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Sample GSM2449444

Query DataSets for GSM2449444

Status

Public on Feb 06, 2019

Title

Patient4-T1-Normal

Sample type

SRA

Source name

purified CD14+ monocytes from ACLF patient, in vitro cultured in the presence of pool of normal human serum

Organism

Homo sapiens

Characteristics

disease: acute-on-chronic liver failure (ACLF)
gender: male
age: 50
cell type: purified CD14+ monocytes
treatment: in vitro cultured in the presence of pool of normal human serum

Growth protocol

CD14 + cells were cultured in RPMI medium supplemented with 20% of a pool of normal human serum obtained from healthy blood donors

Extracted molecule

total RNA

Extraction protocol

Rneasy kit (Qiagen)
Per sample, an amount of 100 ng of total RNA was used as input. Using the Illumina TruSeq® Stranded mRNA Sample Prep Kit (protocol 15031047 Rev E October 2013) poly-A containing mRNA molecules were purified from the total RNA input using poly-T oligo-attached magnetic beads. In a reverse transcription reaction using random primers, RNA was converted into first strand cDNA and subsequently converted into double-stranded cDNA in a second strand cDNA synthesis reaction using DNA PolymeraseI and RNAse H. The cDNA fragments were extended with a single ‘A’ base to the 3’ ends of the blunt-ended cDNA fragments after which multiple indexing adapters were ligated introducing different barcodes for each sample. Finally, enrichment PCR was carried out to enrich those DNA fragments that have adapter molecules on both ends and to amplify the amount of DNA in the library. Sequence-libraries of each sample were equimolarly pooled and sequenced on an Illumina NextSeq 500 instrument (High Output, 75 bp, Single Reads, v2) at the VIB Nucleomics core (www.nucleomics.be).

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NextSeq 500

Description

Patient-M-12h-Normal

Data processing

Read preprocessing: Four fastq-files per sample (suffix L001, L002, L003, L004) were generated as the NextSeq has 4 lanes. Low quality ends and adapter sequences were trimmed off from the Illumina reads with FastX 0.0.14 and Cutadapt 1.7.1 (HannonLab, 2010; Martin, 2011). Subsequently, small reads (length < 35 bp), polyA-reads (more than 90 % of the bases equal A), ambiguous reads (containing N), low-quality reads (more than 50 % of the bases < Q25) and artifact reads (all but three bases in the read equal one base type) were filtered using using FastX 0.0.14 and ShortRead 1.24.0 (Morgan et al, 2009). With Bowtie2 2.2.4 we identified and removed reads that align to phix_illumina (Langmead & Salzberg, 2012).
Mapping: The preprocessed reads were aligned with STAR aligner v2.4.1d to the reference genome of Homo sapiens (GRCh37.73) (Dobin et al, 2013). Default STAR aligner parameter settings were used, except for ‘outSAMprimaryFlag=OneBestScore’, twopassMode=Basic’, ‘alignIntronMin=50‘, ‘alignIntronMax=500,000’, ‘outSAMtype=BAM SortedByCoordinate’. Using Samtools 1.1, reads with a mapping quality smaller than 20 were removed from the alignments (Li et al, 2009). Alignments from the four lanes were merged per sample.
Counting: The number of reads in the alignments that overlap with gene features were counted with featureCounts 1.4.6 (Liao et al, 2014). Following parameters were chosen: -Q 0 –s 2 –t exon –g gene_id.
Differential expression analysis: We removed genes for which all samples had less than 1 count-per-million. The counts were further corrected within samples for GC-content and between samples using full quantile normalization, as implemented in the EDASeq package from Bioconductor (Risso et al, 2011). With the EdgeR 3.8.6 package of Bioconductor, a negative binomial generalized linear model (GLM) was fitted against the normalized counts (Robinson et al, 2007). We did not use the normalized counts directly, but worked with offsets. Differential expression was tested for with a GLM likelihood ratio test, also implemented in the EdgeR package. The resulting p-values were corrected for multiple testing with Benjamini-Hochberg to control the false discovery rate (Benjamini & Hochberg, 1995).
Genome_build: GRCh37
Supplementary_files_format_and_content: RawCounts.xlsx contains the values after counting (integer numbers). The first column contains Ensembl gene IDs.

Submission date

Jan 06, 2017

Last update date

May 15, 2019

Contact name

Rekin's Janky

E-mail(s)

Nucleomics.Bioinformatics@vib.be

Organization name

VIB

Department

Nucleomics Core