2025

1. Kumari, A., Bhati, U., Shankar, R., Yadav, K., S., Sopory, K., S., & Joshi, R., 2025. Genome-wide identification, characterization, and expression pattern analysis of the glyoxalase gene family in Phyllostachys pubescens during abiotic stresses. BMC Plant Biology (Accepted).


2. Gupta, S., Bhati, U., Jyoti, Kesarwani, K., Sharma, A., Kumar, A., & Shankar, R., 2025. Conference Poster: The Genomics Armoury @ The Himalayan Centre for High-throughput Computational Biology. Genomics India Conference, IISc, Bengaluru.
   Find the conference poster here


3. Bhati, U., Sharma, A., Gupta, S., Kumar, A., Pradhan, U. K., & Shankar, R., 2025. Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning. Current Plant Biology.
   https://doi.org/10.1016/j.cpb.2025.100521


4. Kesarwani, V., Kumar, A., Sharma, A., Gupta, S., & Shankar, R., 2025. Deep-Learning on condition specific expression profiles reveal critical cytosines in gene regulation. bioRxiv, pp.2025-06.
   https://www.biorxiv.org/content/10.1101/2025.06.01.657178v1


5. Gupta, S., S., Kesarwani, V., Shankar, R., & Sharma, U., 2025. Chemoinformatics exploration of synthetically accessible N-heterocycles: uncovering new antifungal lead candidates. In Silico Pharmacology 13, 74.
   https://doi.org/10.1007/s40203-025-00359-9


6. Kumari, M., Kumar, P., Saini, V., Joshi, R., Shankar, R., & Kumar, R., 2025. Transcriptional landscape illustrates the diversified adaptation of medicinal plants to multifactorial stress combinations linked with high altitude. Planta 261, 111.
   https://doi.org/10.1007/s00425-025-04686-1


7. Gupta, S., Kumar, A., Kesarwani, V. & Shankar, R., 2025. DMRU: Generative Deep-Learning to unravel condition specific cytosine methylation in plants. bioRxiv, pp.2025-02.
   https://doi.org/10.1101/2025.02.06.635186

2024

1. Jyoti, Ritu, Gupta, S., & Shankar, R. (2024). Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery. Heliyon, 10(20), e39140.
   https://doi.org/10.1016/j.heliyon.2024.e39140


2. Nath, J., Joshi, S., Gupta, S., Kesarwani, V., Shankar, R., & Joshi, R. (2024). Genome-wide identification of WUSHEL-related homeobox genes reveals their differential regulation during cold stress and in vitro organogenesis in Picrorhiza kurrooa Royle ex Benth. In Vitro Cellular & Developmental Biology - Plant, 60(4), 439-455.
   https://doi.org/10.1007/s11627-024-10442-z


3. Gupta, S., Kesarwani, V., Bhati, U., Jyoti, N., & Shankar, R. (2024). PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants. Briefings in Bioinformatics, 25(4).
   https://doi.org/10.1093/bib/bbae324


4. Gupta, S., Jyoti, Bhati, U., Kesarwani, V., Sharma, A., & Shankar, R., 2024. PTF-Vac: Ab-initio discovery of plant transcription factors binding sites using deep co-learning encoders-decoders. bioRxiv, pp.2024-01.
   https://doi.org/10.1101/2024.01.28.577608


5. Choudhary, S., Shanu, K., Hegde, A. S., Kesarwani, V., Kumar, R., Shankar, R., Devi, S., & Srivatsan, V. (2024). Nutritional quality and microbial diversity of Chhurpe from different milk sources: an ethnic fermented food of high-altitude regions of the Western Himalayas. Discover Food, 4(1).
   https://doi.org/10.1007/s44187-024-00073-z

2023

1. Suresh, P. S., Kesarwani, V., Kumari, S., Shankar, R., & Sharma, U. (2023). Flavonoids from aerial parts of Cissampelos pareira L. as antimalarial agents: Computational validation of ethnopharmacological relevance. South African Journal of Botany, 163, 10-19.
   https://doi.org/10.1016/j.sajb.2023.10.017


2. Gupta, S., & Shankar, R. (2023). miWords: transformer-based composite deep learning for highly accurate discovery of pre-miRNA regions across plant genomes. Briefings in Bioinformatics, 24(2).
   https://doi.org/10.1093/bib/bbad088


