ITagPlot: An accurate computation and interactive drawing tool for tag density plot

Sung Hwan Kim, Onyeka Ezenwoye, Hwan Gue Cho, Keith D Robertson, Jeong Hyeon Choi

Research output: Contribution to journalArticle

Abstract

Motivation: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. Results: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering.

Original languageEnglish (US)
Pages (from-to)2384-2387
Number of pages4
JournalBioinformatics
Volume31
Issue number14
DOIs
StatePublished - 2015

Fingerprint

Biological Phenomena
Graphical user interfaces
Cluster Analysis
Statistics
Engines
Unsupervised Clustering
Graphical User Interface
Density Functional
Sequencing
Engine
Grid
Drawing

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

Cite this

ITagPlot : An accurate computation and interactive drawing tool for tag density plot. / Kim, Sung Hwan; Ezenwoye, Onyeka; Cho, Hwan Gue; Robertson, Keith D; Choi, Jeong Hyeon.

In: Bioinformatics, Vol. 31, No. 14, 2015, p. 2384-2387.

Research output: Contribution to journalArticle

Kim, Sung Hwan ; Ezenwoye, Onyeka ; Cho, Hwan Gue ; Robertson, Keith D ; Choi, Jeong Hyeon. / ITagPlot : An accurate computation and interactive drawing tool for tag density plot. In: Bioinformatics. 2015 ; Vol. 31, No. 14. pp. 2384-2387.
@article{ab982655b7d24821a8f5afb5408e0558,
title = "ITagPlot: An accurate computation and interactive drawing tool for tag density plot",
abstract = "Motivation: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. Results: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering.",
author = "Kim, {Sung Hwan} and Onyeka Ezenwoye and Cho, {Hwan Gue} and Robertson, {Keith D} and Choi, {Jeong Hyeon}",
year = "2015",
doi = "10.1093/bioinformatics/btv166",
language = "English (US)",
volume = "31",
pages = "2384--2387",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "14",

}

TY - JOUR

T1 - ITagPlot

T2 - An accurate computation and interactive drawing tool for tag density plot

AU - Kim, Sung Hwan

AU - Ezenwoye, Onyeka

AU - Cho, Hwan Gue

AU - Robertson, Keith D

AU - Choi, Jeong Hyeon

PY - 2015

Y1 - 2015

N2 - Motivation: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. Results: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering.

AB - Motivation: Tag density plots are very important to intuitively reveal biological phenomena from capture-based sequencing data by visualizing the normalized read depth in a region. Results: We have developed iTagPlot to compute tag density across functional features in parallel using multicores and a grid engine and to interactively explore it in a graphical user interface. It allows us to stratify features by defining groups based on biological function and measurement, summary statistics and unsupervised clustering.

UR - http://www.scopus.com/inward/record.url?scp=84941642147&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84941642147&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btv166

DO - 10.1093/bioinformatics/btv166

M3 - Article

C2 - 25792550

AN - SCOPUS:84941642147

VL - 31

SP - 2384

EP - 2387

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 14

ER -