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The high coverage of gene models by this microarray made it possible to identify splicing variants for a given transcript.

Expression profiles are normalized by the maximum expression level of a given transcript across stages and hierarchically clustered using Cluster 3.0 and visualized as a heatmap using Java Treeview.

These forms represent a new level of speciation: A protein derived from a given transcript diversifies into different protein species.

The numbers of mapped reads on a given transcript and those on other regions for two stages were used as variables in 2 × 2 contingency tables for each test.

A cumulative (or "total") context score for a given transcript can thus be calculated by summation of the score values of individual sites.

A student one sample two-tails T-test was first performed on all the exons of a given transcript of a given gene.

To objectively quantify tissue-specificity, we used the concept of entropy (HT) to measure the deviation in expression uniformity for a given transcript across multiple tissues [17].

For a given transcript with relative abundance π, the sampling frequency when N transcripts are sampled can be modeled by Binomial N, π).

Because K is unknown, this method does not allow the absolute quantification of a given transcript, but it permits calculating the relative abundance of two transcripts W1/W2.

The two shHP1γ cell lines did not always exhibit the same levels of a given transcript and distinct degrees of variation were observed between the experiments in all cell lines.

Public cancer genome mutation data was downloaded from their respective publication sites [8], [9], [11], [12], [14] and run through B-SIFT with protein sequences corresponding to the given transcript identifiers in each publication.

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