3. Suresh, P. S., Kesarwani, V., Kumari, S., Shankar, R., & Sharma, U. (2023a). Bisbenzylisoquinolines from Cissampelos pareira L. as antimalarial agents: Molecular docking, pharmacokinetics analysis, and molecular dynamic simulation studies. Computational Biology and Chemistry, 104, 107826.
   https://doi.org/10.1016/j.compbiolchem.2023.107826

2022

1. Bhattacharyya, P., Sharma, T., Yadav, A., Lalthafamkimi, L., Ritu, N., Swarnkar, M. K., Joshi, R., Shankar, R., & Kumar, S. (2022). De novo transcriptome based insights into secondary metabolite biosynthesis in Malaxis acuminata (Jeevak) - A therapeutically important orchid. Frontiers in Plant Science, 13.
   https://doi.org/10.3389/fpls.2022.954467


2. Rathore, N., Kumar, P., Mehta, N., Swarnkar, M. K., Shankar, R., & Chawla, A. (2022). Time-series RNA-Seq transcriptome profiling reveals novel insights about cold acclimation and de-acclimation processes in an evergreen shrub of high altitude. Scientific Reports, 12(1).
   https://doi.org/10.1038/s41598-022-19834-w


3. Ritu, Gupta, S., Sharma, N. K., & Shankar, R. (2022). DeepPlnc: Bi-modal deep learning for highly accurate plant lncRNA discovery. Genomics, 114(5), 110443.
   https://doi.org/10.1016/j.ygeno.2022.110443

2021

1. Kumari, M., Pradhan, U. K., Joshi, R., Punia, A., Shankar, R., & Kumar, R. (2021). In-depth assembly of organ and development dissected Picrorhiza kurroa proteome map using mass spectrometry. BMC Plant Biology, 21(1).
   https://doi.org/10.1186/s12870-021-03394-8


2. Sharma, N. K., Gupta, S., Kumar, A., Kumar, P., Pradhan, U. K., & Shankar, R. (2021). RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery. iScience, 24(12), 103381.
   https://doi.org/10.1016/j.isci.2021.103381


3. Pradhan, U. K., Sharma, N. K., Kumar, P., Kumar, A., Gupta, S., & Shankar, R. (2021). miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles. PLoS ONE, 16(10), e0258550.
   https://doi.org/10.1371/journal.pone.0258550


4. Sharma, T., Sharma, N. K., Kumar, P., Panzade, G., Rana, T., Swarnkar, M. K., Singh, A. K., Singh, D., Shankar, R., & Kumar, S. (2021). The first draft genome of Picrorhiza kurrooa, an endangered medicinal herb from Himalayas. Scientific Reports, 11(1).
   https://doi.org/10.1038/s41598-021-93495-z


5. Gupta, S. S., Kumar, A., Shankar, R., & Sharma, U. (2021). In silico approach for identifying natural lead molecules against SARS-COV-2. Journal of Molecular Graphics and Modelling, 106, 107916.
   https://doi.org/10.1016/j.jmgm.2021.107916

2020

1. Mala, D., Awasthi, S., Sharma, N. K., Swarnkar, M. K., Shankar, R., & Kumar, S. (2021). Comparative transcriptome analysis of Rheum australe, an endangered medicinal herb, growing in its natural habitat and those grown in controlled growth chambers. Scientific Reports, 11(1).
   https://doi.org/10.1038/s41598-020-79020-8


2. Shankar, R. (2020). The dynamic aspects of RNA regulation. In Elsevier eBooks (pp. 85-115).
   https://doi.org/10.1016/b978-0-12-817193-6.00004-2

2019

1. Kumari, M., Thakur, S., Kumar, A., Joshi, R., Kumar, P., Shankar, R., & Kumar, R. (2019). Regulation of color transition in purple tea (Camellia sinensis). Planta, 251(1).
   https://doi.org/10.1007/s00425-019-03328-7


2. Panzade, G., Gangwar, I., Awasthi, S., Sharma, N., & Shankar, R. (2019). Plant Regulomics Portal (PRP): a comprehensive integrated regulatory information and analysis portal for plant genomes. Database, 2019.
   https://doi.org/10.1093/database/baz130


