High-throughput transcriptomics has advanced through the introduction of TempO-seq, a targeted alternative to traditional RNA-seq. TempO-seq platforms use 50 nucleotide probes, each specifically designed to target a known transcript, thus allowing for reduced sequencing depth per sample compared with RNA-seq, without compromising the accuracy of results. Thus far, studies using the TempO-seq method have relied on existing tools for processing the resulting short read data. However, these tools were originally designed for other data types. While they have been used for processing of early TempO-seq data, they have not been systematically assessed for accuracy or compared to determine an optimal framework for processing and analyzing TempO-seq data. Sciome’s work to provide recommendations on alignment and normalization methods for TempO-seq data was recently published. In this work we re-analyze several publicly available TempO-seq data sets covering a range of experimental designs and use corresponding RNA-seq data sets as a gold standard to rigorously assess accuracy at multiple levels. We compared 6 aligners and 5 normalization methods across various accuracy and performance metrics. Complex aligners and advanced normalization methods did not appear to have any general advantage over simpler methods when it came to analyzing TempO-seq data. The reduced complexity of the sequencing space, and the fact that TempO-seq probes were all equal length, appeared to reduce the need for elaborate bioinformatic or statistical methods used to address these factors in RNA-seq data. We hope to help the community make informed analysis decisions through the work in this publication!
