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Your search keyword '"Mohan A. V. S. K. Katta"' showing total 4 results
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4 results on '"Mohan A. V. S. K. Katta"'

1. NGS-QCbox and Raspberry for Parallel, Automated and Rapid Quality Control Analysis of Large-Scale Next Generation Sequencing (Illumina) Data.

Author

Mohan A V S K Katta, Aamir W Khan, Dadakhalandar Doddamani, Mahendar Thudi, and Rajeev K Varshney

Subjects
Medicine, Science
Abstract

Rapid popularity and adaptation of next generation sequencing (NGS) approaches have generated huge volumes of data. High throughput platforms like Illumina HiSeq produce terabytes of raw data that requires quick processing. Quality control of the data is an important component prior to the downstream analyses. To address these issues, we have developed a quality control pipeline, NGS-QCbox that scales up to process hundreds or thousands of samples. Raspberry is an in-house tool, developed in C language utilizing HTSlib (v1.2.1) (http://htslib.org), for computing read/base level statistics. It can be used as stand-alone application and can process both compressed and uncompressed FASTQ format files. NGS-QCbox integrates Raspberry with other open-source tools for alignment (Bowtie2), SNP calling (SAMtools) and other utilities (bedtools) towards analyzing raw NGS data at higher efficiency and in high-throughput manner. The pipeline implements batch processing of jobs using Bpipe (https://github.com/ssadedin/bpipe) in parallel and internally, a fine grained task parallelization utilizing OpenMP. It reports read and base statistics along with genome coverage and variants in a user friendly format. The pipeline developed presents a simple menu driven interface and can be used in either quick or complete mode. In addition, the pipeline in quick mode outperforms in speed against other similar existing QC pipeline/tools. The NGS-QCbox pipeline, Raspberry tool and associated scripts are made available at the URL https://github.com/CEG-ICRISAT/NGS-QCbox and https://github.com/CEG-ICRISAT/Raspberry for rapid quality control analysis of large-scale next generation sequencing (Illumina) data.

Published
2015
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2. Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments

