This guide aims at offering a quick-start on how to access the data sets provided by m6AConquer database. The results provided by m6AConquer consist two parts: Multi-Omics data framework and IDR analysis-based integration result. We will show you how to load and extract data from the downloaded files sequentially.
m6AConquer provides MultiAssayExperiment (multi-omics), SummarizedExperiment (single-omics) and RaggedExperiment (single-omics) files for sharing quantitative m6A data. The MultiAssayExperiment (MAE) contains 4 types of omics data: m6A methylome, transcriptomics, genetic variants, and alernative splicing data. You are free to use any of them.
To access the site calling results in MAE file format, please download the corresponding R object from the MultiAssayExperiment column. As an illustration, here we use the site calling result from eTAM-seq sequencing data. First, load the SummarizedExperiment and MultiAssayExperiment packages and read the file into R:
suppressWarnings(suppressPackageStartupMessages(library(SummarizedExperiment)))
suppressWarnings(suppressPackageStartupMessages(library(MultiAssayExperiment)))
suppressWarnings(suppressPackageStartupMessages(library(RaggedExperiment)))
MAE <- readRDS("eTAM_hg38_WT_MAE.rds")
MAE## A MultiAssayExperiment object of 4 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 4:
## [1] m6AOmics: RangedSummarizedExperiment with 2788705 rows and 4 columns
## [2] TranscriptOmics: RangedSummarizedExperiment with 58650 rows and 4 columns
## [3] SplicingEvents: RangedSummarizedExperiment with 671257 rows and 4 columns
## [4] GeneticVariation: RaggedExperiment with 46 rows and 4 columns
## Functionality:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DataFrame
## sampleMap() - the sample coordination DataFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DataFrame
## assays() - convert ExperimentList to a SimpleList of matrices
## exportClass() - save data to flat filesThe MAE object contains both the 4 levels of omics data.
experiments(MAE)## ExperimentList class object of length 4:
## [1] m6AOmics: RangedSummarizedExperiment with 2788705 rows and 4 columns
## [2] TranscriptOmics: RangedSummarizedExperiment with 58650 rows and 4 columns
## [3] SplicingEvents: RangedSummarizedExperiment with 671257 rows and 4 columns
## [4] GeneticVariation: RaggedExperiment with 46 rows and 4 columnsTo access the m6A methylome data, you may extract the
m6AOmics experiment:
m6AOmics <- experiments(MAE)[["m6AOmics"]]
m6AOmics## class: RangedSummarizedExperiment
## dim: 2788705 4
## metadata(1): OmixM6A_para
## assays(4): m6A Total m6ASiteProb AdjPvalue
## rownames: NULL
## rowData names(2): high_confidence_union high_confidence_specific
## colnames(4): SRR21070403 SRR21070404 SRR21070405 SRR21070406
## colData names(10): SampleID Title ... CurationDate BioSampleThis is basically a SummarizedExperiment object with four assay matrices:
m6A: m6A read counts
Total: Total read counts
m6ASiteProb: m6A site probabilities as normalized m6A levels
AdjPvalue: Benjamini-Hochberg method corrected p-values (FDR) returned by sample-specific site calling
For each m6A profiling techniques, the m6A read counts and total read counts were extracted as below:
| Category | m6A read counts | Total read counts | |
|---|---|---|---|
| GLORI | Chemical-assisted | Counts of unconverted A bases at candidate sites | Total read coverages covering the candidate sites |
| DART-seq | Enzyme-assisted | Counts of U bases converted from C bases at one base downstream of candidate sites | Total read coverages covering the candidate sites |
| eTAM-seq | Enzyme-assisted | Counts of unconverted A bases at candidate sites | Total read coverages covering the candidate sites |
| m6A-REF-seq | Enzyme-assisted | Counts of undigested ACA motifs covering the candidate sites with the first A base | Counts of digested and undigested ACA motifs covering the candidate sites with the first A base |
| m6A-SAC-seq | Enzyme-assisted | Counts of bases mismatching the candidate sites | Total read coverages covering the candidate sites |
| MAZTER-seq | Enzyme-assisted | 3’ read-end coverages at the first A base of the ACA motif covering the candidate sites | Total base coverages at the first A base of the ACA motif covering the candidate sites |
| scDART-seq | Enzyme-assisted | Counts of U bases converted from C bases at one base downstream of candidate sites | Total read coverages covering the candidate sites |
| MeRIP-seq | Antibody-assisted | Counts of IP reads covering the candidate sites | Counts of both IP and input reads covering the candidate sites |
| m6ACE-seq | Antibody-assisted | Counts of digested reads covering the candidate sites | Counts of both digested and input reads covering the candidate sites |
| Oxford-nanopore | Direct RNA sequencing | Counts of m6A reads identified by m6Anet covering the candidate sites | Total read coverages covering the candidate sites |
If you have downloaded the SE objects from the corresponding columns, you can also use the codes on MAE experiments to extract data of interest.
