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| Status |
Public on Feb 02, 2015 |
| Title |
Carrier 16 |
| Sample type |
SRA |
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| Source name |
Whole blood, carrier
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| Organism |
Homo sapiens |
| Characteristics |
tissue: peripheral whole blood
age: 53.89
gender [0=male, 1=female]: 0
cag_repeats: 40
mutation_carrier_status: carrier
presymptomatic hd patient (yes, if motor score is 5 or less): no
motor_score [higher score indicates greater disease severity]: 7
hb_percentage [a proxy for the reticulocyte content of each sample measured as the ratio of hemoglobin tags versus total aligned tags per sample (0-100%)]: 73.88
tfc_score: 13
tfc_disease_stage: 1
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| Extracted molecule |
total RNA |
| Extraction protocol |
Whole blood was drawn into PAX gene tubes and total RNA was isolated using the PAX RNA isolation kit following the manufacturer's instructions, including DNAse treatment
SAGE libraries were produced according to the Illumina 3' Digitial Gene Expression NlaIII protocol
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2000 |
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| Description |
Sample.49
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| Data processing |
Illumina GA Pipeline Software (version 1.5.1) was used for data sequence processing.
The FASTQ files were analyzed using the open source GAPSS_B pipeline.
All sequences were trimmed to 17 base pairs and the NlaIII recognition site (CATG) was added to the 5’ end of the sequence to create the complete 21 mer.
Sequences were aligned using the Bowtie short read aligner (version 0.12.7) against the UCSC hg19 reference genome.
A custom Perl script was used to obtain gene annotations from Biomart.
A custom python script was used to count the tags in each Ensembl gene using the sam output files from bowtie.
Normalization steps:
library(limma)
library(edgeR)
library("org.Hs.eg.db")
library(biomaRt)
#Remove HBA1,HBA2,HBB genes
sumrow=apply(Data,1,sum)
sumroworder=order(sumrow,decreasing=T)
head(sumroworder)
Data=Data[-c(26806,12722,10749),]
#Remove low abundance genes
Data=Data[rowSums(Data)>123,]
#Get HGNC symbols from ensembl
mart = useMart("ensembl", dataset="hsapiens_gene_ensembl")
ann <- getGene(id = rownames(Data), type = "ensembl_gene_id", mart = mart)
m <- match(rownames(Data),ann[,9])
genes <- ann[m,1:8]
#Make the limma design matrix
design=model.matrix(~Metadata$Motor_Score+Metadata$HB_Percentage+Metadata$Gender+Metadata$Age)
design=model.matrix(~Metadata$ TFC_Disease_Stage+Metadata$HB_Percentage+Metadata$Gender+Metadata$Age)
#Do Limma
nf=calcNormFactors(Data)
y=voom(Data,design,plot=TRUE,lib.size=colSums(Data)*nf)
y$genes <- genes
fit=lmFit(y,design)
fit=eBayes(fit)
summary(decideTests(fit))
#Make a table of the genes and export it
Topmodel=topTable(fit,coef=2,n=20,sort.by="p")[,c(1,2,3,9,13)]
write.table(Topmodel, file="Topmodel.txt")
Genome_build: hg19
Supplementary_files_format_and_content: Data.txt: Tab-delimited text file representing tag counts for all 124 samples and in each Ensembl gene using the sam output files from bowtie.
Supplementary_files_format_and_content: Normalised_data.txt: Tab-delimited text file representing scaled data (log transformed and normalized). Obtained from the y$E function in the voom limma package for RNA-seq data analysis.
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| Submission date |
Oct 28, 2013 |
| Last update date |
May 15, 2019 |
| Contact name |
Anastasios Mastrokolias |
| Phone |
0031715269425
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| Organization name |
Leiden University Medical Center
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| Street address |
LUMC, Building 2 Einthovenweg 20 2333 ZC Leiden
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| City |
Leiden |
| ZIP/Postal code |
2333 ZC |
| Country |
Netherlands |
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| Platform ID |
GPL11154 |
| Series (1) |
| GSE51799 |
Huntington’s disease biomarker progression profile identified by transcriptome sequencing in peripheral blood |
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| Relations |
| BioSample |
SAMN02385331 |
| SRA |
SRX2581811 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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