Correction to: Mapping genetic diversity with the GenomeIndia project (Nature Genetics, (2025), 57, 4, (767-773), 10.1038/s41588-025-02153-x) Chandrika Bhattacharyya, Krithika Subramanian, Bharathram Uppili, Nidhan K. Biswas, Shweta Ramdas, Karthik Bharadwaj Tallapaka, Prathima Arvind, Khader Valli Rupanagudi, Arindam Maitra, Tulasi Nagabandi, Tiyasha De, Kuldeep Singh, Praveen Sharma, Nanaocha Sharma, Sunil K. Raghav, Punit Prasad, E. V. Soniya, Abdul Jaleel, Shijulal Nelson Sathi, Madhvi Joshi, Chaitanya Joshi, Mayurika Lahiri, Santosh Dixit, L. S. Shashidhara, Nachimuthu Senthil Kumar, H. Lalhruaitluanga, Lal Nundanga, Venkataram Shivakumar, Ganesan Venkatasubramanian, Naren P. Rao, Mohd Ashraf Ganie, Imtiyaz Ahmad Wani, Ganganath Jha, Ashwin Dalal, Murali Dharan Bashyam, Pritish Kumar Varadwaj, Sanjeev BS, Yogesh Simmhan, Chirag Jain, Durai Sundar, Ishaan Gupta, Pankaj Yadav, Himanshu Sinha, Manikandan Narayanan, Karthik Raman, Raghu Padinjat, Radhakrishnan Sabarinathan, , , Kumarasamy Thangaraj, Mitali Mukerji, Sridhar Sivasubbu, Vinod Scaria, , Suman K. Paine, Karthik Bharadwaj Tallapaka, Govindarajan Umapathy, Vinay K. Nandicoori, Rakesh Mishra, Dinabandhu Sahoo, Ajay Parida, M. Radhakrishna Pillai, Abitha Thomas, L. S. Shashidhara, Nachimuthu Senthil Kumar, Arun Kumar, B. Jayaram, Padinjat Raghu, , Azad Ali, Mahabub Alam, Parveena Choudhury, Poulomi Ghosh, Sukanya Dhar, Saurav Roy, Nasrin Parvin, Rahul Modak, Sayan Bhowmick, Sourav Gangopadhyay, Devashish Tripathi, K. S. H. Shafeeq, G. Rajesh, C. Mohana, A. Divakar, Reddy P. Kommaddi, Neha Singh, Priya Pandey, Devavrat Desai, Mahfuj Hassan, Deepak Kumar Kashyap, Vasantha Kumar, Aman Kumar Suryan, Hema Sindhuja Rachiraju, A. Mahesh, Sushmita Nitta, Vijaya Mohan, Karthikeyan Meenakshisundaram, Jagamohan Chhatai, G. Mala, Sandeep Kumar Pal, Simmy Kaur, Mahino Fatima, Mohammed Akbar, Rahul C. Bhoyar, Pooja Sharma, Shreya Bari, Pratima Pandey, Anushree Mishra, Nishat Ashrafi, Syed Ahmad, Deepak Mudila, Arun Sree Parameswaran, Dolat Singh Shekhawat, Nayan Tada, Tanuja Rajial, Varuna Vyas, Arvinda Thoudam, H. Moushmi Sharma, Khuraijam Dolly Devi, Teresa Tangpua, Adyasha Mishra, Arup Ghosh, Deepak Jena, Soumendu Mahapatra, Sudarshana Jena, Sudeshna Datta, Shijulal Nelson Sathi, Abhitha Thomas, Udaya Lekshmi, R. A. Aswanth, Anjana S. Nair, Vasudev Paveri, T. S. Amal, Aman Tripathi, Bhagirath Dave, Bhumika Prajapati, Ramesh Pandit, Sanman Samova, Ajay Malik, Kajal Gaikwad, Siddharth Gahlaut, Andrew Vanlallawma, John Zohmingthanga, Lalawmpuii Pachuau, Lalchhandama Chhakchhuak, Ranjan Jyoti Sarma, Daddaladka Krishnayya Samartha, Paranthaman V. Kavya, S. G. Tejaswini, Bashir Ahmad Charoo, Mahrukh Hameed Zargar, , K. H. Rakesh, Shobha Anilkumar, , Saurav Roy, Shouvanik Sengupta, Indranil Bagchi, Subrata Patra, M. H. K. Mujawar, Vinayak Hosawad, Valli Undamatla, Pratheusa Machha, Shahrumi Reza, Divya Goel, Bharathram Uppilli, Arushi Batra, Ashvarya Shankar, Gayatri Singh, Suman Mudila, Saima Iram, Mohamed Imran, Mohit Divakar, Vigneshwar Senthivel, , Vinay More, Arghya Dey, Haya Afreen, Animesh Kumar Singh, Arnab Ghosh, Chitrarpita Das, Debashree Tagore, Subrata Das, Krithika Subramanian, Shreya Chakraborty, Raghvendra Agrawal, Sauma Suvra