This was further elaborated and refined through team-based, collaborative coding which made the task of managing 758 transcripts efficient while helping to ensure coding consistency.
In this article, we present a novel approach to the tasks of alternative transcript quantification and differential processing (DP) detection given RNA-Seq data and fixed gene structures, which may be provided by an existing genome annotation or predicted from the data via transcriptome assembly.
The task of defining the complete set of transcripts is complicated because of the fact that transcriptomes are of high dynamic entities, which change in response to both of the intracellular signals and extracellular environment.
The task of making estimates of the input quantity (transcript concentration) of a measurement from observations of its output (intensity) is called calibration.
The task of assigning translational start sites is fraught with errors especially in the absence of transcript data.
Once all the data was collected, a team of five research analysts recruited by the LSE began the complex task of analysing the lengthy transcripts in search of themes.
Further, estimating the effectiveness of transcript discovery using Roche 454 transcriptome data is still a difficult task.
To tentatively map such putative regions of interchromosomal contact, we revisited the specific task of identifying chimeric sequences in transcript databases.
To compute this, we solve the quantification task k times using all transcripts from the transcript inference step and set the quantification value of transcript t to zero.
The interview transcripts were first analysed for problems in the participants' undertaking of the task.
