Detecting transcriptionally active regions using genomic tiling arrays

Gabor Halasz¹^,2 , Marinus F van Batenburg¹^,3 , Joelle Perusse⁴ , Sujun Hua⁴ , Xiang-Jun Lu¹ , Kevin P White⁴^,5 and Harmen J Bussemaker¹^,6

¹Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY, 10027 USA

²Integrated Program in Cellular, Molecular and Biophysical Studies, Columbia University, 630 w. 168^thStreet, New York, NY, 10032 USA

³Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands

⁴Department of Genetics, Yale University School of Medicine, 333 Cedar Street, PO Box 208005, New Haven, CT, 06520-8005, USA

⁵Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street, PO Box 208106, New Haven, CT, 06250-8106, USA

⁶Center for Computational Biology and Bioinformatics, Columbia University, 1130 St. Nicholas Avenue, New York, NY, USA

author email corresponding author email

Genome Biology 2006, 7:R59doi:10.1186/gb-2006-7-7-r59


Published:	19 July 2006

Subject areas: Genome studies, Bioinformatics

Additional files

Additional date file 1:

Probe sequence and raw signal intensities for exon probes

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Probe sequence and raw signal intensities for non-exon probes

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Additional date file 3:

Probe sequence and raw signal intensities for negative control probes

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Genomic coordinates for regions measured by exon probes

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Additional date file 5:

Genomic coordinates for regions measured by non-exon probes

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Additional date file 6:

Supplementary Figure 1 demonstrates that signal variability between different probe populations on the same channel is not explained by probe sequence composition; supplementary Figure 2 shows Q-Q plots for NCP signal intensities in different channels, showing that these have heterogeneous and non-normal distributions; supplementary Figure 3 demonstrates that signal variability between negative control probes on different channels is not explained by probe sequence composition; supplementary Figure 4 has two ROC curves showing true positive rate versus false positive rate relative to (a) mRNA and (b) EST transcripts annotated in the UCSC database (the '+' symbol corresponds to the transfrags as defined by Cheng et al. [3]; and lines correspond to our algorithm as applied with/without neighborhood smoothing and with/without minrun/maxgap post-processing)

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