Sample Size Estimation for Detection of Splicing Events in Transcriptome Sequencing Data.

ABSTRACT: Merging data from multiple samples is required to detect low expressed transcripts or splicing events that might be present only in a subset of samples. However, the exact number of required replicates enabling the detection of such rare events often remains a mystery but can be approached through probability theory. Here, we describe a probabilistic model, relating the number of observed events in a batch of samples with observation probabilities. Therein, samples appear as a heterogeneous collection of events, which are observed with some probability. The model is evaluated in a batch of 54 transcriptomes of human dermal fibroblast samples. The majority of putative splice-sites (alignment gap-sites) are detected in (almost) all samples or only sporadically, resulting in an U-shaped patte

SUBMITTER: Kaisers W 

PROVIDER: S-EPMC5618549 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature