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Sample GSM5974062

Query DataSets for GSM5974062

Status

Public on Apr 10, 2022

Title

Dog B - Tumor

Sample type

SRA

Source name

Osteosarcoma

Organism

Canis lupus familiaris

Characteristics

tissue: Osteosarcoma
dog breed: Rottweiler
source location: Left distal femur

Extracted molecule

total RNA

Extraction protocol

Bone: RNA extracted according to Nance et al (Nance R, Agarwal P, Sandey M, Starenki D, Koehler J, Sajib AM, et al. A method for isolating RNA from canine bone. Biotechniques. 2020 Jun;68(6):311–7). Osteosarcoma Tumor: RNA extracted using Tri-Reagent + mechanical homogenization
RNA sequencing libraries were produced with 500 ng of total RNA using the TruSeq PolyA library kit (Illumina) to deplete samples of any RNA aside from polyadenylated mRNA. They were pooled and sequenced on two lanes of the Illumina HiSeq v4 (PE, 50 bp, 25 M reads)

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 4000

Data processing

Quality - FASTQC (v 0.10.1) was used to assess the read quality
Trimming - Trimmomatic (v 0.39) was used to remove the adapters, first leading base, and reads with a minimum length of 36 bp. After trimming, approximately 32-45 million reads remained for each sample. FASTQC was used again to confirm all bases had a Phred quality score above 28.
Mapping - Reads were mapped to the indexed canine reference genome (CanFam3.1) obtained from ENSEMBL using HiSat2 (v 2.2.1) and a table of mapped read counts was generated with Stringtie (v 1.3.3)
Differential Gene Expression Analysis - DEseq2 (v 3.14) was used to identify differentially expressed genes in tumor vs. bone. A multi-factor design was used for a paired analysis to include patient ID as a random effect in the design formula (design = ~dog + tissue source) to account for differences between individuals
Filtering - Genes with less than 1 read were excluded. To extract significant DEGs while minimizing noise, the data was filtered using a false discovery rate (FDR) less than 0.05, base mean greater than 10, and log2 fold-change greater than 1 and less than −1 (corresponding to a fold-change of 2 and −2, respectively).
Individual Level Analysis - Variance stabilizing transformation (vst) function in DEseq2 was used on the significant DEGs (padj<0.05, base mean<10, log2FC>1,<-1); log2 fold-change values for each gene were derived by subtracting the variance stabilized transformed counts (on the log2 scale) of bone from tumor for each dog
Assembly: CanFam3.1
Supplementary files format and content: Gzipped, tab-delimiited text file inlcuding raw gene counts for every gene and every sample

Submission date

Mar 26, 2022

Last update date

Apr 10, 2022

Contact name

Xu Wang

E-mail(s)

xzw0070@auburn.edu

Organization name

Auburn University

Department

Pathobiology, College of Veterinary Medicine