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 | Dr. Shailesh Kumar, Ph.D.
Staff Scientist III, Bioinformatics Laboratory #202
National Institute of Plant Genome Research (NIPGR), New Delhi
Tel: 91-11-26735217
Fax: 91-11-26741658
E-mail: shailesh@nipgr.ac.in, shaileshgenome@gmail.com |
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 Research Area |
| Bioinformatics, Genomics, Big data analysis, Machine Learning (ML), Deep Learning, Artificial Intelligence (AI), and Plant Biotechnology |
| Professional & Academic Background |
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Staff Scientist III (July 2020- Present): National Institute of Plant Genome Research (NIPGR), New Delhi, India |
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Staff Scientist II (May 2017- June 2020): National Institute of Plant Genome Research (NIPGR), New Delhi, India |
| Postdoctoral Research Associate (2015-2017): University Of Virginia, Charlottesville, VA, USA |
| Research Scientist (2014-2015): Sir Ganga Ram Hospital, New Delhi, India |
| Ph.D. Bioinformatics (2009-2014): Bioinformatics Centre, Institute of Microbial Technology (IMTECH), Chandigarh under Jawaharlal Nehru University (JNU), New Delhi, India |
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M.Sc. Life Sciences (2007-2009): Jawaharlal Nehru University (JNU), New Delhi, India |
| B.Sc. Biotechnology (2004-2007): Jamia Millia Islamia (JMI), New Delhi, India |
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| Scientific Contributions/ Recognitions |
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Selected for SAKURA Exchange Program in Science for Indian Young officers, JAPAN (January 21-27, 2018) by Department of Biotechnology (DBT), India |
| Visiting scientist : Guangxi medical university and Guilin Medical University, China (October-November 2017). |
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Associate editor: PloS one and BMC Bioinformatics. |
| Editorial Board Member of Journal: Theoretical Biology and Medical Modelling. |
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Junior and Senior Research Fellowship (2009-2014) : Council of Scientific and Industrial Research (CSIR), New Delhi, India |
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GATE (Graduate Aptitude Test in Engineering) : Qualified in years 2008 and 2009 |
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| Research Interests |
- Identification and characterization of novel molecules in Plants
My research group is exploring several new areas in plant genome research including identification, characterization of novel molecules in plants. We are using Next Generation sequencing (NGS) datasets for the purpose, and our choice of molecules including Transfer RNA-derived fragments (tRFs), tRNA halves, ribosomal RNA-derived fragments (e.g. rRFs), and Fusion transcripts.
Transfer RNA-derived fragments (e.g. tRFs) in Plants
Transfer RNA-derived fragments or tRFs is a novel class of 15-28 nucleotide length small non-coding RNAs, generated by endonucleolytic cleavage of both mature and precursor tRNAs. Current understanding of the plant tRFs indicates that these small RNAs originate from 5' end (i.e. tRF-5) and 3' end (i.e. tRF-3) of mature tRNAs; and precursor tRNAs (i.e. tRF-1). In plants, there is an enhancement of tRFs generation in case of different biotic and abiotic stress conditions. Initial reports suggests that both Dicer-dependent and Dicer-independent pathways involved in tRFs generation. Reports also suggests that tRFs have role in both transcription and translation repression. In a nutshell, It is very important class of molecule to study in plants. Now, the key question is "when" and "where" to look for such events. We are trying to understand the role of tRFs, biogenesis and their mode of action.
Fusion transcripts in Plants
We are also exploring the area of fusion transcripts in plants. Chimeric transcripts may either act as long non-coding RNAs or encode novel chimeric proteins. Advancement in the high-throughput technologies has led to the accumulation of enormous sequencing data, which has eased the understanding of the molecular mechanism behind this complex event, and its implications are being attempted to be elucidated in eukaryotic organisms including plants. Negligible information available for the chimeric transcripts in plants. We have to understand their role, mode of generation and functional mechanism in plants.
- Development of webservers, databases and computational pipelines for plant research
We are analysing the Big Data generated by genomics and proteomics techniques, for plant research. It is also necessary to compile and share the information with scientific community. For all those purposes, we are developing in silico tools, web-servers, computational pipelines, and useful databases for plant research community. We are also using Machine Learning, Deep Learning and Artificial Intelligence to decipher the biology of different biomolecules.
