Using expression arrays for copy number detection: an example from E. coli

Abstract

Background: The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.

Results: We have developed a robust and flexible methodology for high-resolution analysis of DNA copy number of whole genomes, using Affymetrix high-density expression oligonucleotide microarrays. Copy number is obtained from fluorescence signals after processing with novel normalization, spatial artifact correction, data transformation and deletion/duplication detection. We applied our approach to identify deleted and amplified regions in E. coli mutants obtained after prolonged starvation.

Conclusion: The availability of Affymetrix expression chips for a wide variety of organisms makes the proposed array CGH methodology useful more generally.

Figure 1

Pre-Normalized Probe Intensity Distribution. Summary of data prior to normalization. Distribution of raw signal intensities [left panel], and histogram of median chip intensities [right panel].

Figure 2

Comparison of Different Normalization Methods. Comparison of different normalization methods: distribution of pre-normalized intensities in the dataset [top left], distribution of normalized intensities using quantile normalization [top right], distribution of normalized intensities using invariant set normalization [bottom left], distribution of normalized intensities using proposed normalization routine [bottom right].

Figure 3

Background Correction and Spatial normalization. Background correction steps: chip image prior to background correction [top left], residual map prior to correction [top right], smoothed residuals map [bottom left], residual map after background correction [bottom right].

Figure 4

Background Correction and Spatial normalization. Effect of background correction on residuals. Black solid line shows the smoothed residuals before correction, gray lines represents the residuals after correction.

Figure 5

Signal Transformation. Result of subtracting probe effect from raw signal. Raw signal [upper panel] and transformed signal [lower panel].

Figure 6

Signal Transformation. Distribution of log raw [top panel] and log transformed [bottom panel] intensity for probes in present (solid) and deleted (dashed) genomic regions.

Figure 7

HMM-based DNA Copy Number Inference. A summary of signal processing steps using suggested set of routines. Upper panel shows signal before transformation (raw data), lower panel shows signal after transformation (grey dots) and result of fit to the HMM (black solid line).

Figure 8

Normalization Method. Schematic description of normalization routine.