 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Jun 05, 2021 |
| Title |
RNAseq mESC Mettl3 KO rep2 |
| Sample type |
SRA |
| |
|
| Source name |
Mettl3 KO mouse embryonic stem cells (mESC)
|
| Organism |
Mus musculus |
| Characteristics |
tissue: mouse embryonic stem cells (mESC)
cell line: mESC cells
|
| Treatment protocol |
In the case of mESCs, Mettl3 knockout (KO) cells were taken from a previous publication (PMID: 25569111).
|
| Growth protocol |
Mouse embryonic stem cells (mESC) with wildtype and Mettl3 knockout genotype were taken from a previous publication (PMID: 25569111) and cultured as described therein. The HEK293T cell line was cultured in DMEM (Life Technologies) containing 10% FBS (Life Technologies), 1% L-glutamine (Life Technologies) and 1% penicillin-streptomycin (Life Technologies) at 37°C with 5% CO2.
|
| Extracted molecule |
polyA RNA |
| Extraction protocol |
For RNA extraction from HEK293T cells and mESCs, cells were washed in ice-cold PBS and collected on ice for the isolation of total RNA using the RNeasy Plus Mini Kit (Qiagen) following the manufacturer’s recommended protocol. Poly(A)+ RNA was extracted using Oligo d(T)25 Magnetic Beads using the manufacturer’s recommended protocol. Poly(A)+ concentration was measured using Qubit™ RNA HS Assay Kit (ThermoFisher Scientific). The quality of poly(A)+ RNA was ensured using High sensitivity RNA screen tapes for the 2200 Tape station system (Agilent). If a predominant peak for ribosomal RNA was still detectable an additional round of poly(A) selection was performed. Mouse heart RNA was purchased from Takara Clontech (order number 636202): RNA isolated by a modified guanidinium thiocyanate method followed by poly A+ RNA selection with two rounds of oligo(dT)-cellulose columns.
RNA-seq: NGS library prep was performed with Illumina's TruSeq stranded mRNA LT Sample Prep Kit following Illumina’s standard protocol beginning with fragmentation step (Part # 15031047 Rev. E). Libraries were prepared with a starting amount of 73ng for imb_koenig_2019_21 and amplified in 10 PCR cycles.Libraries were profiled in a DNA 1000 on a 2100 Bioanalyzer (Agilent technologies) and quantified using the Qubit dsDNA HS Assay Kit, in a Qubit 2.0 Fluorometer (Life technologies). All 6 samples were pooled in equimolar ratio and sequenced on 1 NextSeq 500 Highoutput Flowcell, SR for 1x 84 cycles plus 1x 7 cycles for the index read.
miCLIP2: Briefly, 1 µg of polyA selected and fragmented RNA was incubated with an m6A antibody [Synaptic Systems, 202 003] and crosslinked twice at 150 mJ/cm² at 254 nm followed by immunoprecipitation with protein A Dynabeads. After stringent washing, the 3’ end of the RNA was dephosphorylated using T4 PNK and subsequently pre-adenylated L3-App linker was ligated. The 5’ end was radioactively labelled and the samples were purified by SDS PAGE and transferred to a nitrocellulose membrane. After exposure to a Fuji film, the protein-RNA complexes were isolated and elution of the RNA was accomplished by proteinase K treatment, phenol-chloroform extraction and ethanol precipitation. Reverse transcription was perform for cDNA synthesis using RT primers against the L3-App linker. The cDNA was purified using MyONE Silane bead clean up system and the 5’ linker was ligated. A pre-amplification PCR with 6 cycles was performed and subsequently the samples were size-selected using ProNex beads. To prevent overamplification of the library, the PCR cycles were optimised to a minimum. For the preparative PCR, the minimum amount of PCR cycles was used followed by a second size selection step using ProNex beads. Libraries were profiled in a High Sensitivity D1000 ScreenTape on a Tape station 2200 (Agilent) and quantified using the Qubit dsDNA HS Assay Kit, in a Qubit 2.0 Fluorometer (Life technologies). All samples in an experiment were pooled in equimolar ratio and sequenced on one NextSeq 500 High Output Flowcell, 75 cycle kit.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
poly(A)+ RNA
|
| Data processing |
miCLIP data processing:
Reads were mapped to the respective genome and its annotation (GENCODE release 31 for all human samples, GENCODE release M23 for all mouse samples) using STAR (v2.7.3a). When running STAR, up to 4% mismatches were allowed per read (--outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999), soft-clipping was prohibited on the 5'end of reads (--alignEndsType Extend5pOfRead1) and only uniquely mapping reads were kept for further analysis (--outFilterMultimapNmax 1).
Following mapping, BAM files were sorted and indexed (SAMtools v1.9) and duplicate reads were removed (UMI-tools v1.0.0).