Majumdar, Siddhi Jani, Akkshaya Rajesh, Debasrija Mondal, Anand Kumar, Debdutta Chatterjee, Priyanka Singh, A. Sohan Angelo, Tanmay Panigrahi, Eric Macwan, Rupanwita Majumder, S. Sagar, Samarpita Saha, Payel Mukherjee, Sreelekshmi MS, Jayesh Jain, Sofia Banu, Malini Nemalikanti, Sriram Sudarsanam, Ankit Mukherjee, Bani Jolly, Jupita Handique, , , V. Jothibasu, S. Karthik, Divya Tej Sowpati, Sanjay Deshpande, Deepak T. Nair, Saurabh Raghuvanshi, , Asmita Gupta, Sumedha Avadhanula, Imlimaong Aier, Rahul Semwal, B. S. Sanjeev, Ajeya Bhat, Nagakishore Jammula, Sai Manasa Chadalavada, Nirmal Singh Mahar, Jyoti Sharma, Rajveer Singh Shekhawat, Soham Biswas, Ayam Gupta, Harshita Agarwal, Venkatesh Kamaraj, Agastya Singh, Yadati Narahari, Vijayalakshmi Ravindranath, Kumarasamy Thangaraj, Divya Tej Sowpati, Mohammed Faruq, Analabha Basu, Bratati Kahali Nature Genetics, 2025
Mapping genetic diversity with the GenomeIndia project Chandrika Bhattacharyya, Krithika Subramanian, Bharathram Uppili, Nidhan K. Biswas, Shweta Ramdas, Karthik Bharadwaj Tallapaka, Prathima Arvind, Khader Valli Rupanagudi, Arindam Maitra, Tulasi Nagabandi, Tiyasha De, Kuldeep Singh, Praveen Sharma, Nanaocha Sharma, Sunil K. Raghav, Punit Prasad, E. V. Soniya, Abdul Jaleel, Shijulal Nelson Sathi, Madhvi Joshi, Chaitanya Joshi, Mayurika Lahiri, Santosh Dixit, L. S. Shashidhara, Nachimuthu Senthil Kumar, H. Lalhruaitluanga, Lal Nundanga, Venkataram Shivakumar, Ganesan Venkatasubramanian, Naren P. Rao, Mohd Ashraf Ganie, Imtiyaz Ahmad Wani, Ganganath Jha, Ashwin Dalal, Murali Dharan Bashyam, Pritish Kumar Varadwaj, Sanjeev BS, Yogesh Simmhan, Chirag Jain, Durai Sundar, Ishaan Gupta, Pankaj Yadav, Himanshu Sinha, Manikandan Narayanan, Karthik Raman, Raghu Padinjat, Radhakrishnan Sabarinathan, , , Kumarasamy Thangaraj, Mitali Mukerji, Sridhar Sivasubbu, Vinod Scaria, , Suman K. Paine, Yadati Narahari, Karthik Bharadwaj Tallapaka, Govindarajan Umapathy, Vinay K. Nandicoori, Rakesh Mishra, Dinabandhu Sahoo, Ajay Parida, M. Radhakrishna Pillai, Abitha Thomas, L. S. Shashishara, Nachimuthu Senthil Kumar, Y. Narahari, Arun Kumar, B. Jayaram, Padinjat Raghu, , Azad Ali, Mahabub Alam, Parveena Choudhury, Poulomi Ghosh, Sukanya Dhar, Saurav Roy, Nasrin Parvin, Rahul Modak, Sayan Bhowmick, Sourav Gangopadhyay, Devashish Tripathi, K. S. H. Shafeeq, G. Rajesh, C. Mohana, A. Divakar, Reddy P. Kommaddi, Neha Singh, Priya Pandey, Devavrat Desai, Mahfuj Hassan, Deepak Kumar Kashyap, Vasantha Kumar, Aman Kumar Suryan, Hema Sindhuja Rachiraju, A. Mahesh, Sushmita Nitta, Vijaya Mohan, Karthikeyan Meenakshisundaram, Jagamohan Chhatai, G. Mala, Sandeep Kumar Pal, Simmy Kaur, Mahino Fatima, Mohammed Akbar, Rahul C. Bhoyar, Pooja Sharma, Shreya Bari, Pratima Pandey, Anushree Mishra, Nishat Ashrafi, Syed Ahmad, Deepak Mudila, Arun Sree Parameswaran, Dolat Singh Shekhawat, Nayan Tada, Tanuja Rajial, Varuna Vyas, Arvinda Thoudam, H. Moushmi Sharma, Khuraijam Dolly Devi, Teresa Tangpua, Adyasha Mishra, Arup Ghosh, Deepak Jena, Soumendu Mahapatra, Sudarshana Jena, Sudeshna Datta, Shijulal Nelson Sathi, Abhitha Thomas, Udaya Lekshmi, R. A. Aswanth, Anjana S. Nair, Vasudev Paveri, T. S. Amal, Aman Tripathi, Bhagirath Dave, Bhumika Prajapati, Ramesh Pandit, Sanman