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Sample GSM5685838

Query DataSets for GSM5685838

Status

Public on Jun 08, 2022

Title

set36.WT.4.Input.rep2

Sample type

SRA

Source name

human colorectal carcinoma cell line

Organism

Homo sapiens

Characteristics

cell line: HCT116
genotype: Wild type
molecule: RNA fragments

Treatment protocol

For the Ribo-seq samples and the RNA-seq samples, cells were treated with 100µg/mL cycloheximide for 10min

Growth protocol

HCT116 cells were cultured in DMEM with 10% fetal bovine serum (HyClone) and 1% antibiotics (Penicillin-Streptomycin, Sigma).

Extracted molecule

total RNA

Extraction protocol

Different sequencing types have different protocols, please follow the protocols in the "Materials and Methods" part in the article. Briefly, for RNA-seq, total RNAs were extracted with TRizol. For Ribo-seq, ribosome protected fragments were collected through sucrose cushion and extracted with TRizol, then the 24 to 34 nt RNA fragments were collected through 15% TBE-Urea gel. For eCLIP-seq, protein-RNA complexes were isolated with antibody. For PRO-seq, nascent RNA fragments were extractd and made library.
For RNA-seq, library construction protocol can be found here (Y. Joo et al., Topoisomerase 3beta knockout mice show transcriptional and behavioural impairments associated with neurogenesis and synaptic plasticity. Nat Commun 11, 3143 (2020).). For Ribo-seq, library was constructed using NEBNext® Multiplex Small RNA Library Prep Set for Illumina (E7300S, New England BioLabs). For eCLIP-seq, library construction protocol can be found here (E. L. Van Nostrand et al., Robust, Cost-Effective Profiling of RNA Binding Protein Targets with Single-end Enhanced Crosslinking and Immunoprecipitation (seCLIP). Methods Mol Biol 1648, 177-200 (2017).). For PRO-seq, library construction protocol can be found here (D. B. Mahat et al., Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nature Protocols 11, 1455-1476 (2016).).
RNA-seq; Ribo-seq; eCLIP-seq; PRO-seq

Library strategy

RIP-Seq

Library source

transcriptomic

Library selection

other

Instrument model

Illumina HiSeq 2000

Description

sample 95 to 98 are in one group

Data processing

For Ribo-seq, low-quality reads and the linker sequence (AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC) were removed by FASTX-Toolkit from FastQ files. Then reads less than 25 nt were also removed by FASTX-Toolkit. Align the remaining reads to an rRNA reference using the Bowtie short-read alignment program, discard the rRNA alignments and collect unaligned reads. For PRO-seq, adaptor sequences (ADAPT1=GATCGTCGGACTGTAGAACTCTGAA, ADAPT2=TGGAATTCTCGGGTGCCAAGG) were removed by cutadpt. For eCLIP-seq, adaptor sequences were also removed before mapping to the genome.
RNA-seq, Ribo-seq and eCLIP-seq reads were mapped to the hg38 human genome with HISAT2 or TopHat. PRO-seq reads were mapped to the genome using Bowtie2 allowing maximum of 2 mismatches.
For RNA-seq and Ribo-seq, the raw counts were generated using HTSeq-Counts. Then DESeq2 was used to identify the differentially expressed genes (DEGs) and generate the normalized counts. The genes with total counts (untreated and treated groups) less than 10 were filtered. The genes (Fold change > 1.5 and adjust P-value < 0.1) were identified as DEGs. The bedGraph files were generated by deepTools and normalized using RPKM. The bedGraph files were visualized using the Integrative Genomics Viewer (IGV). For PRO-seq, normalized reads at specific sub-regions were used to calculate the DEGs (fold change > 1.2-fold). eCLIP-seq reads were mapped to the genome using TopHat. Other steps were similar to the RNA-seq analysis pipeline. Genes with criterion (WT-IP_fpkm/WT-Input_fpkm > 1.5 and WT-IP_fpkm/TOP3B-KO-IP_fpkm > 1.5) in both biological replicates were selected as TOP3B eCLIP targets. RSeQC was used to analyze the read distribution of Ribo-seq, RNA-seq and eCLIP-seq on CDS, 5’UTR and 3’UTR.
Genome_build: hg38
Supplementary_files_format_and_content: bedGraph file

Submission date

Nov 10, 2021

Last update date

Jun 08, 2022

Contact name

Weidong Wang

E-mail(s)

wangw@grc.nia.nih.gov

Organization name

Shuaikun Su

Department

National Institute on Aging