2025

  1. 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

  2. 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

  3. 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

  4. 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, 2025.
    https://doi.org/10.1016/j.cpb.2025.100521

  5. 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