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If a peak was mapped to more than one feature (e.g. transcription start site and primary transcript), the data listed in the tables of differentially methylated peaks (Supplementary Table S1) were restricted to a single feature selected according to the following order: transcription start site, primary transcript, CpG island, and tiled region.
During a second reading of the transcript, the data were coded to identify and explore themes.
Table 4 summarizes the impacts of ITR on the transcript, the data provided, the interviewee and the researcher.
ITR, in various forms, is widely used among qualitative researchers, however there is limited methodological guidance on how it should be employed and little is known about its actual impact on the transcript, the data, the interviewee or the researcher.
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To identify so far unknown transcripts, the data from the whole transcriptome was used.
Two researchers read and re-read the transcripts; the data were coded, managed, and analysed using NVivo (version 9.0).
Confidentiality was assured by keeping the audio files locked down and de-identifying the transcripts; the data were only accessible to the authors.
Although a recent study by te Velde et al. [ 8] used 20,000 transcript microarrays, the data were not validated.
We began our thematic analysis as we transcribed and organised the transcripts from the data collection sessions, also including notes and transcripts from the researcher debriefing sessions.
The "ground truth" for states is generated by employing a GMM-HMM baseline system to transcript the training data beforehand.
Moreover, there were some unknown transcripts in the data set.
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