Each of these assays can be access as follow:
## Here, we extract the sites with total m6A coverages across various samples larger than 30
index <- rowSums(assays(m6AOmics)[["m6A"]]) > 30
head(assays(m6AOmics)[["m6A"]][index,])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## [1,] 23 9 12 14
## [2,] 32 19 27 37
## [3,] 11 15 23 26
## [4,] 15 20 20 27
## [5,] 8 13 23 24
## [6,] 36 54 52 90head(assays(m6AOmics)[["Total"]][index,])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## [1,] 925 614 924 2024
## [2,] 2473 3693 4103 7044
## [3,] 3158 4628 5032 8219
## [4,] 3222 4795 5035 8236
## [5,] 3285 4790 5042 8168
## [6,] 3277 4740 4900 7884head(assays(m6AOmics)[["m6ASiteProb"]][index,])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## [1,] 0.1306800 0.1318108 0.1334797 0.1410390
## [2,] 0.1199664 0.1549337 0.1458592 0.1509339
## [3,] 0.1795600 0.1844007 0.1623979 0.1790439
## [4,] 0.1591554 0.1669527 0.1702940 0.1767272
## [5,] 0.2101689 0.1984438 0.1625029 0.1839964
## [6,] 0.1223861 0.1285718 0.1330076 0.1292391head(assays(m6AOmics)[["AdjPvalue"]][index,])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## [1,] 1 1 1 1
## [2,] 1 1 1 1
## [3,] 1 1 1 1
## [4,] 1 1 1 1
## [5,] 1 1 1 1
## [6,] 1 1 1 1To access the expression levels of eTAM-seq sequencing data, you may
extract the TranscriptOmics experiment:
TranscriptOmics <- experiments(MAE)[["TranscriptOmics"]]
TranscriptOmics## class: RangedSummarizedExperiment
## dim: 58650 4
## metadata(0):
## assays(2): ReadCounts RPKM
## rownames(58650): ENSG00000223972 ENSG00000227232 ... ENSG00000231514
## ENSG00000235857
## rowData names(6): gene_id gene_name ... symbol entrezid
## colnames(4): SRR21070403 SRR21070404 SRR21070405 SRR21070406
## colData names(10): SampleID Title ... CurationDate BioSampleThis SE only contains two assay, which are the original read counts on all the genes and the normalized RPKM:
head(assays(TranscriptOmics)[["ReadCounts"]])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## ENSG00000223972 0 0 0 0
## ENSG00000227232 164 105 118 162
## ENSG00000278267 0 0 0 0
## ENSG00000243485 0 0 0 0
## ENSG00000237613 0 0 0 0
## ENSG00000268020 0 0 0 0head(assays(TranscriptOmics)[["RPKM"]])## SRR21070403 SRR21070404 SRR21070405 SRR21070406
## ENSG00000223972 0.000000 0.000000 0.000000 0.000000
## ENSG00000227232 3.259619 2.344645 2.467891 2.497534
## ENSG00000278267 0.000000 0.000000 0.000000 0.000000
## ENSG00000243485 0.000000 0.000000 0.000000 0.000000
## ENSG00000237613 0.000000 0.000000 0.000000 0.000000
## ENSG00000268020 0.000000 0.000000 0.000000 0.000000Similarly, to access the alternative splicing data and genetic
variant data, you can extract the SplicingEvents and
GeneticVariation experiment respectively.
