Viewing options:

Abstract
Full text
PDF (1MB)
Additional files

Associated material:

PubMed record

Related literature:

Articles citing this article
on Google Scholar
on ISI Web of Science
on PubMed Central
Other articles by authors
on Google Scholar

Chang LW
Payton JE
Yuan W
Ley TJ
Nagarajan R
Stormo GD

on PubMed

Chang LW
Payton JE
Yuan W
Ley TJ
Nagarajan R
Stormo GD
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Research

Computational identification of the normal and perturbed genetic networks involved in myeloid differentiation and acute promyelocytic leukemia

Li Wei Chang¹ , Jacqueline E Payton² , Wenlin Yuan³ , Timothy J Ley³^,4 , Rakesh Nagarajan² and Gary D Stormo⁴

¹Department of Biomedical Engineering, Washington University, St Louis, MO 63130, USA

²Department of Pathology and Immunology, Division of Laboratory Medicine, Washington University School of Medicine, St Louis, MO 63110, USA

³Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO 63110, USA

⁴Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA

author email corresponding author email

Genome Biology 2008, 9:R38doi:10.1186/gb-2008-9-2-r38


Published:	21 February 2008

Subject areas: Cell biology, Genetics, Medicine, Molecular biology

Additional files

Additional data file 1:

These genes were identified by calculating the Pearson's correlation coefficients of their expression profiles to each individual expression pattern.

Format: XLS Size: 21KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional data file 2:

Genes identified in each coexpressed gene cluster.

Format: XLS Size: 79KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional data file 3:

Predicted regulatory targets of TFs identified in each coexpressed gene cluster.

Format: XLS Size: 24KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional data file 4:

Down-regulated genes in the in vitro myeloid differentiation system.

Format: XLS Size: 21KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional data file 5:

(a) Genes upregulated at day 0; (b) genes upregulated at day 0 and day 1.

Format: PDF Size: 120KB Download file

This file can be viewed with: Adobe Acrobat Reader

Additional data file 6:

(a) Genes upregulated at day 0; (b) genes upregulated at day 0 and day 1. Additional TFs identified by PAP and their regulatory relationships to other genes in the myeloid networks are colored red.

Format: PDF Size: 408KB Download file

This file can be viewed with: Adobe Acrobat Reader

Additional data file 7:

Myeloid TFs identified by PAP and their regulatory relationships to other genes in the myeloid development networks.

Format: XLS Size: 23KB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional data file 8:

(a) TF genes upregulated at day 0. (b) TF genes upregulated at day 0 and day 1. (c) TF genes upregulated at day 6 and day 7. (d) TF genes upregulated at day 7. Color coding in these networks denotes how these TF genes were identified: blue, TFs identified by coexpression; green, additional TFs identified by PAP; yellow, TFs identified by both coexpression and PAP.

Format: PDF Size: 22KB Download file

This file can be viewed with: Adobe Acrobat Reader

Additional data file 9:

TFs dysregulated in APL and their predicted regulatory targets.

Format: XLS Size: 25KB Download file

This file can be viewed with: Microsoft Excel Viewer