GeneTrack - A genomic data processing and visualization framework

Istvan Albert; Shinichiro Wachi; Cizhong Jiang; B. Franklin Pugh

doi:10.1093/bioinformatics/btn119

GeneTrack - A genomic data processing and visualization framework

Istvan Albert
, Shinichiro Wachi
, Cizhong Jiang
, B. Franklin Pugh

Research output: Contribution to journal › Article › peer-review

82 Scopus citations

Abstract

Motivation: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com.

Original language	English (US)
Pages (from-to)	1305-1306
Number of pages	2
Journal	Bioinformatics
Volume	24
Issue number	10
DOIs	https://doi.org/10.1093/bioinformatics/btn119
State	Published - May 2008

All Science Journal Classification (ASJC) codes

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

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10.1093/bioinformatics/btn119

Cite this

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title = "GeneTrack - A genomic data processing and visualization framework",

abstract = "Motivation: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com.",

author = "Istvan Albert and Shinichiro Wachi and Cizhong Jiang and Pugh, \{B. Franklin\}",

note = "Funding Information: The software was designed with extensibility in mind. There is a clear separation of the database schemas, parsing, fitting and prediction modules, to the extent that the schema or prediction algorithm that is to be invoked in a certain analysis run can be changed via the configuration file. Similarly, the output tracks and graphs are fully customizable and may be entirely replaced although this requires Python expertise. The currently distributed schemas are for sequencing data, but we are preparing a set of modules to streamline tiling array data processing. We are committed to providing a smooth data exchange with other existing data analysis and visualization platforms provided by UCSC and Ensemble. To that end we have implemented export functionality that produces results in BED, GFF or wiggle format. The software has been tested on Windows and Linux platforms and is believed to work on all major operating systems that can run Python and its extension libraries for HDF and Numerical Python. We maintain several GeneTrack instances to disseminate our results (see http:// atlas.bx.psu.edu). Funding for the project has been provided by NIH R01-HG004160.",

year = "2008",

month = may,

doi = "10.1093/bioinformatics/btn119",

language = "English (US)",

volume = "24",

pages = "1305--1306",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "10",

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TY - JOUR

T1 - GeneTrack - A genomic data processing and visualization framework

AU - Albert, Istvan

AU - Wachi, Shinichiro

AU - Jiang, Cizhong

AU - Pugh, B. Franklin

N1 - Funding Information: The software was designed with extensibility in mind. There is a clear separation of the database schemas, parsing, fitting and prediction modules, to the extent that the schema or prediction algorithm that is to be invoked in a certain analysis run can be changed via the configuration file. Similarly, the output tracks and graphs are fully customizable and may be entirely replaced although this requires Python expertise. The currently distributed schemas are for sequencing data, but we are preparing a set of modules to streamline tiling array data processing. We are committed to providing a smooth data exchange with other existing data analysis and visualization platforms provided by UCSC and Ensemble. To that end we have implemented export functionality that produces results in BED, GFF or wiggle format. The software has been tested on Windows and Linux platforms and is believed to work on all major operating systems that can run Python and its extension libraries for HDF and Numerical Python. We maintain several GeneTrack instances to disseminate our results (see http:// atlas.bx.psu.edu). Funding for the project has been provided by NIH R01-HG004160.

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AB - Motivation: High-throughput 'ChIP-chip' and 'ChIP-seq' methodologies generate sufficiently large data sets that analysis poses significant informatics challenges, particularly for research groups with modest computational support. To address this challenge, we devised a software platform for storing, analyzing and visualizing high resolution genome-wide binding data. GeneTrack automates several steps of a typical data processing pipeline, including smoothing and peak detection, and facilitates dissemination of the results via the web. Our software is freely available via the Google Project Hosting environment at http://genetrack.googlecode.com.

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U2 - 10.1093/bioinformatics/btn119

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SP - 1305

EP - 1306

JO - Bioinformatics

JF - Bioinformatics

IS - 10

ER -

GeneTrack - A genomic data processing and visualization framework

Abstract

All Science Journal Classification (ASJC) codes

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