scholarly journals MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data

2015 ◽  
Vol 10 ◽  
pp. BMI.S25132 ◽  
Author(s):  
Jun-ichi Satoh ◽  
Yoshihiro Kino ◽  
Shumpei Niida
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Public Data ◽  
Data Analysis Pipeline

Background Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious. Methods To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath. Results By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28–3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186–5p, miR-425–5p, miR-550a-5p, miR-1468, miR-4781–3p, miR-5001–3p, and miR-6513–3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17–3p, miR-29b-3p, miR-98–5p, miR-144–5p, miR-148a-3p, miR-502–3p, miR-660–5p, miR-1294, and miR-3200–3p. DIANA miRPath indicated that miRNA-regulated pathways potentially down– regulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication. Conclusions The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA–Seq data.

scholarly journals tRNAnalysis: A flexible pre-processing and next-generation sequencing data analysis pipeline for transfer RNA

2019 ◽  
Author(s):  
Anna James-Bott ◽  
Adam P. Cribbs
Keyword(s):  
Data Analysis ◽  
Rna Sequencing ◽  
Small Rna ◽  
Read Length ◽  
Next Generation Sequencing Data ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Gene Transcripts ◽  
Data Analysis Pipeline

AbstractMany tools have been developed to analyse small RNA sequencing data, however it remains a challenging task to accurately process reads aligning to small RNA due to their short-read length. Most pipelines have been developed with miRNA analysis in mind and there are currently very few workflows focused on the analysis of transfer RNAs. Moreover, these workflows suffer from being low throughput, difficult to install and lack sufficient visualisation to make the output interpretable. To address these issues, we have built a comprehensive and customisable small RNA-seq data analysis pipeline, with emphasis on the analysis of tRNAs. The pipeline takes as an input a fastq file of small RNA sequencing reads and performs successive steps of mapping and alignment to transposable elements, gene transcripts, miRNAs, snRNAs, rRNA and tRNAs. Subsequent steps are then performed to generate summary statistics on reads of tRNA origin, which are then visualised in a html report. Unlike other low-throughput analysis tools currently available, our high-throughput method allows for the simultaneous analysis of multiple samples and scales with the number of input files. tRNAnalysis is command line runnable and is implemented predominantly using Python and R. The source code is available at https://github.com/Acribbs/tRNAnalysis.

scholarly journals sRNAnalyzer—a flexible and customizable small RNA sequencing data analysis pipeline

2017 ◽  
Vol 45 (21) ◽  
pp. 12140-12151 ◽  
Author(s):  
Xiaogang Wu ◽  
Taek-Kyun Kim ◽  
David Baxter ◽  
Kelsey Scherler ◽  
Aaron Gordon ◽  
...  
Keyword(s):  
Data Analysis ◽  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Data Analysis Pipeline ◽  
Sequencing Data Analysis

scholarly journals Relationships of Alzheimer’s disease and apolipoprotein E genotypes with small RNA and protein cargo of brain tissue extracellular vesicles

2020 ◽  
Author(s):  
Yiyao Huang ◽  
Tom A. P. Driedonks ◽  
Lesley Cheng ◽  
Andrey Turchinovich ◽  
Harinda Rajapaksha ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Extracellular Vesicles ◽  
Small Rna ◽  
Apoe Genotype ◽  
Tau Proteins ◽  
Small Rna Sequencing ◽  
Health Crisis ◽  
Metabolic Functions ◽  
Apolipoprotein E Genotypes

ABSTRACTAlzheimer’s disease (AD) is a public health crisis that grows as populations age. Hallmarks of this neurodegenerative disease include aggregation of beta-amyloid peptides and hyperphosphorylated tau proteins in the brain. Variants of the APOE gene are the greatest known risk factors for sporadic AD. As emerging players in AD pathophysiology, extracellular vesicles (EVs) contain proteins, lipids, and RNAs and are involved in disposal of cellular toxins and intercellular communication. AD-related changes in the molecular composition of EVs may contribute to pathophysiology and lend insights into disease mechanisms. We recently adapted a method for separation of brain-derived EVs (bdEVs) from post-mortem tissues. Using this method, we isolated bdEVs from AD patients with different APOE genotypes (n=23) and controls (n=7). bdEVs were counted, sized, and subjected to parallel small RNA sequencing and proteomic analysis. Numerous bdEV-associated RNAs and proteins correlated with AD pathology and APOE genotype. Some of the identified entities have been implicated previously in important AD-related pathways, including amyloid processing, neurodegeneration, and metabolic functions. These findings provide further evidence that bdEVs and their molecular cargo modulate development and progression of AD.

scholarly journals Single nuclei RNA‐seq data analysis identifies glial cell lineages associated with Alzheimer’s disease severity

2020 ◽  
Vol 16 (S3) ◽  
Author(s):  
Rebecca Elyanow ◽  
Kevin J. Green ◽  
Anna K. Greenwood ◽  
Katherine E. Prater ◽  
Erica J. Melief ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Glial Cell ◽  
Disease Severity ◽  
Rna Seq ◽  
Cell Lineages

scholarly journals A Low-Cost Library Construction Protocol and Data Analysis Pipeline for Illumina-Based Strand-Specific Multiplex RNA-Seq

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e26426 ◽  
Author(s):  
Lin Wang ◽  
Yaqing Si ◽  
Lauren K. Dedow ◽  
Ying Shao ◽  
Peng Liu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Low Cost ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Library Construction ◽  
Data Analysis Pipeline

Small RNA sequencing revealed dysregulated piRNAs in Alzheimer's disease and their probable role in pathogenesis

2017 ◽  
Vol 13 (3) ◽  
pp. 565-576 ◽  
Author(s):  
Jyoti Roy ◽  
Arijita Sarkar ◽  
Sibun Parida ◽  
Zhumur Ghosh ◽  
Bibekanand Mallick
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Healthy Human ◽  
Probable Role ◽  
First Time

For the first time, this study reports specific piRNA signatures in a healthy human brain and an AD-diagnosed brain and decrypted the regulatory roles of dysregulated piRNAs in the pathogenesis of AD.

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