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Bioinformatics. 2016 Mar 1;32(5):673-81. doi: 10.1093/bioinformatics/btv632. Epub 2015 Nov 2.

Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis.

Author information

1
Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain, Universitat Autonoma de Barcelona, Institut de Biotecnologia i de Biomedicina, Bellaterra (Cerdanyola del Valles), Barcelona, Spain and.
2
Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona 08003, Spain, Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain, IMIM, Hospital del Mar Medical Research Institute, Barcelona 08003, Spain, CIBER de Epidemiología y Salud Pública (CIBERESP), CRG, Instituto Carlos III Barcelona 08003, Spain.
3
Institut Neuropatologia, Servei Anatomia Patològica, IDIBELL-Hospital Universitari de Bellvitge, Universitat de Barcelona, Spain and CIBER de Enfermedades Neurodegenerativas (CIBERNED), Instituto Carlos III, Barcelona, Spain.

Abstract

MOTIVATION:

Most computational tools for small non-coding RNAs (sRNA) sequencing data analysis focus in microRNAs (miRNAs), overlooking other types of sRNAs that show multi-mapping hits. Here, we have developed a pipeline to non-redundantly quantify all types of sRNAs, and extract patterns of expression in biologically defined groups. We have used our tool to characterize and profile sRNAs in post-mortem brain samples of control individuals and Parkinson's disease (PD) cases at early-premotor and late-symptomatic stages.

RESULTS:

Clusters of co-expressed sRNAs mapping onto tRNAs significantly separated premotor and motor cases from controls. A similar result was obtained using a matrix of miRNAs slightly varying in sequence (isomiRs). The present framework revealed sRNA alterations at premotor stages of PD, which might reflect initial pathogenic perturbations. This tool may be useful to discover sRNA expression patterns linked to different biological conditions.

AVAILABILITY AND IMPLEMENTATION:

The full code is available at http://github.com/lpantano/seqbuster

CONTACT:

lpantano@hsph.harvard.edu or eulalia.marti@crg.eu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26530722
DOI:
10.1093/bioinformatics/btv632
[Indexed for MEDLINE]

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