3. Dhiman, N., Sharma, N. K., Thapa, P., Sharma, I., Swarnkar, M. K., Chawla, A., Shankar, R., & Bhattacharya, A. (2019). De novo transcriptome provides insights into the growth behaviour and resveratrol and trans-stilbenes biosynthesis in Dactylorhiza hatagirea - An endangered alpine terrestrial orchid of western Himalaya. Scientific Reports, 9(1).
   https://doi.org/10.1038/s41598-019-49446-w

2018

1. Rajan, S., Panzade, G., Srivastava, A., Shankar, K., Pandey, R., Kumar, D., Gupta, S., Gupta, A., Varshney, S., Beg, M., Mishra, R. K., Shankar, R., & Gaikwad, A. (2018). miR-876-3p regulates glucose homeostasis and insulin sensitivity by targeting adiponectin. Journal of Endocrinology, 239(1), 1-17.
   https://doi.org/10.1530/joe-17-0387


2. Goel, P., Sharma, N. K., Bhuria, M., Sharma, V., Chauhan, R., Pathania, S., Swarnkar, M. K., Chawla, V., Acharya, V., Shankar, R., & Singh, A. K. (2018). Transcriptome and Co-Expression Network Analyses Identify Key Genes Regulating Nitrogen Use Efficiency in Brassica juncea L. Scientific Reports, 8(1).
   https://doi.org/10.1038/s41598-018-25826-6

2017

1. Gangwar, I., Sharma, N. K., Panzade, G., Awasthi, S., Agrawal, A., & Shankar, R. (2017). Detecting the Molecular System Signatures of Idiopathic Pulmonary Fibrosis through Integrated Genomic Analysis. Scientific Reports, 7(1).
   https://doi.org/10.1038/s41598-017-01765-6

2016

1. Kumar, A., Chawla, V., Sharma, E., Mahajan, P., Shankar, R., & Yadav, S. K. (2016). Comparative Transcriptome Analysis of Chinary, Assamica and Cambod tea (Camellia sinensis) Types during Development and Seasonal Variation using RNA-seq Technology. Scientific Reports, 6(1).
   https://doi.org/10.1038/srep37244


2. Bhartiya, D., Chawla, V., Ghosh, S., Shankar, R., & Kumar, N. (2016). Genome-wide regulatory dynamics of G-quadruplexes in human malaria parasite Plasmodium falciparum. Genomics, 108(5-6), 224-231.
   https://doi.org/10.1016/j.ygeno.2016.10.004


3. Jayaswall, K., Mahajan, P., Singh, G., Parmar, R., Seth, R., Raina, A., Swarnkar, M. K., Singh, A. K., Shankar, R., & Sharma, R. K. (2016). Transcriptome Analysis Reveals Candidate Genes involved in Blister Blight defense in Tea (Camellia sinensis (L) Kuntze). Scientific Reports, 6(1).
   https://doi.org/10.1038/srep30412


4. Chawla, V., Kumar, R., & Shankar, R. (2016). Identifying wrong assemblies in de novo short read primary sequence assembly contigs. Journal of Biosciences, 41(3), 455-474.
   https://doi.org/10.1007/s12038-016-9630-0


5. Bhardwaj, J., Gangwar, I., Panzade, G., Shankar, R., & Yadav, S. K. (2016). Global De Novo Protein-Protein Interactome Elucidates Interactions of Drought-Responsive Proteins in Horse Gram (Macrotyloma uniflorum). Journal of Proteome Research, 15(6), 1794-1809.
   https://doi.org/10.1021/acs.jproteome.5b01114


6. Manjunatha, B. L., Singh, H. R., Ravikanth, G., Nataraja, K. N., Shankar, R., Kumar, S., & Shaanker, R. U. (2016). Transcriptome analysis of stem wood of Nothapodytes nimmoniana (Graham) Mabb. identifies genes associated with biosynthesis of camptothecin, an anti-carcinogenic molecule. Journal of Biosciences, 41(1), 119-131.
   https://doi.org/10.1007/s12038-016-9591-3

