ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases View Full Text


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Article Info

DATE

2014-04-15

AUTHORS

Li Shen, Ningyi Shao, Xiaochuan Liu, Eric Nestler

ABSTRACT

BackgroundUnderstanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.ResultsWe have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.ConclusionsWe conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data. More... »

PAGES

284

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    47 schema:description BackgroundUnderstanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge.ResultsWe have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready.ConclusionsWe conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.
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    55 DNA elements
    56 DNA sequencing
    57 DNA-interacting proteins
    58 NG
    59 ResultsWe
    60 amount
    61 amount of data
    62 big sequencing data
    63 challenges
    64 conjunction
    65 considerable challenge
    66 cost
    67 data
    68 database
    69 dataset
    70 diverse phenotypes
    71 elements
    72 enrichment patterns
    73 era
    74 example
    75 expression
    76 figures
    77 functional DNA elements
    78 gap
    79 gene expression
    80 genome
    81 genome-wide scale
    82 genomic databases
    83 genomic information
    84 important regions
    85 information
    86 interaction
    87 interpretation
    88 mammalian genomes
    89 massive datasets
    90 millions
    91 mining
    92 next-generation sequencing data
    93 next-generation sequencing technologies
    94 patterns
    95 phenotype
    96 plots
    97 program
    98 protein
    99 protein regulators
    100 protein-DNA interactions
    101 publications
    102 quick mining
    103 region
    104 regulator
    105 relationship
    106 scale
    107 sequencing
    108 sequencing data
    109 sequencing technologies
    110 standalone program
    111 technology
    112 tool
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