Parkinson's disease (PD) is an age-related, chronic and progressive neurodegenerative disorder characterized by a loss of multifocal neurons, resulting in both non-motor and motor symptoms. While several genetic and environmental contributory risk factors have been identified, more exact methods for diagnosing and assessing prognosis of PD have yet to be established. Here we describe the generation and validation of a dataset comprising whole-blood transcriptomes originally intended for use in detection of blood biomarkers and transcriptomic network changes indicative of PD. Whole-blood samples extracted from both early-stage PD patients and healthy controls were sequenced using no-amplification non-tagging cap analysis of gene expression (nAnT-iCAGE) to analyse differences in global RNA expression patterns across the conditions. Subsequent sampling of a subset of PD patients one-year later provides the opportunity to study changes in transcriptomes arising due to disease progression.