OP-26 Predicting protein level changes from transcript level data
Presenting Author: Edward Lau, University of Colorado School of Medicine
Abstract: Proteins perform the majority of biological functions. It follows that gene signatures from transcriptomics data would have different biological relevance based on how well they predict protein levels. We revisit how well transcript level changes predict protein level changes at gene-wise granularity, using current sequencing and mass spectrometry data sets and comparing several statistical learning approaches. The result adds to emerging evidence for a biological basis of RNA-protein non-correlation that varies by cellular components and pathways. We identified proteins whose levels are nonlinearly related to transcript levels, as well as proteins better predicted by different transcripts than their own gene's. We propose a strategy to analyze and prioritize transcript signatures in RNA sequencing data and apply it to examine striated muscle aging mechanisms.
