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 pattern for observation probabilities. The probabilistic model systematically underestimates event numbers due to a bias resulting from finite sampling. However, using an additional assumption, the probabilistic model can predict observed event numbers within a <10% deviation from the median. Single samples contain a considerable amount of uniquely observed putative splicing events (mean 7122 in alignments from TopHat alignments and 86,215 in alignments from STAR). We conclude that the probabilistic model provides an adequate description for observation of gap-sites in transcriptome data. Thus, the calculation of required sample sizes can be done by application of a simple binomial model to sporadically observed random events. Due to the large number of uniquely observed putative splice-sites and the known stochastic noise in the splicing machinery, it appears advisable to include observation of rare splicing events into analysis objectives. Therefore, it is beneficial to take scores for the validation of gap-sites into account.

CRISPR activation enables high-fidelity reprogramming into human pluripotent stem cells

“…We then confirmed the pluripotency of generated CRISPRa iPSC lines. Principal-component analysis (PCA) of bulk RNA-seq of 15 CRISPRa iPSC lines from 5 LCL and 2 fibroblast donors demonstrated that all CRISPRa iPSC lines grouped with previously published iPSC lines generated from blood and fibroblast cells using conventional methods ( Carcamo-Orive et al., 2017 ; Kilpinen et al., 2017 ) and distinct from LCL and fibroblasts ( Kaisers et al., 2017 ; Ozgyin et al., 2019 ) ( Figure 1 C). CRISPRa iPSC lines were then compared more closely with a total of 661 Human Induced Pluripotent Stem Cells Initiative (HipSci) iPSC lines, which showed that our cell lines grouped closely together with the HipSci lines ( Kilpinen et al., 2017 ) ( Figure 1 D).…”

“…Bulk RNA-seq was performed as a service at Novagen after the cells passed quality control. The expression profiles of the RNA-seq data were compared to published reference datasets of iPSC, LCL, and fibroblasts (GEO: GSE79636 , HipSci, GEO: GSE121926 , E-MTAB-4652) ( Carcamo-Orive et al., 2017 ; Kaisers et al., 2017 ; Kilpinen et al., 2017 ; Ozgyin et al., 2019 ) analyzed with the same methods.…”

Fingerprints of Modified RNA Bases from Deep Sequencing Profiles

“…Higher depths (typically on the order of ca. 100×) are sometimes used in RNA-seq for identification of splicing events. , Our experiments suggest that of the modifications tested, most would still not be distinguishable at this depth. One exception is m6G, since its miscoding rate is high, but it would likely only be distinguishable at high levels of occupancy.…”