Samova, Ajay Malik, Kajal Gaikwad, Siddharth Gahlaut, Andrew Vanlallawma, John Zohmingthanga, Lalawmpuii Pachuau, Lalchhandama Chhakchhuak, Lalnundanga, Ranjan Jyoti Sarma, Daddaladka Krishnayya Samartha, Paranthaman V. Kavya, S. G. Tejaswini, Bashir Ahmad Charoo, Mahrukh Hameed Zargar, , K. H. Rakesh, Shobha Anilkumar, , Sourav Roy, Shouvanik Sengupta, Indranil Bagchi, Subrata Patra, M. H. K. Mujawar, Vinayak Hosawad, Valli Undamatla, Pratheusa Maccha, Shahrumi Reza, Divya Goel, Bharathram Uppilli, Arushi Batra, Ashvarya Shankar, Gayatri Singh, Suman Mudila, Saima Iram, Mohamed Imran, Mohit Divakar, Vigneshwar Senthivel, , Vinay More, Arghya Dey, Haya Afreen, Animesh Kumar Singh, Arnab Ghosh, Chitrarpita Das, Debashree Tagore, Subrata Das, Krithika Subramanian, Shreya Chakraborty, Raghvendra Agrawal, Sauma Suvra Majumdar, Siddhi Jani, Akkshaya Rajesh, Debasrija Mondal, Anand Kumar, Debdutta Chatterjee, Priyanka Singh, A. Sohan Angelo, Tanmay Panigrahi, Eric Macwan, Rupanwita Majumder, S. Sagar, Samarpita Saha, Payel Mukherjee, Sreelekshmi MS, Jayesh Jain, Sofia Banu, Malini Nemalikanti, Sriram Sudarsanam, Ankit Mukerji, Bani Jolly, Jupita Handique, , , V. Jothibasu, S. Karthik, Divya Tej Sowpati, Sanjay Deshpande, Deepak T. Nair, Saurabh Raghuvanshi, , Asmita Gupta, Sumedha Avadhanula, Imlimaong Aier, Rahul Semwal, B. S. Sanjeev, Ajeya Bhat, Nagakishore Jammula, Sai Manasa Chadalavada, Nirmal Singh Mahar, Jyoti Sharma, Rajveer Singh Shekhawat, Soham Biswas, Ayam Gupta, Harshita Agarwal, Venkatesh Kamaraj, Agastya Singh, Yadati Narahari, Vijayalakshmi Ravindranath, Thangaraj Kumarasamy, Divya Tej Sowpati, Mohammed Faruq, Analabha Basu, Bratati Kahali Nature Genetics, 2025
XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm Pankaj Tyagi, Anju Sharma, Rahul Semwal, Uma Shanker Tiwary, Pritish Kumar Varadwaj Journal of Biomolecular Structure and Dynamics, 2024 Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.
E-nose: a low-cost fruit ripeness monitoring system Pankaj Tyagi, Rahul Semwal, Anju Sharma, Uma Shanker Tiwary, Pritish Varadwaj Journal of Agricultural Engineering, 2023 All fruits emit some specific volatile organic compounds (VOCs) during their life cycle. These VOCs have specific characteristics, by using these characteristics fruit ripening stage can be identified without destructing the fruit. In this study, an application-specific electronic nose device was designed for monitoring fruit ripeness.The proposed electronic nose is cost-efficient and does not require any modern or costly laboratory instruments. Metal oxide semiconductor (MOS) sensors were used for designing the proposed electronic nose. These MOS sensors were integrated with a microcontroller board to detect and extract the meaningful features of VOCs, and an artificial neural network (ANN) algorithm was used for pattern recognition. Measurements were done with apples, bananas, oranges, grapes, and pomegranates. The designed electronic nose proved to be reliable in classifying fruit samples into three different fruit ripening stage (unripe, ripe, and over-ripe) with high precision and recall. The proposed electronic nose performed uniformly on all three fruit ripening stages with an average accuracy of ≥ 95%.