After removing duplicates, all mutations found in reads were extracted using the Perl script parseAlignment.pl of the CLIP Tool Kit (CTK, v1.1.3). The list was filtered for C-to-T mutations using basic Bash commands and kept in BED file format. Based on the filtered list of C-to-T mutations, deduplicated reads were separated into two BAM files holding reads with and without C-to-T mutation, respectively, using SAMtools and basic Bash commands. The BAM file of reads without C-to-T mutation was transformed to a BED file using bedtools bamtobed (BEDTools v2.27.1) and considering only the 5' mapping position of each read. Afterwards, the BED file was sorted and summarized to strand-specific bedGraph files which were shifted by one base pair upstream (since this nucleotide is considered as the crosslinked nucleotide) using bedtools genomecov (BEDTools v2.27.1). Similarly, the BED files of C-to-T mutations were also sorted and summarized to strand-specific bedGraph files using bedtools genomecov. Finally, all bedGraph files were transformed to bigWig track files using bedGraphToBigWig of the UCSC tool suite (v365).
RNAseq data processing:
Reads were mapped to the mouse genome and its annotation based on GENCODE release M23 using STAR (v2.6.1b). When running STAR, up to 4% mismatches were allowed per read (--outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999). SAMtools was used to remove secondary alignments and keep only one location for multimapping reads (-F 256).
For differential expression analysis, mapped reads were counted with htseq-count (v0.12.4, -s reverse) into gene annotation based on GENCODE release M23 [PMID: 25260700].
Genome_build: mm10 (GRCm38.p6), hg38 (GRCh38.p12)
Supplementary_files_format_and_content: *.C2T.plus.bw, *.C2T.minus.bw: bigwig files of positions with C-to-T mutations and their counts. They were generated by filtering for C-to-T mutations in the list of all found mutations, which were output of the Perl script parseAlignment.pl of the CLIP Tool Kit (CTK). The filtered list was in BED file format and was then sorted and summarized to strand-specific bedGraph files using bedtools genomecov, which were then transformed to bigWig track files using bedGraphToBigWig of the UCSC tool suite.
Supplementary_files_format_and_content: *.noC2T.plus.bw, *.noC2T.minus.bw: bigwig files of positions upstream of reads without C-to-T mutation and their counts. They were generated by first removing all reads with C-to-T mutation from the BAM files and then transforming the resulting BAM file to a BED file using bedtools bamtobed considering only the 5' mapping position of each read. The BED file was then sorted and summarized to strand-specific bedGraph files which were shifted by one base pair upstream using bedtools genomecov. bedGraph files were then transformed to bigWig track files using bedGraphToBigWig of the UCSC tool suite.
Supplementary_files_format_and_content: The samples miCLIP_mESC_WT_1 and miCLIP_mESC_Mettl3_KO_1 were sequenced in two technical replicates (miCLIP_mESC_WT_1_1 + miCLIP_mESC_WT_1_2 and miCLIP_mESC_Mettl3_KO_1_1 + miCLIP_mESC_Mettl3_KO_1_2), for which we provide separate raw data files (fastq.gz), but have merged their bigwig tracks after processing into miCLIP_mESC_WT_1.merged... and miCLIP_mESC_Mettl3_KO_1.merged...
Supplementary_files_format_and_content: *.txt: count tables of reads mapping to genes generated by counting reads with htseq-count (v0.12.4, -s reverse) into gene annotation based on GENCODE release M23 [PMID: 25260700].
Supplementary_files_format_and_content: *.bed: bed files with predicted m6A sites by m6Aboost. In brief, bam files with reads without C-to-T mutation were used for peak calling with PureCLIP (v1.3.1) individually on each replicate for each condition. PureCLIP significant sites per replicate were then filtered for presence in at least two replicates for a given condition. m6Aboost was applied to the reproducible peaks to predict m6A sites.
|
| |
|
| Submission date |
Dec 18, 2020 |
| Last update date |
Jun 05, 2021 |
| Contact name |
Kathi Zarnack |
| E-mail(s) |
kathi.zarnack@uni-wuerzburg.de
|
| Organization name |
University Würzburg
|
| Department |
Theodor Boveri Institute
|
| Street address |
Biocenter, Am Hubland
|
| City |
Würzburg |
| ZIP/Postal code |
97074 |
| Country |
Germany |
| |
|
| Platform ID |
GPL19057 |
| Series (2) |
| GSE163491 |
Deep and accurate detection of m6A RNA modifications in human and mouse cells using miCLIP2 and m6Aboost machine learning [a] |
| GSE163500 |
Deep and accurate detection of m6A RNA modifications in human and mouse cells using miCLIP2 and m6Aboost machine learning |
|
| Relations |
| BioSample |
SAMN17113325 |
| SRA |
SRX9702478 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4980028_RNAseq_mESC_Mettl3_KO_2.counts.txt.gz |
222.0 Kb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
|
|
|
|
 |