SplicingEvents <- experiments(MAE)[["SplicingEvents"]]
SplicingEvents## class: RangedSummarizedExperiment
## dim: 671257 4
## metadata(0):
## assays(6): ReadCounts SE ... A5SS A3SS
## rownames(671257): ENSG00000290825:E001 ENSG00000290825:E002 ...
## ENSG00000210194:E001 ENSG00000210196:E001
## rowData names(7): tx_id tx_name ... exon_rank exonic_part
## colnames(4): SRR21070403 SRR21070404 SRR21070405 SRR21070406
## colData names(10): SampleID Title ... CurationDate BioSampleGeneticVariation <- experiments(MAE)[["GeneticVariation"]]
GeneticVariation## class: RaggedExperiment
## dim: 46 4
## assays(10): paramRangeID REF ... GQ PL
## rownames(46): rs1279138
## chr12:6770904_TCCCTTTTTGTATAATTTAATAAAGAAATGGTCGCGCTTCTGTTTTTAACCTGTCTCCTGCTTTCCCGGGGGCTCCAGTCAGTGCGACAAAAGGGTAGAGAGGGAGGAGGGTGGCTGACCTCCCATTCTGCCAGGA/T
## ... rs10096642 rs6478689
## colnames(4): SRR21070403 SRR21070404 SRR21070405 SRR21070406
## colData names(10): SampleID Title ... CurationDate BioSampleTo access the consistent genomic ranges used to count m6A and total
read coverages, you can extract the Rowrange data from
m6AOmics:
rowRanges(m6AOmics)## GRanges object with 2788705 ranges and 2 metadata columns:
## seqnames ranges strand | high_confidence_union
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr1 11873 + | 0
## [2] chr1 11896 + | 0
## [3] chr1 11940 + | 0
## [4] chr1 12017 + | 0
## [5] chr1 12147 + | 0
## ... ... ... ... . ...
## [2788701] chrM 14324 - | 0
## [2788702] chrM 15023 - | 0
## [2788703] chrM 15413 - | 0
## [2788704] chrM 15639 - | 0
## [2788705] chrM 15968 - | 0
## high_confidence_specific
## <numeric>
## [1] 0
## [2] 0
## [3] 0
## [4] 0
## [5] 0
## ... ...
## [2788701] 0
## [2788702] 0
## [2788703] 0
## [2788704] 0
## [2788705] 0
## -------
## seqinfo: 25 sequences (1 circular) from hg38 genomeThe metacolumn in the rowrange data of m6AOmics are the
dummy variables indicating if the sites are supported by technical
orthogonal integration. high_confidence_union indicates if
the site is supported by integration of any orthogonal technique pair,
namely high-confidence m6A sites. high_confidence_specific
indicates if the site is supported by the integration of any orthogonal
technique pair covering current technqiue.
Similarly, to access the genomic locations of all the genes and
exonic parts, you can extract the Rowrange from
TranscriptOmics and SplicingEvents
respectively:
rowRanges(TranscriptOmics)## GRanges object with 58650 ranges and 6 metadata columns:
