Biology subjects

Palaniappan, A.

Publications and source records attributed to Palaniappan, A..

9 recordsLinked to original sources

bioRxiv2024

GenomicSign: A computational method to discover unique, specific, and amplifiable signatures of target genomic sequences

Molecular diagnostics for the rapid identification of infectious, virulent, and pathogenic organisms are key to health and global security. Such methods rely on the identification and detection of signatures possessed by the organism. In this work, we outline a computational algorithm, GenomicSign, to determine unique and amplifiable genomic signatures of a set of target sequences against a background set of non-target sequences. The set of target sequences might comprise variants of a pathogen of interest, say SARS-CoV2 virus. Unique k-mers of the consensus target sequence for a range of k-values are determined, and the threshold k-value yielding a sharp transition in the number of unique k-mers is identified as kopt. Corresponding unique k-mers for k [≥] kopt are compared against the set of non-target sequences to identify target-specific unique k-mers. A pair of proximal such k-mers could enclose a potential amplicon. Primers to such pairs are designed and scored using a custom scheme to rank the potential amplicons. The top-ranked resulting amplicons are candidates for unique and amplifiable genomic signatures. The entire workflow is demonstrated using a case study with the SARS-CoV2 omicron genome. A case study distinguishing the SARS-CoV2 omicron target strain against non-target other SARS-CoV2 variants is performed to illustrate the workflow. GenomicSign has been implemented in Python and is available as an open-source software under MIT licence (https://www.github.com/apalania/GenomicSign).

bioinformatics↗

bioRxiv2021

miR2Trait: an integrated resource for investigating miRNA-disease associations

MicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function could lead to cascading effects culminating in pathophenotypes. A better understanding of the role of miRNAs in diseases would aid human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogues, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. The resource could aid in identifying the disease enrichment of a user-given set of miRNAs and analyzing the miRNA profile of a specified diseasome. miR2Trait is available as both a web-server (https://sas.sastra.edu/pymir18) and an open-source command-line interface (https://github.com/miR2Trait) under MIT license for both commercial and non-commercial use. The datasets are available for download at: https://doi.org/10.6084/m9.figshare.8288825.

bioinformatics↗

bioRxiv2021

End-to-end computational approach to the design of RNA biosensors for miRNA biomarkers of cervical cancer

Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in todays world. The approach has the potential to direct experimental efforts and minimise costs. All resources including the machine learning toolkit, reaction kinetics simulation, designed toehold sequences, genetic circuits, data and sbml files for replicating and utilizing our study are provided open-source with the iGEM Foundation (https://github.com/igem2019) under GNU GPLv3 licence.

synthetic biology↗

bioRxiv2021

NanoTox: Development of a parsimonious in silico model for toxicity assessment of metal-oxide nanoparticles using physicochemical features

Metal-oxide nanoparticles find widespread applications in mundane life today, and cost-effective evaluation of their cytotoxicity and ecotoxicity is essential for sustainable progress. Machine learning models use existing experimental data, and learn the relationship of various features to nanoparticle cytotoxicity to generate predictive models. In this work, we adopted a principled approach to this problem by formulating a feature space based on intrinsic and extrinsic physico-chemical properties, but exclusive of any in vitro characteristics such as cell line, cell type, and assay method. A minimal set of features was developed by applying variance inflation analysis to the correlation structure of the feature space. Using a balanced dataset, a mapping was then obtained from the normalized feature space to the toxicity class using various hyperparameter-tuned machine learning models. Evaluation on an unseen test set yielded > 96% balanced accuracy for both the random forest model, and neural network with one hidden layer model. The obtained cytotoxicity models are parsimonious, with intelligible inputs, and include an applicability check. Interpretability investigations of the models yielded the key predictor variables of metal-oxide nanoparticle cytotoxicity. Our models could be applied on new, untested oxides, using a majority-voting ensemble classifier, NanoTox, that incorporates the neural network, random forest, support vector machine, and logistic regression models. NanoTox is the very first predictive nanotoxicology pipeline made freely available under the GNU General Public License (https://github.com/NanoTox).

bioengineering↗

bioRxiv2020

Computational studies of drug repurposing targeting P-glycoprotein mediated multidrug-resistance phenotypes in agents of neglected tropical diseases

Mammalian ABCB1 P-glycoprotein is an ATP- dependent efflux pump with broad substrate specificity associated with cellular drug resistance. Homologous to this role in mammalian biology, the P-glycoprotein of agents of neglected tropical diseases (NTDs) mediates the emergence of multidrug-resistance phenotypes. The clinical and socioeconomic implications of NTDs are exacerbated by the lack of research interest among Big Pharma for treating such conditions. This work aims to characterise P-gp homologues in certain agents of key NTDs, namely O_LIProtozoa: Leishmania major, Trypanosoma cruzi; C_LIO_LIHelminths: Onchocerca volvulus, Schistosoma mansoni. C_LI PSI-BLAST searches against the genome of each of these organisms confirmed the presence of P-gp homologues. Each homologue was aligned against five P-gp sequences of known structure, to identify the most suitable template based on sequence homology, phylogenetic nearest neighbor, and query coverage. Antibiotics used in the current line of therapy against each of these pathogens were identified using PubChem and their SMILES structures were converted to PDB using BABEL software. Potential antibiotics to test against the set of FDA-approved antibiotics were identified based on similarity to the chemical class of the known drugs and repurposing of the existing drugs. Docking studies of the respective modelled Pgp structures and the set of antibiotic ligands were carried out using AutoDock and the most tenable target-ligand conformations were assessed. The interacting residues within 4.5 [A] of the ligand were identified, and the binding pockets were studied. The relative efficacy of the new drugs and the interacting pump residues were identified. Our studies could lay the foundation for the development of effective synergistic or new therapies against key neglected tropical diseases.