It is very important to decipher the mechanism of generation and action of novel molecules for the plant genomics research. Here, we are performing different experiments to characterize and validate our in silico findings with the help of our colleagues. |
| Group Members |
| Shafaque Zahra | Ph.D. Student |
| Ajeet Singh | Ph.D. Student |
| A. T. Vivek | Ph.D. Student |
| Mohini Jaiswal | Ph.D. Student |
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Rohan Bhardwaj |
Project Associate-1 |
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Pragya Chitkara |
Project Associate-1 |
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Srija Chakraborty |
Junior Research Fellow (JRF) |
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| Web Resources, Databases and Tools (Developed/Contributed) |
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Publications
A) Corresponding Author / First Author Publications |
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Singh, A., Zahra S., Poddar N. and Kumar S.* Transfer RNA-derived non-coding RNAs (tncRNAs): hidden regulation of plants transcriptional regulatory circuits. Computational and Structural Biotechnology Journal. Volume 19, 2021, Pages 5278-5291. PMID: 34630945 |
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Bhardwaj R, Thakur JK* and Kumar S*. MedProDB: A database of Mediator proteins. Computational and Structural Biotechnology Journal Volume 19, 2021, Pages 4165-4176. PMID: 34527190 |
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Singh A, Vivek AT and Kumar S*.(2021) AlnC: An extensive database of long non-coding RNAs in angiosperms. PLoS ONE 16(4): e0247215. PMID: 33852582 |
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Vivek AT and Shailesh Kumar*. Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq. Briefings in Bioinformatics, Volume 22, Issue 4, July 2021, bbaa322, https://doi.org/10.1093/bib/bbaa322 PMID: 33333550 |
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Das D, Jaiswal M, Khan F N, Ahamad S and Kumar S*. PlantPepDB: A manually curated plant peptide database. Scientific reports 10:2194(2020) PMID: 32042035 |
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Vivek AT, Zahra S, and Kumar S*. From current knowledge to best practice: A primer on Viral diagnostics using deep sequencing of virus-derived small interfering RNAs (vsiRNAs) in infected plants. Methods; 2020 Nov 1;183:30-37. PMID: 31669354 |
| Singh, A., Zahra S., Das D. and Kumar S.* AtFusionDB: A Database of Fusion Transcripts in Arabidopsis thaliana.
Database (2018). Database (Oxford).Volume 2018 Jan 1. doi.org/10.1093/database/bay135. PMID: 30624648 |
| Gupta, N., Zahra, S., Singh. A. and Kumar S.* PVsiRNAdb: A Database for Plant Exclusive Viral-derived small interfering RNAs.
Database (2018). Database (Oxford).Volume 2018 Jan 1. doi.org/10.1093/database/bay105. PMID: 30307523 |
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Gupta, N., Singh, A., Zahra, S. and Kumar S.* PtRFdb: a database for plant transfer RNA-derived fragments. Database (2018). Database (Oxford). 2018 Jan 1;2018. doi: 10.1093/database/bay063 PMID: 29939244 |
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Kumar S, Razzaq SK, Vo AD, Gautam M, Li H: Identifying fusion transcripts using next generation sequencing. Wiley Interdisciplinary Reviews: RNA 2016, 7:811–823. (Cover Image) PMID: 27485475 |
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Kumar S, Vo AD, Qin F, Li H: Comparative assessment of methods for the fusion transcripts detection from RNA-Seq data. Scientific reports 2016, 6:21597. PMID: 26862001 |
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Kumar S, Vikram S, Raghava GPS: Genome Annotation of Burkholderia sp. SJ98 with Special Focus on Chemotaxis Genes. PLoS ONE 2013, 8:e70624. PMID: 23940608 |
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Kumar S, Kushwaha H, Bachhawat AK, Raghava GPS, Ganesan K: Genome sequence of the oleaginous red yeast Rhodosporidium toruloides MTCC 457. Eukaryotic cell 2012,11:1083–4. PMID: 22858828 |
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Kumar S, Randhawa A, Ganesan K, Raghava GPS, Mondal AK: Draft genome sequence of salt-tolerant yeast Debaryomyces hansenii var. hansenii MTCC 234. Eukaryotic cell 2012,11:961–2. PMID: 22744717 |
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Kumar S, Subramanian S, Raghava GPS, Pinnaka AK: Genome sequence of the marine bacterium Marinilabilia salmonicolor JCM 21150T. Journal of bacteriology 2012, 194:3746. PMID: 22740671 |