## seqnames ranges strand | gene_id
## <Rle> <IRanges> <Rle> | <character>
## ENSG00000223972 chr1 11869-14409 + | ENSG00000223972
## ENSG00000227232 chr1 14404-29570 - | ENSG00000227232
## ENSG00000278267 chr1 17369-17436 - | ENSG00000278267
## ENSG00000243485 chr1 29554-31109 + | ENSG00000243485
## ENSG00000237613 chr1 34554-36081 - | ENSG00000237613
## ... ... ... ... . ...
## ENSG00000224240 chrY 26549425-26549743 + | ENSG00000224240
## ENSG00000227629 chrY 26586642-26591601 - | ENSG00000227629
## ENSG00000237917 chrY 26594851-26634652 - | ENSG00000237917
## ENSG00000231514 chrY 26626520-26627159 - | ENSG00000231514
## ENSG00000235857 chrY 56855244-56855488 + | ENSG00000235857
## gene_name gene_biotype seq_coord_system
## <character> <character> <character>
## ENSG00000223972 DDX11L1 transcribed_unproces.. chromosome
## ENSG00000227232 WASH7P unprocessed_pseudogene chromosome
## ENSG00000278267 MIR6859-1 miRNA chromosome
## ENSG00000243485 MIR1302-2 lincRNA chromosome
## ENSG00000237613 FAM138A lincRNA chromosome
## ... ... ... ...
## ENSG00000224240 CYCSP49 processed_pseudogene chromosome
## ENSG00000227629 SLC25A15P1 unprocessed_pseudogene chromosome
## ENSG00000237917 PARP4P1 unprocessed_pseudogene chromosome
## ENSG00000231514 FAM58CP processed_pseudogene chromosome
## ENSG00000235857 CTBP2P1 processed_pseudogene chromosome
## symbol entrezid
## <character> <list>
## ENSG00000223972 DDX11L1 100287596,100287102,727856,...
## ENSG00000227232 WASH7P <NA>
## ENSG00000278267 MIR6859-1 102466751
## ENSG00000243485 MIR1302-2 105376912,100302278
## ENSG00000237613 FAM138A 654835,645520,641702
## ... ... ...
## ENSG00000224240 CYCSP49 <NA>
## ENSG00000227629 SLC25A15P1 <NA>
## ENSG00000237917 PARP4P1 <NA>
## ENSG00000231514 FAM58CP <NA>
## ENSG00000235857 CTBP2P1 <NA>
## -------
## seqinfo: 25 sequences from hg38 genomerowRanges(SplicingEvents)## GRanges object with 671257 ranges and 7 metadata columns:
## seqnames ranges strand | tx_id
## <Rle> <IRanges> <Rle> | <IntegerList>
## ENSG00000290825:E001 chr1 11869-12009 + | 1
## ENSG00000290825:E002 chr1 12058-12178 + | 1
## ENSG00000290825:E003 chr1 12698-12721 + | 1
## ENSG00000223972:E001 chr1 12975-13052 + | 2
## ENSG00000290825:E004 chr1 13375-13452 + | 1
## ... ... ... ... . ...
## ENSG00000210144:E001 chrM 5827-5891 - | 252831
## ENSG00000210151:E001 chrM 7446-7514 - | 252832
## ENSG00000198695:E001 chrM 14149-14673 - | 252833
## ENSG00000210194:E001 chrM 14674-14742 - | 252834
## ENSG00000210196:E001 chrM 15956-16023 - | 252835
## tx_name gene_id exon_id
## <CharacterList> <character> <IntegerList>
## ENSG00000290825:E001 ENST00000456328 ENSG00000290825 1
## ENSG00000290825:E002 ENST00000456328 ENSG00000290825 1
## ENSG00000290825:E003 ENST00000456328 ENSG00000290825 5
## ENSG00000223972:E001 ENST00000450305 ENSG00000223972 6
## ENSG00000290825:E004 ENST00000456328 ENSG00000290825 8
## ... ... ... ...
## ENSG00000210144:E001 ENST00000387409 ENSG00000210144 797948
## ENSG00000210151:E001 ENST00000387416 ENSG00000210151 797949
## ENSG00000198695:E001 ENST00000361681 ENSG00000198695 797950
## ENSG00000210194:E001 ENST00000387459 ENSG00000210194 797951
## ENSG00000210196:E001 ENST00000387461 ENSG00000210196 797952
## exon_name exon_rank exonic_part
## <CharacterList> <IntegerList> <integer>
## ENSG00000290825:E001 ENSE00002234944 1 1
## ENSG00000290825:E002 ENSE00002234944 1 2
## ENSG00000290825:E003 ENSE00003582793 2 3
## ENSG00000223972:E001 ENSE00001799933 4 1
## ENSG00000290825:E004 ENSE00002312635 3 4
## ... ... ... ...
## ENSG00000210144:E001 ENSE00001544488 1 1
## ENSG00000210151:E001 ENSE00001544487 1 1
## ENSG00000198695:E001 ENSE00001434974 1 1
## ENSG00000210194:E001 ENSE00001544476 1 1
## ENSG00000210196:E001 ENSE00001544473 1 1
## -------
## seqinfo: 25 sequences (1 circular) from an unspecified genome; no seqlengthsThe sample annotations provided by data generators are also provided in MAE. For most of the sequencing techniques, the sample annotations for all experiments are identical. For antibody-assisted techniques, the sample annotations for m6A methylome data are from IP samples, and those for transcriptomic data are from input samples. If you access sample annotation from MAE object, you can obtain a complete sample annotation tables for both IP and input samples. Here, eTAM-seq is a enzyme-assisted techniques. Therefore, their sample annotations are the same.
