Sequence biases in large scale gene expression profiling data

Abstract

We present the results of a simple, statistical assay that measures the G+C content sensitivity bias of gene expression experiments without the requirement of a duplicate experiment. We analyse five gene expression profiling methods: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, 'Classic' Massively Parallel Signature Sequencing (MPSS) and 'Signature' MPSS. We demonstrate the methods have systematic and random errors leading to a different G+C content sensitivity. The relationship between this experimental error and the G+C content of the probe set or tag that identifies each gene influences whether the gene is detected and, if detected, the level of gene expression measured. LongSAGE has the least bias, while Signature MPSS shows a strong bias to G+C rich tags and Affymetrix data show different bias depending on the data processing method (MAS 5.0, RMA or GC-RMA). The bias in the Affymetrix data primarily impacts genes expressed at lower levels. Despite the larger sampling of the MPSS library, SAGE identifies significantly more genes (60% more RefSeq genes in a single comparison).

Figure 1

Histogram showing the distribution of bias among five experimental methods. The A+T/C+G bias of an individual experiment is measured in units of the number of standard deviations by which the observed bias deviates from the expected bias. The biases of the individual experiments comprising each series are plotted as a histogram. The position of the peak and the width of the distribution are different for each method and illustrate differences in systematic and random error with respect to A+T/G+C bias.

Figure 2

Histogram showing the distribution of bias among individual series of Affymetrix experiments. For each series, the position of the peak and the width of the distribution are different. The figure illustrates individual laboratories have their own bias and variation in bias. Thus, combining experiments from different laboratories leads to a greater width in the summed distribution (Affymetrix 993 experiments). The series identifiers in the figure (e.g. GSE994) are GEO dataset identifiers.