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

By Li Shen, Ning-Yi Shao, Xiaochuan Liu, E. Nestler. BMC Genomics, 2014

865 citations recorded by the source corpus.

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. 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. 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1b092b12ac66363d7d60f314aaacb861fd675bde

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

  1. Li Zhan
  2. Meijun Chen
  3. Tianzhi Wu
  4. Erqiang Hu
  5. Shuangbing Xu
  6. Lin Li
  7. Zijing Xie
  8. Qianwen Wang

Citations in the corpus