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Fig. 13 Exemplar trial. This figure shows the fi...

Figure 3 The number of distinct tags assigned per...

Figure 4 The number of distinct tags assigned per...

Figure 2. (a) Schematic representation of the DNA ...

Figure 1 Input formats. Left: generic format, tab...

Figure 6 Tail probabilities for tagging various f...

Figure 4 Histogram of log2 absolute tag counts fr...

Figure 1 Four shape classes of static TSS usage. ...

Fig. 5. A histogram of the occurrence of individ...

Figure 5 Access probability, probability that bac...

Figure 4: Examples of posterior marginal probability distributions for four genes, YFL060C, YPR035W, YOL040C, and YKL152C, during log phase based on data in [9]. Genes were chosen to cover a wide range of tag formation probabilities φi and counts Ti. More specifically, these genes had tag formation probabilities φi of 0.356879, 0.44494, 0.98255, and 0.555, respectively, and observed tags counts of 0, 10, 103, and 228, respectively.

Image Text (High Precision): Marginal frequency mRNA

Other Images from "Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework":


Figure 3 Composite diagram of tag formation proba...

Figure 1 Diagram of hypothetical mRNA transcript ...

Figure 6 Comparison of the tag and mRNA frequency...

Figure 4 Examples of posterior marginal probabili...

Figure 5 Illustration of how changing the tag for...

Figure 2 Posterior probability distributions for ...

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Abstract

BackgroundSerial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome.ResultsUsing the yeast Saccharomyces cerevisiae as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA.ConclusionWith a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process.


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