Recent evidence demonstrates the power of RNA sequencin
g (RNA-Seq) for identifying valuable and urgently need-
ed blood biomarkers and advancing both early and accurate detection of neurological diseases, and in particular
Parkinson's disease (PD). RNA sequencing technology enab
les non-biased, high throughput, probe-independent in-
spection of expression data and high coverage and both quanti
fi
cation of global transcript levels as well as the de-
tection of expressed exons and junctions given a suf
fi
cient sequencing depth (coverage). However, the analysis of
sequencing data frequently presents a bottleneck. Tools for quanti
fi
cation of alternative splicing from sequenced li-
brarieshardlyexistatthepresenttime,andmethodsthatsupportmultiplesequencingplatformsare especiallylack-
ing. 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 o
f the corresponding analysis of in-depth splice isoform
data from junction and exon reads, with the use o
f 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.
© 2014 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND licens