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The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

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

Open access
Shen, Li, Shao, Ningyi, Liu, Xiaochuan, Nestler, Eric·2014·BMC Genomics

Abstract

BACKGROUND: Understanding 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. RESULTS: We 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. CONCLUSIONS: We 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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Metadata

Authors
Shen, L., Shao, N., Liu, X., & Nestler, E.
Venue
BMC Genomics
Published
2014
Volume
15
Issue
1
Pages
284
ISSN
1471-2164 (ISSN-L)
OpenAlex
W2040909468
Cited by
887
References
21
FWCI
24.46
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases. https://app.cochat.ai/papers/10.1186/1471-2164-15-284
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Shen, L., Shao, N., Liu, X., & Nestler, E. (2014). ngs.plot: Quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics. https://doi.org/10.1186/1471-2164-15-284

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