Abstract
RNA sequencing (RNA-seq) is a powerful technology that allows one to assess the RNA levels in a sample. Analysis of these levels can help in identifying novel transcripts (coding, non-coding and splice variants), understanding transcript structures, and estimating gene/allele expression. Biologists face specific challenges while designing RNA-seq experiments. The nature of these challenges lies in determining the total number of sequenced reads and technical replicates required for detecting marginally differentially expressed transcripts. Despite previous attempts to address these challenges, easily-accessible and biologist-friendly mobile applications do not exist. Thus, we developed RNAtor, a mobile application for Android platforms, to aid biologists in correctly designing their RNA-seq experiments. The recommendations from RNAtor are based on simulations and real data.
Highlights
RNA sequencing (RNA-seq) offers several advantages over low-throughput technologies such as quantitative PCR and annotation-dependent methods such as microarrays
We describe RNAtor, an Android app with a user-friendly graphical user interface (GUI) that helps biologists design RNA-seq experiments
RNAtor was evaluated using questions that a biologist would typically ask before starting an experiment, followed by the recommendations provided by RNAtor
Summary
RNA-seq offers several advantages over low-throughput technologies such as quantitative PCR and annotation-dependent methods such as microarrays. Designing RNA-seq experiments accurately, poses challenge to biologists. This is true when prior knowledge on genome or transcriptome of the organism of choice is not available. Web-based tools, Scotty (Busby et al, 2013) and EDDA (Luo et al, 2014), have an established precedence in aiding RNA-seq design. While Scotty relies solely on pilot or prototype data, EDDA relies on either pilot data or a simulate-and-test paradigm to account for variability across experimental conditions. Scotty has a built-in t-test based module, whereas EDDA has been linked to five other DE tools, post mode-normalization of the data. Both can detect DEGs upto 2-fold difference. How does RNAtor account for these? How were the various differential analysis softwares run? They usually have cutoffs that can be chosen to make their results more or less stringent
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