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Kumar S, Vikram S, Subramanian S, Raghava GPS, Pinnaka AK: Genome sequence of the halotolerant bacterium Imtechella halotolerans K1T. Journal of bacteriology 2012, 194:3731. PMID: 22740661 |
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Kumar S, Vikram S, Raghava GPS: Genome sequence of the nitroaromatic compounddegrading Bacterium Burkholderia sp. strain SJ98. Journal of bacteriology 2012, 194:3286. PMID: 22628512 |
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Vikram S, Kumar S, Subramanian S, Raghava GPS: Draft genome sequence of the nitrophenol-degrading actinomycete Rhodococcus imtechensis RKJ300. Journal of bacteriology 2012, 194:3543. PMID: 22689233 |
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Kumar S, Kaur N, Singh NK, Raghava GPS, Mayilraj S: Draft Genome Sequence of Streptomyces gancidicus Strain BKS 13-15. Genome announcements, 1:e0015013. PMID: 23599292 |
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Kumar S, Bala M, Raghava GPS, Mayilraj S: Draft Genome Sequence of Rhodococcus triatomae Strain BKS 15-14. Genome announcements, 1:e0012913. PMID: 23538907 |
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Kumar S, Kaur C, Kimura K, Takeo M, Raghava GPS, Mayilraj S: Draft Genome Sequence of the Type Species of the Genus Citrobacter, Citrobacter freundii MTCC 1658. Genome announcements 2013, 1. PMID: 23405287 |
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Bala M, Kumar S, Raghava GPS, Mayilraj S: Draft Genome Sequence of Rhodococcus ruber Strain BKS 20-38. Genome announcements, 1:e0013913. (Equal Contribution) PMID: 23558535 |
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Kaur N, Kumar S, Bala M, Raghava GPS, Mayilraj S: Draft Genome Sequence of Amycolatopsis decaplanina Strain DSM 44594T. Genome announcements, 1:e0013813. (Equal Contribution) PMID: 23558534 |
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Singh NK, Kumar S, Raghava GPS, Mayilraj S: Draft Genome Sequence of Acinetobacter baumannii Strain MSP4-16. Genome announcements, 1:e0013713. (Equal Contribution) PMID: 23558533 |
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Bala M, Kumar S, Raghava GPS, Mayilraj S: Draft Genome Sequence of Rhodococcus qingshengii Strain BKS 20-40. Genome announcements, 1:e0012813. (Equal Contribution) PMID: 23538906 |
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Vikram S, Kumar S, Vaidya B, Pinnaka AK, Raghava GPS: Draft Genome Sequence of the 2-Chloro-4-Nitrophenol-Degrading Bacterium Arthrobacter sp. Strain SJCon. Genome announcements, 1:e0005813. (Equal Contribution) PMID: 23516196 |
| Kaur N, Kumar S, Mayilraj S: Genome sequencing and annotation of Amycolatopsis vancoresmycina strain DSM 44592T. Genomics Data 2014, 2:16–17. (Equal Contribution) PMID: 26484057 |
| Kimura K, Kumar S, Takeo M, Mayilraj S: Genome sequencing, annotation of Citrobacter freundii strain GTC 09479. Genomics Data 2014, Dec; 2: 40–41 . (Equal Contribution) PMID: 26484065 |
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| B) As Collaborator / Contributor / Co-author |
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Raul B, Bhattacharjee O, Ghosh A, Upadhyay P, Tembhare K, Singh A, Shaheen T, Ghosh AK, Torres-Jerez I, Krom N, Clevenger J, Udvardi M, E Scheffler B, Ozias Akins P, Dutta Sharma R, Bandyopadhyay K, Gaur V, Kumar S, Sinharoy S. Microscopic and transcriptomic analyses of Dalbergoid legume peanut reveal a divergent evolution leading to Nod Factor dependent epidermal crack-entry and terminal bacteroid differentiation. Mol Plant Microbe Interact. 2021 Oct 23. doi: 10.1094/MPMI-05-21-0122-R. Epub ahead of print. PMID: 34689599 |
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Singh S, Qin F, Kumar S, Elfman J, Lin E, Pham L, Yang A and Li H (2019) The Landscape of Chimeric RNAs in Non-Diseased Tissues and Cells. Nucleic acids research 2020; 48(4):1764-1778 PMID: 31965184 |
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Wu P, Yang S, Singh S, Qin F, Kumar S, Wang L, Ma D and Li H: The Landscape and Implications of Chimeric RNAs in Cervical Cancer. EBioMedicine 2018 Oct 31. pii: S2352-3964(18)30477-8. doi: 10.1016/j.ebiom.2018.10.059. PMID: 30389505 |
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Huang R, Kumar S, Li H: Absence of Correlation between Chimeric RNA and Aging. Genes 2017, 8: 386. PMID: 29240691 |
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Xie Z, Babiceanu M, Kumar S, Jia Y, Qin F, Barr FG, Li H: Fusion transcriptome profiling provides insights into alveolar rhabdomyosarcoma. Proceedings of the National Academy of Sciences (PNAS) of the United States of America 2016, 113:13126–13131. PMID: 27799565 |