Author

Jun Wang, Katrien M. Devos, Jianping Wang, Andrew H. Paterson, Cédric Mariac, Xinming Liang, Wenbin Chen, Dadakhalandar Doddamani, Sunil Gupta, Om Parkash Yadav, Rattan Yadav, Eric Lyons, Rajeev K. Varshney, Arindam Ghatak, Wolfram Weckwerth, C. Tom Hash, He Zhang, Mame Codou Gueye, Dev Paudel, Abhishek Rathore, Palak Chaturvedi, R. S. Mahala, Christian Dupuy, Xun Xu, Jeffrey L. Bennetzen, Guangyi Fan, Swapan K. Datta, Yusheng Zhao, Somashekhar Punnuri, Annapurna Chitikineni, Ndjido Ardo Kane, Mohan A. V. S. K. Katta, Falalou Hamidou, Francesca Sparvoli, Jason G. Wallace, Mahendar Thudi, Edward S. Buckler, Joann A. Conner, Prasad Bajaj, Xiyin Wang, Ramanjulu Sunkar, Peggy Ozias-Akins, Marie Couderc, Bharat P. Singh, Trilochan Mohapatra, Stefania Grando, Xin Liu, Bénédicte Rhoné, Vanika Garg, K. D. Mungra, Hari D. Upadhyaya, Chengcheng Shi, Peng Qi, Philippe Cubry, Yong Jiang, Hao Wang, Karen R. Harris-Shultz, Sabarinath Subramaniam, Yves Vigouroux, Jérémy Clotault, Cécile Berthouly-Salazar, Shifeng Cheng, Jochen C. Reif, Neetin Desai, Rakesh K. Srivastava, Institut des Mondes Africains (IMAF), Université Paris 1 Panthéon-Sorbonne (UP1)-Institut de Recherche pour le Développement (IRD)-École des hautes études en sciences sociales (EHESS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), ANR-13-BSV7-0017,AfriCrop,Etude de l'histoire évolutive des plantes domestiquées africaines(2013), Diversité, adaptation, développement des plantes (UMR DIADE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Numerical modeling and high performance computing for evolution problems in complex domains and heterogeneous media (NACHOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (JAD), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Institut Sénégalais de Recherches Agricoles [Dakar] (ISRA), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Recherches Forestières Méditerranéennes (URFM), Institut National de la Recherche Agronomique (INRA), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université Paris 1 Panthéon-Sorbonne (UP1)-Institut de Recherche pour le Développement (IRD)-École des hautes études en sciences sociales (EHESS)-École pratique des hautes études (EPHE), Unité de Mécanique (UME), École Nationale Supérieure de Techniques Avancées (ENSTA Paris), State Key Laboratory of Fine Chemicals, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université d'Angers (UA)-AGROCAMPUS OUEST-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Panthéon-Sorbonne (UP1)-Institut de Recherche pour le Développement (IRD)-École des hautes études en sciences sociales (EHESS)-École pratique des hautes études (EPHE)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, genome sequence, [SDV]Life Sciences [q-bio], adaptation, Applied Microbiology and Biotechnology, hybridism, Conserved Sequence, 2. Zero hunger, Agriculture, Staple food, genetic diversity, Genomics, hybridations, Phenotype, diversité génétique, pennisetum glaucum, Molecular Medicine, Desert Climate, pearl millet, Genome, Plant, Biotechnology, Agricultural genetics, Pennisetum, [SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory, Drought tolerance, Biomedical Engineering, Bioengineering, Biology, Genes, Plant, milieu aride, Article, domestication, 03 medical and health sciences, Quantitative Trait, Heritable, Mil (Cenchrus americanus), Genetic variation, Plant breeding, Domestication, Millet (Cenchrus americanus), Genetic diversity, Base Sequence, Genetic Variation, Molecular Sequence Annotation, 15. Life on land, Arid, arid environment, Plant Breeding, 030104 developmental biology, Agronomy, Next-generation sequencing, Hybridization, Genetic, Genome-Wide Association Study
Abstract

Draft genome, 994 re-sequenced lines and GWAS for yield-traits provide a resource of genetics and genomics tools for pearl millet researchers and breeders. Supplementary information The online version of this article (doi:10.1038/nbt.3943) contains supplementary material, which is available to authorized users., Pearl millet [Cenchrus americanus (L.) Morrone] is a staple food for more than 90 million farmers in arid and semi-arid regions of sub-Saharan Africa, India and South Asia. We report the ∼1.79 Gb draft whole genome sequence of reference genotype Tift 23D2B1-P1-P5, which contains an estimated 38,579 genes. We highlight the substantial enrichment for wax biosynthesis genes, which may contribute to heat and drought tolerance in this crop. We resequenced and analyzed 994 pearl millet lines, enabling insights into population structure, genetic diversity and domestication. We use these resequencing data to establish marker trait associations for genomic selection, to define heterotic pools, and to predict hybrid performance. We believe that these resources should empower researchers and breeders to improve this important staple crop. Supplementary information The online version of this article (doi:10.1038/nbt.3943) contains supplementary material, which is available to authorized users.

Published
2017
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3. Prioritization of candidate genes in 'QTL-hotspot' region for drought tolerance in chickpea (Cicer arietinum L.)

Author

Sandip M. Kale, Jacqueline Batley, Henry T. Nguyen, Mohan A. V. S. K. Katta, Deepa Jaganathan, Pooran M. Gaur, Charles Chen, Vanika Garg, Dadakhalandar Doddamani, Tim Sutton, Pradeep Ruperao, P. B. Kavi Kishor, Mahendar Thudi, Himabindu Kudapa, Manish Roorkiwal, David Edwards, Rajeev K. Varshney, and Ramu Punna

