Dumpster diving in RNA-sequencing to find the source of every last read
High throughput RNA sequencing technologies have provided invaluable research opportunities across distinct scientific domains by producing quantitative readouts of the transcriptional activity of both entire cellular populations and single cells. The majority of RNA-Seq analyses begin by mapping each experimentally produced sequence (i.e., read) to a set of annotated reference sequences for the organism of interest. For both biological and technical reasons, a significant fraction of reads remains unmapped. In this work, we develop Read Origin Protocol (ROP) to discover the source of all reads originating from complex RNA molecules, recombinant T and B cell receptors, and microbial communities. We applied ROP to 8,641 samples across 630 individuals from 54 tissues. A fraction of RNA-Seq data (n=86) was obtained in-house; the remaining data was obtained from the Genotype-Tissue Expression (GTEx v6) project. To generalize the reported number of accounted reads, we also performed ROP analysis on thousands of different, randomly selected, and publicly available RNA-Seq samples in the Sequence Read Archive (SRA). Our approach can account for 99.9% of 1 trillion reads of various read length across the merged dataset (n=10641). Using in-house RNA-Seq data, we show that immune profiles of asthmatic individuals are significantly different from the profiles of control individuals, with decreased average per sample T and B cell receptor diversity. We also show that immune diversity is inversely correlated with microbial load. Our results demonstrate the potential of ROP to exploit unmapped reads in order to better understand the functional mechanisms underlying connections between the immune system, microbiome, human gene expression, and disease etiology. ROP is freely available at https://github.com/smangul1/rop and currently supports human and mouse RNA-Seq reads.