inhibitors for their activity leads to Alzheimer directly or indirectly. Design of such inhibitors will be a promising approach for the prevention and cure of AD.

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Comprehensive analysis of non-synonymous missense SNPs of human galactose mutarotase (GALM) gene: an integrated computational approach

Journal of Biomolecular Structure & Dynamics, Jan 2, 2023

Data Mining, Big Data, Data Analytics

IGI Global eBooks, 2019

Big data analytics (BDA) is a systematic approach for analyzing and identifying different pattern... more Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-theart data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process. INDEX TERMS Big data analytics (BDA), data mining, data visualization, neural network, time series forecasting.

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Tiratha Singh

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