Journal of Neuropathology & Experimental NeurologyNeurosciencePeer-reviewed journal article
An analysis of RNA quality metrics in human brain tissue
Jiahe TianiD, Tiffany G. Lam, Sophie K. Ross, Benjamin Ciener, Sandra LeskineniD, Sharanya Sivakumar, David A. BennettiD, Vilas MenoniD, Guy M. McKhanniD, Alexi M. RunnelsiD, Andrew F. TeichiD
Abstract
Human brain tissue studies have historically used a range of metrics to assess RNA quality. However, few large-scale cross-comparisons of pre-sequencing quality metrics with RNA-seq quality have been published. Here, we analyze how well metrics gathered before RNA sequencing (post-mortem interval (PMI) and RNA integrity number RIN) relate to analyses of RNA quality after sequencing (Percent of counts in Top Ten genes (PTT), 5 bias, and 3 bias) as well as with individual gene counts across the transcriptome. We conduct this analysis across four different human cortical brain tissue collections sequenced with varying library preparation protocols. PMI and RIN have a low inverse correlation, and both PMI and RIN show consistent and opposing correlations with PTT. Unlike PMI, RIN shows strong consistent correlations with measurements of 3 and 5 bias, and RIN also correlates with 3,933 genes across datasets, in comparison to 138 genes for PMI. Neuronal and immune response genes correlate positively and negatively with RIN respectively, suggesting that different gene sets have divergent relationships with RIN in brain tissue. In summary, these analyses suggest that conventional metrics of RNA quality have varying degrees of value, and that PMI has an overall minimal but reproducible effect on RNA quality.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 13
- FWCI
- 1.77
- Field percentile
- top 14%
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