Mr. Ashok Kumar Sharma - Indian Institute of Science Education and Research, Bhopal

Mr. Ashok Kumar Sharma

Indian Institute of Science Education and Research, Bhopal

Bhopal, India, Madhya Pradesh | India

Mr. Ashok Kumar Sharma - Indian Institute of Science Education and Research, Bhopal

Mr. Ashok Kumar Sharma

Introduction

Ashok Kumar Sharma is currently a Ph.D. candidate in biological sciences at IISERB. Ashok completed his graduation with a B. Pham. degree in pharmaceutical sciences from Dr. H. S. Gour Central University in 2010 and obtained an M.S. degree from NIPER Mohali in 2012.
Ashok's general research area is development of computational tools for large scale data analysis. His research interests includes to find out the interaction between xenobiotics and human gut microbes. He has developed computational tools such as woods and 16s classifier for the functional annotation and taxonomic classification of metagenomic data. Right now he is developing a computational approach to find out the xenobiotics metabolism solely by human gut microbes.
Ashok has authored in papers on the computational tools for the large scale data analysis. Ashok has qualified various national level examinations such as GPAT, NIPER, GATE and CSIR and he is a winner of Ministry of Chemicals and Fertilizers fellowship for pursuing the masters in pharmaceutical sciences. Recently he has received a travel grant to attend a conference on human gut microbiome at EMBL, Germany.

Primary Affiliation: Indian Institute of Science Education and Research, Bhopal - Bhopal, India, Madhya Pradesh , India

Research Interests:


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Publications

12Publications

102Reads

3Profile Views

16PubMed Central Citations

Gut microbiome composition of wild western lowland gorillas is associated with individual age and sex factors.

Am J Phys Anthropol 2019 Jul 25;169(3):575-585. Epub 2019 Apr 25.

Department of Animal Science, University of Minnesota Twin Cities, St. Paul, Minnesota.

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https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23842
Publisher Site
http://dx.doi.org/10.1002/ajpa.23842DOI Listing
July 2019
8 Reads
2.379 Impact Factor

Mechanistic elucidation of amphetamine metabolism by tyramine oxidase from human gut microbiota using molecular dynamics simulations.

J Cell Biochem 2019 Jan 30. Epub 2019 Jan 30.

Department of Biological Sciences, Metagenomics and Systems Biology Laboratory, Indian Institute of Science Education and Research Bhopal, Bhopal, India.

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http://dx.doi.org/10.1002/jcb.28396DOI Listing
January 2019
3.263 Impact Factor

Genome Sequence of Peacock Reveals the Peculiar Case of a Glittering Bird.

Front Genet 2018 19;9:392. Epub 2018 Sep 19.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India.

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https://www.frontiersin.org/article/10.3389/fgene.2018.00392
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http://dx.doi.org/10.3389/fgene.2018.00392DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156156PMC
September 2018
4 Reads

A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota

Scientific Reportsvolume 7, Article number: 9751

Nature Scientific Reports

The human gut microbiota is constituted of a diverse group of microbial species harbouring an enormous metabolic potential, which can alter the metabolism of orally administered drugs leading to individual/population-specific differences in drug responses. Considering the large heterogeneous pool of human gut bacteria and their metabolic enzymes, investigation of species-specific contribution to xenobiotic/drug metabolism by experimental studies is a challenging task. Therefore, we have developed a novel computational approach to predict the metabolic enzymes and gut bacterial species, which can potentially carry out the biotransformation of a xenobiotic/drug molecule. A substrate database was constructed for metabolic enzymes from 491 available human gut bacteria. The structural properties (fingerprints) from these substrates were extracted and used for the development of random forest models, which displayed average accuracies of up to 98.61% and 93.25% on cross-validation and blind set, respectively. After the prediction of EC subclass, the specific metabolic enzyme (EC) is identified using a molecular similarity search. The performance was further evaluated on an independent set of FDA-approved drugs and other clinically important molecules. To our knowledge, this is the only available approach implemented as ‘DrugBug’ tool for the prediction of xenobiotic/drug metabolism by metabolic enzymes of human gut microbiota.

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April 2017
1 Read

Prediction of anti-inflammatory proteins/peptides: an insilico approach.

J Transl Med 2017 01 6;15(1). Epub 2017 Jan 6.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, India.

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http://dx.doi.org/10.1186/s12967-016-1103-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5216551PMC
January 2017
23 Reads
3 Citations
3.930 Impact Factor

Prediction of Biofilm Inhibiting Peptides: An In silico Approach.

Front Microbiol 2016 16;7:949. Epub 2016 Jun 16.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal Bhopal, India.

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http://dx.doi.org/10.3389/fmicb.2016.00949DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4909740PMC
July 2016
14 Reads
3 Citations
3.941 Impact Factor

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.

J Transl Med 2016 06 14;14(1):178. Epub 2016 Jun 14.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, Madhya Pradesh, India.

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http://dx.doi.org/10.1186/s12967-016-0928-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908730PMC
June 2016
8 Reads
4 Citations
3.930 Impact Factor

Reconstruction of Bacterial and Viral Genomes from Multiple Metagenomes.

Front Microbiol 2016 12;7:469. Epub 2016 Apr 12.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, India.

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http://dx.doi.org/10.3389/fmicb.2016.00469DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4828583PMC
May 2016
18 Reads
5 Citations
3.941 Impact Factor

Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins.

BMC Genomics 2016 05 27;17:411. Epub 2016 May 27.

Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, Bhopal, 462066, India.

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http://dx.doi.org/10.1186/s12864-016-2753-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4882796PMC
May 2016
18 Reads
1 Citation
3.990 Impact Factor

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

Genomics

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier ‘Woods’ by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and > 97% on two real metagenomic datasets. In addition, it performed > 87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

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July 2015
4 Reads

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

PLOS ONE

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.

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February 2015
6 Reads

Top co-authors

Sudheer Gupta
Sudheer Gupta

CSIR-Institute of Microbial Technology

3
Shubham K Jaiswal
Shubham K Jaiswal

Metagenomics and Systems Biology Laboratory

3
Klara J Petrzelkova
Klara J Petrzelkova

Oita University

2
Andres Gomez
Andres Gomez

Mayo Clinic

2
Midhun K Madhu
Midhun K Madhu

Indian Institute of Science Education and Research Bhopal

2
Ankit Gupta
Ankit Gupta

University of Massachusetts Medical School

2
Sanjiv Kumar
Sanjiv Kumar

Vascular Biology Center

2