 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Jul 18, 2014 |
| Title |
human_Exp2siMETTL3_input |
| Sample type |
SRA |
| |
|
| Source name |
human_Exp2siMETTL3_input
|
| Organism |
Homo sapiens |
| Characteristics |
knockdown: siMETTL3
cell line: A549
antibody: none
|
| Extracted molecule |
polyA RNA |
| Extraction protocol |
RNA was extracted from cells using a standard hot acid phenol protocol. Briefly, cell pellets were resuspended in equal volumes of acid phenol:chloroform 5:1 pH 4.3-4.7 (Sigma), buffer AE (50 mM sodium acetate, 10mM EDTA 1% SDS) and glass beads. This mixture was vortexed for 15 minutes, followed by a 15 minute incubation at 65°C. Samples were centrifuged for 10 minutes (12,000g, 4°C); the supernatant isolated, re-extracted with phenol:chloroform::5:1, and precipitated with sodium acetate and isopropanol. Enrichment of polyadenylated RNA (polyA+ RNA) from total RNA was performed using Oligo(dT) dynabeads (Invitrogen) according to the manufacturer’s protocol. The mRNA was chemically fragmented into ~80-nt-long fragments using RNA fragmentation reagent (Ambion). The sample was then subjected to Turbo DNAse treatment (Ambion), followed by a phenol-chloroform extraction, and resuspension in 20 μl of IPP buffer (150 mM NaCl, 0.1% NP-40, 10 mM Tris-HCl, pH 7.5). 25 μl of protein-G magnetic beads were washed and resuspended in 200 μl of IPP buffer, and tumbled with 3 μl of affinity purified anti-m6A polyclonal antibody (Synaptic Systems) at room temperature for 30 minutes. Following 2 washes in IPP buffer, RNA was added to the antibody-bead mixture, and incubated for 2 h at 4°C. The RNA was then washed twice in 200 μl of IPP buffer, twice in low-salt IPP buffer (50 mM NaCl, 0.1% NP-40, 10 mM Tris-HCl, pH 7.5), and twice in high-salt IPP buffer (500 mM NaCl, 0.1% NP-40, 10 mM Tris-HCl, pH 7.5), and eluted in 30 μl RLT (Qiagen). To purify the RNA, 20 μl MyOne Silane Dynabeads (Life Technologies) were washed in 100 μl RLT, resuspended in 30 μl RLT, and added to the eluted RNA. 60 μl 100% ethanol was added to the mixture, the mixture attached to the magnet and the supernantant discarded. Following two washes in 100 μl of 70% ethanol, the RNA was eluted from the beads in 160 μl IPP buffer. Eluted RNA was subjected to an additional round of IP, by re-incubating it with protein-A magnetic beads coupled to anti-m6A antibody, followed by washes, elution from the protein-A beads and purification as above, followed by elution from the MyOne silane dynabeads in 10 μl H20.
Strand-specific m6A RNA-seq libraries were generated as described in (Engreitz et al., 2013). Briefly, RNA was first subjected to FastAP Thermosensitive Alkaline Phosphatase (Thermo Scientific), followed by a 3’ ligation of an RNA adapter using T4 ligase (New England Biolabs). Ligated RNA was reverse transcribed using AffinityScript Multiple Temperature Reverse Transcriptase (Agilent), and the cDNA was subjected to a 3’ ligation with a second adapter using T4 ligase. The single-stranded cDNA product was then amplified for 9-14 cycles in a PCR reaction. Libraries were sequenced on Illumina Miseq, HiSeq 2000 and/or HiSeq 2500 platforms generating paired end reads (25 or 30 bp from each end, depending on the platform).
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 2500 |
| |
|
| Data processing |
Reads were initially mapped against a set of human or mouse ribosomal RNA (rRNA) sequences using Bowtie (version 0.12.7), and all reads aligning to the rRNA were discarded. All remaining reads were aligned against the human (hg19) or mouse (mm9) genome using Tophat (version 1.4.1). Parameters used were ‘--max-multihits 1 –prefilter-multihits’ and ‘–transcriptome-index’, for which we assigned a pre-indexed version of the relevant transcriptomes, based on the UCSC Known Genes set of annotations. An in-house script was then used to project all reads aligning to the genome upon the human and mouse transcriptomes. Only reads fully matching a transcript structure, as defined by the ‘UCSC Known Genes’ transcriptome annotation, were retained. Such reads were computationally extended in transcriptome space from the beginning of the first read to the end of its mate, and coverage in transcriptome-space was calculated for each nucleotide across all transcripts.
