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Sample GSM1581780

Query DataSets for GSM1581780

Status

Public on Jul 01, 2015

Title

miR-seq 7

Sample type

SRA

Source name

lung

Organism

Homo sapiens

Characteristics

histology: Lung Adenocarcinoma
lymph node status: 1
age: 61
gender: F
Stage: 1

Extracted molecule

total RNA

Extraction protocol

MicroRNA samples from fresh frozen tissue were extracted using PureLink™ microRNA Isolation Kit (Life Technologies Corporation, Carlsbad, CA). Briefly, 5 mg fresh frozen tissue samples were mixed with 300 μl Binding Buffer (L3) and homogenized using a tissue homogenizer. Homogenized samples were centrifuged, and the supernatants were mixed with 300 μl 70% ethanol. Solutions containing RNA were purified by two rounds of spin column cleanup and RNA was eluted with 50 μl sterile RNase-free water.
Total RNA samples were processed by the flashPAGE fractionator (Ambion) and flashPAGE Clean-Up Kit (Ambion). Enriched small RNA was then processed according to the SOLiD Small RNA Expression Kit protocol (Applied Biosystems) and purified small RNAs were ligated with 5' and 3' adapter Mix using RNA ligase. Ligated products (40–60 bases in length) were reverse transcribed and purified on Novex 10% TBE-Urea gel. Subsequently, 15–18 cycles of PCR were performed by amplifying the purified cDNA with barcoded PCR primer sets provided with the kit, which differed by a unique 6-nucleotide sequence. Amplified products were loaded on Novex 6% TBE gel (Invitrogen) and the gel bands containing 110 to 130bp fragments were excised. Amplified products were purified from the excised gel band and then loaded on the Applied Biosystems SOLiD next generation high throughput sequencing system for data acquisition. The quality of the samples and libraries were verified on the Agilent Bioanalyzer.

Library strategy

miRNA-Seq

Library source

transcriptomic

Library selection

size fractionation

Instrument model

AB SOLiD 4 System

Data processing

Among the total 64 tissue samples, 10 samples (6 with N0 stage and 4 with N1+ stage) were sequenced using the SOLiD platform. First, all reads were trimmed by removing the 3’ adaptor sequence using the Cutadapt program, and the length distribution of reads for each sample has been plotted . Subsequently, we used a “Sequential Trim Alignment (SeqTrimAlign)” strategy to map all reads to the hg19 reference genome in color space , in which the last one color base of those unmapped reads in the previous alignment round would be trimmed, and re-submited to the next round of alignment using Bowtie, until a minimum read length was reached, in our case 19 color bases. This strategy is believed to fit specifically for solid data and could promote a high mapping ratio without sacrificing much performance. Detection of novel microRNAs was performed by miRDeep2 using the above alignment, which utilizes the position and frequency of reads uniquely aligned to the genome (“signature”) with respect to a putative RNA hairpin and scores the microRNA candidate employing a probabilistic model based on microRNA biogenesis . The more positive the score the more reliable the prediction would be. We chose predicted microRNAs as novel candidates if they had a score >= 2.0 and were present in at least 3 samples. Applying these selection criteria 65 novel microRNAs were identified in our data set, that were then included in the following expression analysis.
Expression analysis of microRNA-Seq data was performed using the R/Bioconductor package EdgeR , which is designed for use with digital gene expression data. First, we counted the number of reads uniquely mapped to microRNA regions according to the reference database miRBase and novel microRNAs identified as above; then the normalization and differential expression between the N0 group and N1+ group was assessed in EdgeR by calculating an exact test p-value analogous to the Fisher’s exact test.
Genome_build: hg19, miRBase 19
Supplementary_files_format_and_content: raw_counts_table.txt and novel_miR_position.txt
Supplementary_files_format_and_content: edgeR_output.txt; columns: logConc: the log2-average concentration/abundance for each tag in the two groups being compared. logFC: the log2-abundance ratio (N1+ group/N0 group) p.value: exact p-value for differential expression using the NB model (refer to edgeR manual). adj.p.val: the p-value adjusted for multiple testing by Benjamini-Hochberg method.

Submission date

Jan 12, 2015

Last update date

May 15, 2019

Contact name

wei meng

E-mail(s)

wei.meng@osumc.edu

Organization name

The Ohio State University

Department

Radiation Oncology