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Recent evidence demonstrates the power of RNA sequencing (RNA-Seq) for identifying valuable and urgently needed blood biomarkers and advancing both early and accurate detection of neurological diseases, and in particular Parkinson's disease (PD). RNA sequencing technology enables non-biased, high throughput, probe-independent inspection of expression data and high coverage and both quantification of global transcript levels as well as the detection of expressed exons and junctions given a sufficient sequencing depth (coverage). However, the analysis of sequencing data frequently presents a bottleneck. Tools for quantification of alternative splicing from sequenced libraries hardly exist at the present time, and methods that support multiple sequencing platforms are especially lacking. Here, we describe in details a whole RNA-Seq transcriptome dataset produced from PD patient's blood leukocytes. The samples were taken prior to, and following deep brain stimulation (DBS) treatment while being on stimulation and following 1 h of complete electrical stimulation cessation and from healthy control volunteers. We describe in detail the methodology applied for analyzing the RNA-Seq data including differential expression of long noncoding RNAs (lncRNAs). We also provide details of the corresponding analysis of in-depth splice isoform data from junction and exon reads, with the use of the software AltAnalyze. Both the RNA-Seq raw (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42608) and analyzed data (https://www.synapse.org/#!Synapse:syn2805267) may be found valuable towards detection of novel blood biomarkers for PD.

genomics data

Whole transcriptome RNA sequencing data from blood leukocytes derived from Parkinson's disease patients prior to and following deep brain stimulation treatment