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Dhanda SK, Vir P, Singla D, Gupta S, Kumar S, Raghava GPS: A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. PLOS ONE 2016, 11:e0153771. PMID: 27096425 |
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Babiceanu M, Qin F, Xie Z, Jia Y, Lopez K, Janus N, Facemire L, Kumar S, Pang Y, Qi Y, Lazar IM, Li H: Recurrent chimeric fusion RNAs in non-cancer tissues and cells. Nucleic acids research 2016, 44:2859–72. PMID: 26837576 |
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Gupta S, Chaudhary K, Dhanda SK, Kumar R, Kumar S, Sehgal M, Nagpal G, Raghava GPS: A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer. PLOS ONE 2016, 11:e0166372. PMID: 27832200 |
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Nagpal G, Sharma M, Kumar S, Chaudhary K, Gupta S, Gautam A, Raghava GPS: PCMdb: Pancreatic Cancer Methylation Database. Scientific reports 2014, 4:4197. PMID: 24569397 |
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Kumar R, Chaudhary K, Gupta S, Singh H, Kumar S, Gautam A, Kapoor P, Raghava GPS: CancerDR: cancer drug resistance database. Scientific reports 2013, 3:1445. PMID: 23486013 |
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Vikram S, Pandey J, Kumar S, Raghava GPS: Genes involved in degradation of paranitrophenol are differentially arranged in form of non-contiguous gene clusters in Burkholderia sp. strain SJ98. PloS one 2013, 8:e84766. PMID: 24376843 |
| Singh SV, Kumar N, Singh SN, Bhattacharya T, Sohal JS, Singh PK, Singh AV, Singh B, Chaubey KK, Gupta S, Sharma N, Kumar S, Raghava GPS: Genome Sequence of the “Indian Bison Type” Biotype of Mycobacterium avium subsp. paratuberculosis Strain S5. Genome announcements 2013, 1. PMID: 23469332 |
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| C) Book Chapters |
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Narayan A, Singh A, and Kumar S.* Understanding Microbiome Science through Big Data analysis. (eds). In: Singh S. (eds). Metagenomics Systems Biology. Springer, Singapore. https://doi.org/10.1007/978-981-15-8562-3_3
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Vivek A.T., and Kumar S.* (2020). Genomics approaches in Plant Stress Research. In. Khan, Singh, and Poor (eds). Improving Abiotic Stress Tolerance in Plants. CRC Press/Taylor & Francis Group DOI: 10.1201/9780429027505-16. |
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Jaiswal M, Zahra S and Kumar S.* (2020). Bioinformatics tools for Epitope Prediction. In: Singh S. (eds). Systems and Synthetic Immunology. Springer, Singapore https://doi.org/10.1007/978-981-15-3350-1_4 |
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Singh A and Kumar S.* (2019). Study of Plant Exclusive Virus-Derived Small Interfering RNAs. In: Kumar S., Egbuna C. (eds) Phytochemistry: An in-silico and in-vitro Update. Springer,Singapore. https://doi.org/10.1007/978-981-13-6920-9_29. |
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Zahra S and Kumar S.* (2019). PtRFdb: Plant tRNA-Derived Fragments Database. In: Kumar S., Egbuna C. (eds) Phytochemistry: An in-silico and in-vitro Update. Springer,Singapore. https://doi.org/10.1007/978-981-13-6920-9_27. |
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Singh A, Zahra S, and Kumar S.* (2019). In-Silico Tools in Phytochemical Research. In: Kumar S., Egbuna C. (eds) Phytochemistry: An in-silico and in-vitro Update. Springer,Singapore. https://doi.org/10.1007/978-981-13-6920-9_19. |
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Narayan A, Singh A, and Kumar S.* (2019). Protein Homology Modeling in Phytochemical Research. In: Kumar S., Egbuna C. (eds) Phytochemistry: An in-silico and in-vitro Update. Springer, Singapore. https://doi.org/10.1007/978-981-13-6920-9_24. |
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Zahra S, Singh A and Kumar S.* (2018). Synthetic Probes, Their Applications and Designing. In: Singh S. (eds) Synthetic Biology (Omics Tools and Their Applications). Springer, Singapore https://doi.org/10.1007/978-981-10-8693-9_11.
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Kumar S and Li H. (2017) In silico designing of Anticancer Peptides. In: Lazar I., Kontoyianni M., Lazar A. (eds) Proteomics for Drug Discovery. Methods in Molecular Biology, vol 1647. Humana Press, New York, NY https://doi.org/10.1007/978-1-4939-7201-2_17.
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My citation : https://scholar.google.com/citations?user=6sFjjocAAAAJ&hl=en&authuser=1 |
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