Subjects
Candidate gene, Genotype, Genetic Linkage, Quantitative Trait Loci, Drought tolerance, Population, Adaptation, Biological, Genomics, Single-nucleotide polymorphism, Quantitative trait locus, Biology, Genes, Plant, Polymorphism, Single Nucleotide, Article, Chromosome Breakpoints, Quantitative Trait, Heritable, Gene Expression Regulation, Plant, Stress, Physiological, Genetic linkage, Inbreeding, education, Recombination, Genetic, Genetics, education.field_of_study, Multidisciplinary, Chromosome Mapping, High-Throughput Nucleotide Sequencing, Reproducibility of Results, food and beverages, Cicer, Droughts, Genetic marker, Genome, Plant, Genome-Wide Association Study
Abstract

A combination of two approaches, namely QTL analysis and gene enrichment analysis were used to identify candidate genes in the “QTL-hotspot” region for drought tolerance present on the Ca4 pseudomolecule in chickpea. In the first approach, a high-density bin map was developed using 53,223 single nucleotide polymorphisms (SNPs) identified in the recombinant inbred line (RIL) population of ICC 4958 (drought tolerant) and ICC 1882 (drought sensitive) cross. QTL analysis using recombination bins as markers along with the phenotyping data for 17 drought tolerance related traits obtained over 1–5 seasons and 1–5 locations split the “QTL-hotspot” region into two subregions namely “QTL-hotspot_a” (15 genes) and “QTL-hotspot_b” (11 genes). In the second approach, gene enrichment analysis using significant marker trait associations based on SNPs from the Ca4 pseudomolecule with the above mentioned phenotyping data and the candidate genes from the refined “QTL-hotspot” region showed enrichment for 23 genes. Twelve genes were found common in both approaches. Functional validation using quantitative real-time PCR (qRT-PCR) indicated four promising candidate genes having functional implications on the effect of “QTL-hotspot” for drought tolerance in chickpea.

Published
2015
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4. NGS-QCbox and Raspberry for Parallel, Automated and Rapid Quality Control Analysis of Large-Scale Next Generation Sequencing (Illumina) Data

Author

Dadakhalandar Doddamani, Aamir W. Khan, Mohan A. V. S. K. Katta, Rajeev K. Varshney, and Mahendar Thudi

Subjects
Physics, FASTQ format, Internet, Multidisciplinary, business.industry, Interface (computing), lcsh:R, High-Throughput Nucleotide Sequencing, lcsh:Medicine, Bioinformatics, computer.software_genre, Pipeline (software), Uncompressed video, Task (computing), Software, Operating system, Batch processing, lcsh:Q, lcsh:Science, business, Sequence Alignment, Throughput (business), computer, Research Article
Abstract

Rapid popularity and adaptation of next generation sequencing (NGS) approaches have generated huge volumes of data. High throughput platforms like Illumina HiSeq produce terabytes of raw data that requires quick processing. Quality control of the data is an important component prior to the downstream analyses. To address these issues, we have developed a quality control pipeline, NGS-QCbox that scales up to process hundreds or thousands of samples. Raspberry is an in-house tool, developed in C language utilizing HTSlib (v1.2.1) (http://htslib.org), for computing read/base level statistics. It can be used as stand-alone application and can process both compressed and uncompressed FASTQ format files. NGS-QCbox integrates Raspberry with other open-source tools for alignment (Bowtie2), SNP calling (SAMtools) and other utilities (bedtools) towards analyzing raw NGS data at higher efficiency and in high-throughput manner. The pipeline implements batch processing of jobs using Bpipe (https://github.com/ssadedin/bpipe) in parallel and internally, a fine grained task parallelization utilizing OpenMP. It reports read and base statistics along with genome coverage and variants in a user friendly format. The pipeline developed presents a simple menu driven interface and can be used in either quick or complete mode. In addition, the pipeline in quick mode outperforms in speed against other similar existing QC pipeline/tools. The NGS-QCbox pipeline, Raspberry tool and associated scripts are made available at the URL https://github.com/CEG-ICRISAT/NGS-QCbox and https://github.com/CEG-ICRISAT/Raspberry for rapid quality control analysis of large-scale next generation sequencing (Illumina) data.

Published
2015
Full Text
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