head(colData(MAE))## DataFrame with 4 rows and 10 columns
## SampleID Title SourceDatabase TissueOrCellLine
## <character> <character> <character> <character>
## SRR21070403 SRR21070403 HeLa_polyA_WT_FTO-_r.. GEO HeLa
## SRR21070404 SRR21070404 HeLa_polyA_WT_FTO-_r.. GEO HeLa
## SRR21070405 SRR21070405 HeLa_polyA_WT_FTO-_r.. GEO HeLa
## SRR21070406 SRR21070406 HeLa_polyA_WT_FTO-_r.. GEO HeLa
## Organism Treatment DetectionTechnique DataProcessing
## <character> <character> <character> <character>
## SRR21070403 Homo Sapiens WT eTAM-seq Cutadapt|Hisat3N|fin..
## SRR21070404 Homo Sapiens WT eTAM-seq Cutadapt|Hisat3N|fin..
## SRR21070405 Homo Sapiens WT eTAM-seq Cutadapt|Hisat3N|fin..
## SRR21070406 Homo Sapiens WT eTAM-seq Cutadapt|Hisat3N|fin..
## CurationDate BioSample
## <character> <character>
## SRR21070403 Oct 24, 2022 SAMN30324096
## SRR21070404 Oct 24, 2022 SAMN30324097
## SRR21070405 Oct 24, 2022 SAMN30324098
## SRR21070406 Oct 24, 2022 SAMN30324098Finally, the MultiAssayExperiment object also contains fitted parameters of the beta-binomial mixture models.
metadata(MAE)[[1]]## bg_proportion fg_proportion alpha_m6A_bg beta_m6A_bg alpha_m6A_fg
## SRR21070403 0.6259963 0.3740037 1.0403727 45.46696 0.3501398
## SRR21070404 0.6067907 0.3932093 0.9823051 50.64032 0.3159781
## SRR21070405 0.6110785 0.3889215 0.9205881 44.92017 0.3238199
## SRR21070406 0.6103329 0.3896671 0.9397112 45.87532 0.3478345
## beta_m6A_fg
## SRR21070403 0.9485091
## SRR21070404 0.9112292
## SRR21070405 0.9308952
## SRR21070406 0.9743458This can also be accessed through the metadata of site summary SE:
metadata(m6AOmics)[[1]]## bg_proportion fg_proportion alpha_m6A_bg beta_m6A_bg alpha_m6A_fg
## SRR21070403 0.6259963 0.3740037 1.0403727 45.46696 0.3501398
## SRR21070404 0.6067907 0.3932093 0.9823051 50.64032 0.3159781
## SRR21070405 0.6110785 0.3889215 0.9205881 44.92017 0.3238199
## SRR21070406 0.6103329 0.3896671 0.9397112 45.87532 0.3478345
## beta_m6A_fg
## SRR21070403 0.9485091
## SRR21070404 0.9112292
## SRR21070405 0.9308952
## SRR21070406 0.9743458m6AConquer provides both Genomic Ranges and CSV files for IDR analysis-based integration result. Both file types contain the same content. Currently, we only provide the individual and merged integration results across orthogonal techniques. You are free to use any of them.
For each technique pair, we also provide two kinds of integration results: positive reproducible sites and all reproducible sites.
Positive Reproducible Site: Includes reproducible sites with site calling FDR < 0.05
All Reproducible Site: Includes all reproducible sites (IDR < 0.05)
To access the individual integration results in Genomic Ranges file format, please download the corresponding R object from the corresponding column. As an illustration, here we use the positive integration results between GLORI and eTAM-seq. First, load the GenomicRanges packages and read the file into R:
suppressWarnings(suppressPackageStartupMessages(library(GenomicRanges)))
gr <- readRDS("m6A_HighConfSites_eTAM_GLORI_hg38.rds")
head(gr)## GRanges object with 6 ranges and 7 metadata columns:
## seqnames ranges strand | m6A_ratio_eTAM-seq m6A_ratio_GLORI
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr5 172339669 * | 1 0.988571428571429
## [2] chr20 30512433 * | 1 0.993157380254154
## [3] chr7 4764049 * | 1 1
## [4] chr9 84002004 * | 1 1
## [5] chr8 10725587 * | 1 0.994117647058824
## [6] chr12 53945469 * | 1 0.991087344028521
## m6A_probability_eTAM-seq m6A_probability_GLORI Pvalue_adjusted_eTAM-seq
## <character> <character> <character>
## [1] 1 1 6.59523340105527e-65
## [2] 1 1 9.1957035297511e-40
## [3] 1 1 4.90269299673246e-31
## [4] 1 1 2.41502528942755e-58
## [5] 1 1 9.10217850073897e-28
## [6] 1 1 2.07642323706231e-47
## Pvalue_adjusted_GLORI Irreproducible_discovery_rate
## <character> <character>
## [1] 1.05596909776191e-180 3.64423036192107e-09
## [2] 0 3.64423036192107e-09
## [3] 4.85119768337239e-39 3.64423036192107e-09
## [4] 2.67073957390016e-27 3.64423036192107e-09
## [5] 2.55277641665495e-178 3.64423036192107e-09
## [6] 0 3.64423036192107e-09
## -------
## seqinfo: 25 sequences from an unspecified genome; no seqlengthsThe Genomic Ranges file shows the genomic locations of all the positive reproducible sites identified by IDR between GLORI and eTAM-seq, with a total site number of 91979. The meta columns of the results contain the m6A methylation ratios, posterior probabilities, and BH corrected p-values modelled in each technique, along with the IDR value evaluated.