bioinformatics↗

bioRxiv2019

Riboflow: using deep learning to classify riboswitches with ~99% accuracy

Riboswitches are cis-regulatory genetic elements that use an aptamer to control gene expression. Specificity to cognate ligand and diversity of such ligands have expanded the functional repetoire of riboswitches to mediate mounting apt responses to sudden metabolic demands and signal changes in environmental conditions. Given their critical role in microbial life, and novel uses in synthetic biology, riboswitch characterisation remains a challenging computational problem. Here we have addressed the issue with advanced deep learning frameworks, namely convolutional neural networks (CNN), and bidirectional recurrent neural networks (RNN) with Long Short-Term Memory (LSTM). Using a comprehensive dataset of 32 ligand classes and a stratified train-validate-test approach, we demonstrated the superior performance of both the deep models (CNN and RNN) relative to other conventional machine learning classifiers on all key performance metrics, including the ROC curve analysis. In particular, the bidirectional LSTM RNN emerged as the best-performing learning method for identifying the ligand-specificity of riboswitches with an accuracy > 0.99 and macro-averaged F-score of 0.96. A dynamic update functionality is inbuilt to account for the discovery of new riboswitches and extend the predictive modelling to any number of new additional classes. Our work would be valuable in the design and assembly of genetic circuits and the development of the next generation of antibiotics. The software is freely available as a Python package and standalone resource for wide use in genome annotation and biotechnology workflows. AvailabilityPyPi package: riboflow @ https://pypi.org/project/riboflow Repository with Standalone suite of tools: https://github.com/RiboswitchClassifier Language: Python 3.6 with numpy, keras, and tensorflow libraries. Licence: MIT

bioinformatics↗

bioRxiv2018

Novel significant stage-specific differentially expressed genes in liver hepatocellular carcinoma

Liver cancer is among the top deadly cancers worldwide with a very poor prognosis, and the liver is a particularly vulnerable site for metastasis of other cancers. In this study, we developed a novel computational framework for the stage-specific analysis of hepatocellular carcinoma initiation and progression. Using publicly available clinical and RNA-Seq data of cancer samples and controls, we annotated the gene expression matrix with sample stages. We performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in gene expression in cancer samples relative to control. Using a contrast against the control, we were able to identify differentially expressed genes (log fold change >2) that were significant at an adjusted p-value < 10E-3. In order to identify genes that were specific to each stage without confounding differential expression in other stages, we developed a full set of pairwise stage contrasts and enforced a p-value threshold (<0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI and GNMT, and ten stage-IV specific genes including GABRD, PGAM2 and PECAM1. Of these, only APOBEC3B is an established cancer driver gene. DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD, well studied in literature on other cancers, emerged as a stage-IV specific gene. Our findings could be validated using multiple sources of omics data as well as experimentally. The biomarkers identified herein could potentially underpin diagnosis as well as pinpoint drug targets.

cancer biology↗

bioRxiv2018

PromoterPredict: sequence-based modelling of Escherichia coli σ70 promoter strength yields logarithmic dependence between promoter strength and sequence

We present PromoterPredict, a dynamic multiple regression approach to predict the strength of Escherichia coli promoters binding the {sigma}70 factor of RNA polymerase. {sigma}70 promoters are ubiquitously used in recombinant DNA technology, but characterizing their strength is demanding in terms of both time and money. Using a well-characterized set of promoters, we trained a multivariate linear regression model and found that the log of the promoter strength is significantly linearly associated with a weighted sum of the -10 and -35 sequence profile scores. It was found that the two regions contributed almost equally to the promoter strength. PromoterPredict accepts -10 and -35 hexamer sequences and returns the predicted promoter strength. It is capable of dynamic learning from user-supplied data to refine the model construction and yield more confident estimates of promoter strength.\n\nAvailabilityOpen source code and a standalone executable with both dynamic model-building and prediction are available (under GNU General Public License 3.0) at https://github.com/PromoterPredict, and require Python 2.7 or greater. PromoterPredict is also available as a web service at https://promoterpredict.com.\n\nContactapalania@scbt.sastra.edu

bioinformatics↗

bioRxiv2016

Computational studies of P-glycoprotein polymorphisms in antiepileptic drug resistance mechanisms

The treatment of epilepsy using antiepileptogenic drugs is complicated by drug re-sistance, resulting in treatment failure in more than one-third of cases. Human P-glycoprotein (hPGP; MDR1) is a known epileptogenic mediator. Given that experimental investigations have suggested a role for pharmacogenetics in this treatment failure, it would be of interest to study hPGP polymorphisms that might contribute to the emergence of drug resistance. Changes in protein functional activity could result from point mutations as well as altered abundance. Bioinformatics approaches were used to assess and rank the functional impact of 20 missense MDR1 polymorphisms and the top five were selected. The structures of the wildtype and mutant hPGP were modelled based on the mouse PGP structure. Docking studies of the wildtype and mutant hPGP with four standard anti-epileptic drugs were carried out. Our results revealed that the drug binding site with respect to the wildtype protein was uniform. However the mutant hPGP proteins displayed a repertoire of binding sites with stronger binding affinities towards the drug. Our studies indicated that specific polymorphisms in MDR1 could drive conformational changes of PGP structure, facilitating altered contacts with drug-substrates and resulting in drug extrusion. This suggests that MDR1 polymorphisms could play an active role in modifying drug bioavailability, leading to pharmacoresistance in antiepileptic chemotherapy.

bioinformatics↗