Vijayaraj Nagarajan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Vijayaraj Nagarajan has a total of 25 co-authors. Here are the top co-authors by number of shared publications:
Andrei Gabrielian
Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, NIH , Bethesda, MD , USA
1 shared publicationAndrew J. Oler
Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, NIH , Bethesda, MD , USA
1 shared publication- Bruce L. Henschen
Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA
1 shared publication Darrell Hurt
Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, NIH , Bethesda, MD , USA
1 shared publicationGerman Grekhov
Unipro Center for Information Technologies , Novosibirsk , Russia
1 shared publication- Gil McVean
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK; Ellison Institute of Technology Oxford, Oxford OX4 4GE, UK
1 shared publication
Latest Publications
- Open Access
Unipro UGENE NGS pipelines and components for variant calling, RNA-seq and ChIP-seq data analyses
The advent of Next Generation Sequencing (NGS) technologies has opened new possibilities for researchers. However, the more biology becomes a data-intensive field, the more biologists have to learn how to process and analyze NGS data with complex computational tools. Even with the availability of common pipeline specifications, it is often a time-consuming and cumbersome task for a bench scientist to install and configure the pipeline tools. We believe that a unified, desktop and biologist-friendly front end to NGS data analysis tools will substantially improve productivity in this field. Here we present NGS pipelines "Variant Calling with SAMtools", "Tuxedo Pipeline for RNA-seq Data Analysis" and "Cistrome Pipeline for ChIP-seq Data Analysis" integrated into the Unipro UGENE desktop toolkit. We describe the available UGENE infrastructure that helps researchers run these pipelines on different datasets, store and investigate the results and re-run the pipelines with the same parameters. These pipeline tools are included in the UGENE NGS package. Individual blocks of these pipelines are also available for expert users to create their own advanced workflows.
Open AccessUnipro UGENE NGS pipelines and components for variant calling, RNA-seq and ChIP-seq data analyses
The advent of Next Generation Sequencing (NGS) technologies has opened new possibilities for researchers. However, the more biology becomes a data-intensive field, the more biologists have to learn how to process and analyze NGS data with complex computational tools. Even with the availability of common pipeline specifications, it is often a time-consuming and cumbersome task for a bench scientist to install and configure the pipeline tools. We believe that a unified, desktop and biologist-friendly front end to NGS data analysis tools will substantially improve productivity in this field. Here we present NGS pipelines "Variant Calling with SAMtools", "Tuxedo Pipeline for RNA-seq Data Analysis" and "Cistrome Pipeline for ChIP-seq Data Analysis" integrated into the Unipro UGENE desktop toolkit. We describe the available UGENE infrastructure that helps researchers run these pipelines on different datasets, store and investigate the results and re-run the pipelines with the same parameters. These pipeline tools are included in the UGENE NGS package. Individual blocks of these pipelines are also available for expert users to create their own advanced workflows.
- Open Access
- 4 April 2011
- Published by Springer Nature in Genome Biology
- Vol. 12 (4) , 1-15
High recombination rates and hotspots in a Plasmodium falciparum genetic cross
The human malaria parasite Plasmodium falciparum survives pressures from the host immune system and antimalarial drugs by modifying its genome. Genetic recombination and nucleotide substitution are the two major mechanisms that the parasite employs to generate genome diversity. A better understanding of these mechanisms may provide important information for studying parasite evolution, immune evasion and drug resistance. Here, we used a high-density tiling array to estimate the genetic recombination rate among 32 progeny of a P. falciparum genetic cross (7G8 × GB4). We detected 638 recombination events and constructed a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb per centimorgan and identified 54 candidate recombination hotspots. Similar to centromeres in other organisms, the sequences of P. falciparum centromeres are found in chromosome regions largely devoid of recombination activity. Motifs enriched in hotspots were also identified, including a 12-bp G/C-rich motif with 3-bp periodicity that may interact with a protein containing 11 predicted zinc finger arrays. These results show that the P. falciparum genome has a high recombination rate, although it also follows the overall rule of meiosis in eukaryotes with an average of approximately one crossover per chromosome per meiosis. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination rate observed. The lack of recombination activity in centromeric regions is consistent with the observations of reduced recombination near the centromeres of other organisms.
- 4 April 2011
- Published by Springer Nature in Genome Biology
- Vol. 12 (4) , 1-15
Open AccessHigh recombination rates and hotspots in a Plasmodium falciparum genetic cross
The human malaria parasite Plasmodium falciparum survives pressures from the host immune system and antimalarial drugs by modifying its genome. Genetic recombination and nucleotide substitution are the two major mechanisms that the parasite employs to generate genome diversity. A better understanding of these mechanisms may provide important information for studying parasite evolution, immune evasion and drug resistance. Here, we used a high-density tiling array to estimate the genetic recombination rate among 32 progeny of a P. falciparum genetic cross (7G8 × GB4). We detected 638 recombination events and constructed a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb per centimorgan and identified 54 candidate recombination hotspots. Similar to centromeres in other organisms, the sequences of P. falciparum centromeres are found in chromosome regions largely devoid of recombination activity. Motifs enriched in hotspots were also identified, including a 12-bp G/C-rich motif with 3-bp periodicity that may interact with a protein containing 11 predicted zinc finger arrays. These results show that the P. falciparum genome has a high recombination rate, although it also follows the overall rule of meiosis in eukaryotes with an average of approximately one crossover per chromosome per meiosis. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination rate observed. The lack of recombination activity in centromeric regions is consistent with the observations of reduced recombination near the centromeres of other organisms.