2015

1. Shafi, A., Chauhan, R., Gill, T., Swarnkar, M. K., Sreenivasulu, Y., Kumar, S., Kumar, N., Shankar, R., Ahuja, P. S., & Singh, A. K. (2015). Expression of SOD and APX genes positively regulates secondary cell wall biosynthesis and promotes plant growth and yield in Arabidopsis under salt stress. Plant Molecular Biology, 87(6), 615-631.
   https://doi.org/10.1007/s11103-015-0301-6


2. Mehra, M., Gangwar, I., & Shankar, R. (2015). A deluge of complex repeats: the Solanum genome. PLoS ONE, 10(8), e0133962.
   https://doi.org/10.1371/journal.pone.0133962


3. Jha, A., Panzade, G., Pandey, R., & Shankar, R. (2015). A legion of potential regulatory sRNAs exists beyond the typical microRNAs microcosm. Nucleic Acids Research, 43(18), 8713-8724.
   https://doi.org/10.1093/nar/gkv871

2014

1. Paul, A., Jha, A., Bhardwaj, S., Singh, S., Shankar, R., & Kumar, S. (2014). RNA-seq-mediated transcriptome analysis of actively growing and winter dormant shoots identifies non-deciduous habit of evergreen tree tea during winters. Scientific Reports, 4(1).
   https://doi.org/10.1038/srep05932


2. Jha, A., & Shankar, R. (2014). miRNAting control of DNA methylation. Journal of Biosciences, 39(3), 365380.
   https://doi.org/10.1007/s12038-014-9437-9


3. Kumari, A., Singh, H., Jha, A., Swarnkar, M., Shankar, R., & Kumar, S. (2014). Transcriptome sequencing of rhizome tissue of Sinopodophyllum hexandrum at two temperatures. BMC Genomics, 15(1), 871.
   https://doi.org/10.1186/1471-2164-15-871

2013

1. Bhardwaj, J., Chauhan, R., Swarnkar, M. K., Chahota, R. K., Singh, A. K., Shankar, R., & Yadav, S. K. (2013). Comprehensive transcriptomic study on horse gram (Macrotyloma uniflorum): De novo assembly, functional characterization and comparative analysis in relation to drought stress. BMC Genomics, 14(1).
   https://doi.org/10.1186/1471-2164-14-647


2. Jha, A., & Shankar, R. (2013). miReader: Discovering Novel miRNAs in Species without Sequenced Genome. PLoS ONE, 8(6), e66857.
   https://doi.org/10.1371/journal.pone.0066857


3. Thakur, K., Chawla, V., Bhatti, S., Swarnkar, M. K., Kaur, J., Shankar, R., & Jha, G. (2013). De Novo Transcriptome Sequencing and Analysis for Venturia inaequalis, the Devastating Apple Scab Pathogen. PLoS ONE, 8(1), e53937.
   https://doi.org/10.1371/journal.pone.0053937

2012

1. Jha, A., Chauhan, R., Mehra, M., Singh, H. R., & Shankar, R. (2012). MIR-BAG: Bagging Based identification of MicroRNA precursors. PLoS ONE, 7(9), e45782.
   https://doi.org/10.1371/journal.pone.0045782


2. Gahlan, P., Singh, H. R., Shankar, R., Sharma, N., Kumari, A., Chawla, V., Ahuja, P. S., & Kumar, S. (2012). De novo sequencing and characterization of Picrorhiza kurrooa transcriptome at two temperatures showed major transcriptome adjustments. BMC Genomics, 13(1).
   https://doi.org/10.1186/1471-2164-13-126

2011

1. Jha, A., & Shankar, R. (2011). Employing machine learning for reliable miRNA target identification in plants. BMC Genomics, 12(1).
   https://doi.org/10.1186/1471-2164-12-636


2. Jha, A., Mehra, M., & Shankar, R. (2011). The regulatory epicenter of miRNAs. Journal of Biosciences, 36(4), 621-638.
   https://doi.org/10.1007/s12038-011-9109-y

2010

1. Shankar, R. (2010). The bioinformatics of next generation sequencing: a meeting report. Journal of Molecular Cell Biology, 3(3), 147-150.
   https://doi.org/10.1093/jmcb/mjq024


2. Heikham, R., & Shankar, R. (2010). Flanking region sequence information to refine microRNA target predictions. Journal of Biosciences, 35(1), 105-118.
   https://doi.org/10.1007/s12038-010-0013-7