Putative m6A sites were identified using a 3 step-approach, consisting of: (1) Examination of the IP sample, to identify regions within genes in the IP samples that were enriched in comparison to background gene levels; (2) Comparison of IP sample with input sample, to ensure that these regions were not enriched in corresponding input samples; and (3) Comparison across multiple replicates of IP and input samples. Below we provide a detailed description of these steps, which is very similar to the approach used in (Schwartz et al., 2013), with a few extensions and modifications.
(1) Peak detection within genes. To search for enriched peaks in the m6A IP samples, we scanned each gene using sliding windows of 100 nucleotides with 50 nucleotides overlap. Each window was assigned a Peak Over Median (POM) score, defined as mean coverage in the window / median coverage across the gene. Windows with POM scores greater than 4 (i.e., greater than 4-fold enrichment) and with a mean coverage >10 reads were retained. Overlapping windows were merged together, and for each disjoint set of windows in transcriptome space we recorded its start, end, and peak position, corresponding to the position with the maximal coverage across the window.
(2) Ensuring that peaks were absent in input. We repeated the procedure in step (1) for the input sample. We eliminated from all subsequent analysis all windows that were detected in both step (1) and (2).
(3) Comparison of multiple samples and criteria for WTAP-dependence. To compare between different perturbations and/or conditions, we applied the following strategy. We first merged the coordinates of all windows from all samples passing step (1) and (2), to define a set of disjoint windows passing these filters in at least one of the samples. For each such window, we recalculated the peak start, end, peak position, and POM score (as defined above) across each of the samples using the approach in step (1). In addition, for each window we calculated a Peak Over Input (POI) score, corresponding to the fold-change of coverage across the window in the IP sample over the coverage in the input sample. To account for differences in sample depth, we estimated the mean difference between IP and input samples across the 500 most highly expressed genes, which we used as an estimate for background. We subtracted this background from the POI score. Of note, the POM and POI scores generally correlated well with each other; nevertheless, we empirically found that in some cases it was more informative to filter based on the one than on the other.
The provided bed files are provided to facilitate easy browsing of the data. The were produced by first extending each read until the end of its mate in genomic space. Coverage was then calculated individually for the + and - strands using bedtools. The bed files were not used for the data analysis. TDF files, similarly provided for facilitating browsing via IGV genome browser, were generated using the 'count' command in IGV tools, with parameters `-z 7 -w 5 -e 70`. The tdf files (binary format, summarizing coverage genome-wide) were used for the data analysis.
Genome_build: hg19
Supplementary_files_format_and_content: .bed
Supplementary_files_format_and_content: .tdf
|
| |
|
| Submission date |
Mar 04, 2014 |
| Last update date |
May 15, 2019 |
| Contact name |
Schraga Schwartz |
| Organization name |
WEIZMANN INSTITUTE OF SCIENCE
|
| Street address |
Herzl 234, Department of Molecular Genetics
|
| City |
Rehovot |
| State/province |
Choose a State or Province |
| ZIP/Postal code |
7610001 |
| Country |
Israel |
| |
|
| Platform ID |
GPL16791 |
| Series (2) |
| GSE54365 |
High-resolution mapping reveals a conserved, widespread, dynamic meiotically regulated mRNA methylation program |
| GSE55572 |
High-resolution mapping reveals a conserved, widespread, dynamic meiotically regulated mRNA methylation program [Hs] |
|
| Relations |
| BioSample |
SAMN02673155 |
| SRA |
SRX481040 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM1339436_human_Exp2siMETTL3_input.tdf |
63.9 Mb |
(ftp)(http) |
TDF |
| GSM1339436_human_Exp2siMETTL3_input_minus.bed.gz |
16.6 Mb |
(ftp)(http) |
BED |
| GSM1339436_human_Exp2siMETTL3_input_plus.bed.gz |
17.3 Mb |
(ftp)(http) |
BED |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
|
|
|
|
 |
