Sentence examples for models of transcript from inspiring English sources

We focused on exon level expression and used existing models of transcript isoforms to identify alternative exon structures.

As more microbial transcriptome data become available (e.g. through sequencing efforts such as the MMETSP (http://marinemicroeukaryotes.org/)), it should be possible to refine these models of transcript abundance to reflect increased levels of transcripts involved in core processes and thereby produce more realistic simulations of metatranscriptome data.

To investigate how changes in rates of transcription and degradation could affect differential gene expression in torpor, we developed a mathematical model of transcript dynamics across the torpor arousal cycle.

A model of transcript production was assumed that includes a two-state promoter (active or inactive), followed by a step associated to transcription initiation of the active promoter.

Results: We propose a novel approximate inference scheme based on VB and apply it to an existing model of transcript expression inference from RNA-seq data.

Previous models of mRNA transcript abundance have focused on modelling mRNA transcript abundance genome-wide [ 15– 15].

As TFs and HMs are known to play critical roles in regulating transcription, accurate predictive models of mRNA transcript abundance have been constructed from corresponding ChIP-seq binding data for various organisms, cell types and modelling techniques [ 14, 17– 17].

Predictive models of mRNA transcript abundance were constructed using two regression techniques: log-linear regression and SVR [ 64], as illustrated in Figure  4(a).

Finally, referring to the renewed gene models, we calculated FPKM (Fragments Per Kilobase of exon Models) of each transcript under normal, abiotic stressed conditions, and leaf epidermal cells as well [ 45].

Unlike the R score (i.e., the coefficient of determination, equivalent to the square of the Pearson correlation coefficient and previously used to evaluate models of mRNA transcript abundance [ 16, 17]), the adjusted R prevents spurious inflation due to the introduction of additional explanatory variables [ 65].

This again highlights the importance of seed projects defining cell types in terms of model distributions of transcript counts.