To access the merged integration results, you may load the merged genomic ranges file into R:
gr_merged <- readRDS("m6A_HighConfSites_Combined_hg38.rds")
head(gr_merged)## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | support_number support_technique
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr1 139472 * | 1 eTAM-seq&GLORI
## [2] chr1 185038 * | 2 GLORI&m6A-SAC-seq, G..
## [3] chr1 258559 * | 1 GLORI&MeRIP-seq
## [4] chr1 348032 * | 1 GLORI&MeRIP-seq
## [5] chr1 348080 * | 1 GLORI&m6A-SAC-seq
## [6] chr1 826939 * | 2 GLORI&m6ACE-seq, GLO..
## -------
## seqinfo: 25 sequences from an unspecified genome; no seqlengthsThe meta columns of the merged results contain the names and numbers of supported orthogonal technique pair. Totally, 146250 reproducible sites are identified across all orthogonal technique pairs.
To access the integration results through CSV tables, simply read the CSV tables into R:
csv <- read.csv("m6A_HighConfSites_eTAM_GLORI_hg38.csv", row.names = NULL)
head(csv)## seqnames start end width strand m6A_ratio_eTAM_seq m6A_ratio_GLORI
## 1 chr5 172339669 172339669 1 * 1 0.9885714
## 2 chr20 30512433 30512433 1 * 1 0.9931574
## 3 chr7 4764049 4764049 1 * 1 1.0000000
## 4 chr9 84002004 84002004 1 * 1 1.0000000
## 5 chr8 10725587 10725587 1 * 1 0.9941176
## 6 chr12 53945469 53945469 1 * 1 0.9910873
## m6A_probability_eTAM_seq m6A_probability_GLORI Pvalue_adjusted_eTAM_seq
## 1 1 1 6.595233e-65
## 2 1 1 9.195704e-40
## 3 1 1 4.902693e-31
## 4 1 1 2.415025e-58
## 5 1 1 9.102179e-28
## 6 1 1 2.076423e-47
## Pvalue_adjusted_GLORI Irreproducible_discovery_rate
## 1 1.055969e-180 3.64423e-09
## 2 0.000000e+00 3.64423e-09
## 3 4.851198e-39 3.64423e-09
## 4 2.670740e-27 3.64423e-09
## 5 2.552776e-178 3.64423e-09
## 6 0.000000e+00 3.64423e-09csv_merged <- read.csv("m6A_HighConfSites_Combined_hg38.csv", row.names = NULL)
head(csv_merged)## seqnames start end width strand support_number
## 1 chr1 139472 139472 1 * 1
## 2 chr1 185038 185038 1 * 2
## 3 chr1 258559 258559 1 * 1
## 4 chr1 348032 348032 1 * 1
## 5 chr1 348080 348080 1 * 1
## 6 chr1 826939 826939 1 * 2
## support_technique
## 1 eTAM-seq&GLORI
## 2 GLORI&m6A-SAC-seq, GLORI&m6ACE-seq
## 3 GLORI&MeRIP-seq
## 4 GLORI&MeRIP-seq
## 5 GLORI&m6A-SAC-seq
## 6 GLORI&m6ACE-seq, GLORI&MeRIP-seq