DeepLBS: A deep Convolutional Neural Network-Based Ligand-Binding Site Prediction Tool Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Utkarsh Raj, Pritish Kumar Varadwaj 2023 6th International Conference on Information Systems and Computer Networks Iscon 2023, 2023 In the recent past, with the improvement of high throughput technology, the availability of protein structural data has increased exponentially. All these structural data have to be correctly mapped to their functional attributes to decode their biological role. However, to perform the functional annotation of these structural entities, the essential move is to locate the ligand-binding site (LBS) information. Although many approaches have been proposed to locate the LBS, most have low performance in terms of predictive quality. In this proposed work, we are presenting a deep neural network-based approach, DeepLBS, which uses geometrical as well as pharmacophoric properties to quantify the ligand-binding site (LBS) with high accuracy. To determine the efficiency of our work, DeepLBS was compared with the most recently developed deep learning tools. The result demonstrated that DeepLBS outperformed the existing state of art tools in terms of predictive quality.
Pr[m]: An Algorithm for Protein Motif Discovery Rahul Semwal, Imlimaong Aier, Utkarsh Raj, Pritish Kumar Varadwaj IEEE ACM Transactions on Computational Biology and Bioinformatics, 2022 Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].
DeepOlf: Deep Neural Network Based Architecture for Predicting Odorants and Their Interacting Olfactory Receptors Anju Sharma, Rajnish Kumar, Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Pritish K. Varadwaj IEEE ACM Transactions on Computational Biology and Bioinformatics, 2022 Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR's) present in the nasal cavity. Different odorants stimulate different OR's due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier's performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83 and 99.92 percent for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at https://bioserver.iiita.ac.in/deepolf/.
DeEPn: a deep neural network based tool for enzyme functional annotation Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Pritish Kumar Varadwaj Journal of Biomolecular Structure and Dynamics, 2021 With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/. Communicated by Ramaswamy H. Sarma.
Comparative modeling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma Imlimaong Aier, Rahul Semwal, Utkarsh Raj, Pritish Kumar Varadwaj Journal of Biomolecular Structure and Dynamics, 2021 Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modeled, and the response of key amino acids to perturbation was identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of −9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches. Communicated by Ramaswamy H. Sarma
HAMP: A knowledge-base of antimicrobial peptides from human microbiome Viswajit Mulpuru, Rahul Semwal, Pritish Kumar Varadwaj, Nidhi Mishra Current Bioinformatics, 2021 Background: Antimicrobial peptides (AMPs) can defend the hosts against various pathogens and are found in almost every life form from microorganisms to humans. As the rapid increase of drug-resistant strains in recent years is presenting a serious challenge to healthcare, antimicrobial peptides (AMPs) can revolutionize the antimicrobial development against the drugresistant microbes. Objective: The objective was to encourage the study on the human microbiome towards the inhibition of drug-resistant bacteria by the development of a database containing antimicrobial peptides from the human microbiome. Methods: This database is an outcome of an extended analysis of human metagenome, involving the prediction of coding regions, extraction of peptides, prediction of antimicrobial peptides, and modeling their structure utilizing different in silico tools. Furthermore, an intelligent hash function-based query engine was designed to validate the novelty of specific candidate peptide over the reported Knowledge-base. Result and Discussion: This Knowledge-base currently focuses on antimicrobial peptide sequences (AMPs) predicted from the human microbiome along with their 3D structures modeled using various modeling and molecular dynamics approaches. It includes a total of 1087 unique AMPs from various body sites, with 454 AMPs from the oral cavity, 180 AMPs from the gastrointestinal tract, 42 AMPs from the skin, 12 AMPs from the airway, 6 AMPs from the urogenital tract and 393 AMPs from undefined body locations. A scoring matrix has been generated based on the similarity scores of the sequences that have been incorporated into the Knowledge-base. Furthermore, a Jmol applet is included in the website to help users visualize the 3D structures. Conclusion: The information and functions of the Knowledge-base can offer great help in finding novel antimicrobial drugs, especially towards finding inhibitors for drug-resistant bacteria. The HAMP is freely available at https://bioserver.iiita.ac.in/amp/index.html.
