GEO help: Mouse over screen elements for information.
|Public on Feb 04, 2020
|U2OS/MTX300 reference cells
|osteosarcoma cell U2OS/MTX300
|cell line: U2OS/MTX300
cell type: osteosarcoma cell
|U2OS/MTX300 cell was transfected with Vector, CBX4, shControl, shCBX4_1 or shCBX4_2, then these sample were used for the global transcriptomes analysis.
|The base medium for this cell line is ATCC-formulated McCoy's 5a Medium Modified, Catalog No. 30-2007. To make the complete growth medium, add the following components to the base medium: fetal bovine serum to a final concentration of 10%.
|Total RNA was extracted using TRIZOL Reagent (Cat#15596-018，Life technologies, Carlsbad, CA, US) following the manufacturer’s instructions and checked for a RIN number to inspect RNA integration by an Agilent Bioanalyzer 2100 (Agilent technologies, Santa Clara, CA, US).Qualified total RNA was further purified by RNeasy mini kit (Cat#74106, QIAGEN, GmBH, Germany) and RNase-Free DNase Set (Cat#79254, QIAGEN, GmBH, Germany).. RNA was quantified using a NanoDrop ND-100 UV-VIS spectrophotometer
RNA libraries were prepared for sequencing using standard Illumina protocols
|Illumina NovaSeq 6000
|Gene expression of U2OS/MTX300 transfected with pSIN-Vector
|The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated.
Raw data (raw reads) of fastq format were firstly processed through in-house perl scripts. In this step, clean data (clean reads) were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. All the downstream analyses were based on the clean data with high quality.
Reference genome and gene model annotation files were downloaded from genome website directly. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. We selected Hisat2 as the mapping tool for that Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus a better mapping result than other non-splice mapping tools.
featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels
Supplementary_files_format_and_content: .txt files include read counts, FPKM values, log2 fold change, p-value and padj for each Sample.
|Nov 21, 2019
|Last update date
|Feb 04, 2020
|Sun Yat-sen University Cancer Center
|Dongfengdong road 651