Analysis of tag-position bias in MPSS technology

Abstract

Background: Massively Parallel Signature Sequencing (MPSS) technology was recently developed as a high-throughput technology for measuring the concentration of mRNA transcripts in a sample. It has previously been observed that the position of the signature tag in a transcript (distance from 3' end) can affect the measurement, but this effect has not been studied in detail.

Results: We quantify the effect of tag-position bias in Classic and Signature MPSS technology using published data from Arabidopsis, rice and human. We investigate the relationship between measured concentration and tag-position using nonlinear regression methods. The observed relationship is shown to be broadly consistent across different data sets. We find that there exist different and significant biases in both Classic and Signature MPSS data. For Classic MPSS data, genes with tag-position in the middle-range have highest measured abundance on average while genes with tag-position in the high-range, far from the 3' end, show a significant decrease. For Signature MPSS data, high-range tag-position genes tend to have a flatter relationship between tag-position and measured abundance. Thus, our results confirm that the Signature MPSS method fixes a substantial problem with the Classic MPSS method. For both Classic and Signature MPSS data there is a positive correlation between measured abundance and tag-position for low-range tag-position genes. Compared with the effects of mRNA length and number of exons, tag-position bias seems to be more significant in Arabadopsis. The tag-position bias is reflected both in the measured abundance of genes with a significant tag count and in the proportion of unexpressed genes identified.

Conclusion: Tag-position bias should be taken into consideration when measuring mRNA transcript abundance using MPSS technology, both in Classic and Signature MPSS methods.

Figure 1

Tag-position bias analysis using Classic and Signature data from Arabidopsis. Plots A and B show the relationship between tag-position and the percentage of low-count genes. The gene density shows the distribution of genes with a given tag-position. The curves on the top of each plot show the relationship between tag-position and the percentage of low-count genes. Plots C and D show the dependence of measured abundance on tag-position for genes with significant associated tag-count. The scatter-plot shows all data points from five different libraries. The red curve represents a LOWESS smooth of all these data and the green curve represents the bootstrap credibility intervals (5–95%). (Inset) Each line except the bold black one represents a LOWESS smooth of an individual library, while the bold black one is the same as the red one in the main plot. Plots A and C are from Classic MPSS data while Plots B and D are from Signature MPSS data.

Figure 2

Tag-position bias analysis using Human Classic and Rice Signature data. The same as described in Figure 1 except using Human Classic Data (Plots A and C) and Rice Signature Data (Plots B and D).

Figure 3

Comparison with measured abundance using Classic and Signature data from Arabidopsis. Plots compare the measured abundance using Classic and Signature data from Arabisopsis. In Plot A, all data points in the scatter-plot represent mean values of 21608 genes from Classic Data and Signature Data of five experiments measuring the same sample. The red line is the middle line (x = y) and the blue line is calculated by total least squares. In Plots B-E, genes are selected with log₂ (tag-position) in the ranges [4 6], [6 8], [8 10] and [10 12], with 3417, 10005, 8037 and 149 genes in each range respectively.

Figure 4

Dependence of expression on mRNA length and number of exons. Plot A shows the dependence of measured abundance on mRNA length. Plot B shows the dependence of measured abundance on number of exons.