Disease SNP prioritization

This is an protocol for prioritization of SNPs associated certain phenotype/disease. Here is a study for prioritization of SNPs associated with Type 1 diabetes. You can follow the below analysis steps.

1. Seed SNPs preparation for type 1 diabetes (T1D)

> gwas.r

To download GWAS Catalog data (MacArthur et al, 2017, Nucleic Acids Research, pmid 27899670), you can search certain disease. In this study, we downloaded SNP-sets for type 1 diabetes. Then you can run R code file for filtering the GWAS Catalog data as below CMD command line:

• Bellow functions are run under the windows or linux command console environment.
• Instead of [ ], you have to put the arguments file path or value by the options.
• Usage: Rscript gwas.r [GWAS_file_path] [p-value_criteria]

Rscript T1D_gwas.r db/GWAS_EFO0001359.tsv 5e-08

> ldlink_dn.py / link_filt.r

To download LDlink data (version 3.3.0 12/24/2018) (Machiela et al, 2015, Bioinformatics, pmid 26139635), you can run T1D_ldlink.py as below CMD command line:

• To run the code, you need list of SNP RS IDs of dbSNP database as txt file
• Usage: python ldlink.py [SNP_file_path.txt]

python ldlink_dn.py data/gwas_5e-08_129.tsv

... 129/129 = rs11580078 status_code = 200 line number = 950 file saved = db/ldlink/rs11580078.tsv

Download process completed. Job time= 00:42:49

To filter the LDlink data by statistical criteria, both r² >0.6 and D'=1, you can run T1D_ldlink.r as below CMD command line:

• Usage: Rscript ldlink.r [SNP_file_path.txt] [LDlink_data_folder_path] [LDlink_filter_option]
• The LDlink_filter_option is a mandatory. Choose one of the following option numbers.
  1. r2>0.6 or Dprime=1
  2. r2>0.6
  3. Dprime=1
  4. r2>0.6 and Dprime=1

Rscript ldlink_filt.r data/gwas_5e-08_129.tsv db/ldlink 2

Input SNP list number = 129

Error in read.table(as.character(snptb[i, 2]), header = T) : more columns than column names In addition: Warning message: In read.table(as.character(snptb[i, 2]), header = T) : incomplete final line found by readTableHeader on 'db/ldlink/rs75793288.tsv' NULL Filtering option, r2 > 0.6 was chosen. ::Expluded no rsid elements = 31

1/3. Numbers of SNPs SNP Tier1 = 129 SNP Tier2 = 5116 SNP seed = 5245

2/3. Generation of a result TSV file File write: data/seedSNP_5245_ldlink.tsv

3/3. Generation of a result BED file Table, rows= 5245 cols= 4 File write: data/seedSNP_5245.bed Job done for 7.9 sec

• data/seedSNP_5245_ldlink.tsv -> Supplementary Table 1 and Supplementary Table 2

Q1. Generation of private SNP list (rsids) to BED file format?

To use bedtools later, you have to prepare SNP list as bed format. If you have simple dbSNP rsid list, you can run src/biomart_snp.r for generate bed file. But you should check NA values and fill it manually.

#Rscript src/biomart_snp.r [rsid_list_file_path]
Rscript src/biomart_snp.r data/seedSNP_5245_biomart.txt

Input contents, rows= 5244 cols= 1 Table, rows= 10280 cols= 4

Job done for 12.7 sec

[IMPORTANT] Before you move to next step, we make sure that your seedSNP_#.bed file has no NA values.

2. RoadMap data download and filter

The RoadMAP project provides epigenome annotations such as 12-mark/127-reference epigenome/25-state Imputation Based Chromatin State Model by using ChromHMM algorithm (Ernst and Kellis, 2012, Nature Methods, pmid 22373907). We downloaded the 127 files by their cell types (e.g., E001_25_imputed12marks_hg38lift_dense.bed.gz and etc) using R code (T1D_roadmap.r). And then we filtered the data by annotation code (see db/[roadmap] ) including 13_EnhA1, 14_EnhA2, 15_EnhAF, 16_EnhW1, 17_EnhW2, 18_EnhAc.

To download RoadMap data, you need to install AnnotationHub and rtracklayer in BiocManaer as below R code:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("AnnotationHub", version = "3.8")
BiocManager::install("rtracklayer", version = "3.8")

> roadmap_dn.r

To download RoadMap data, you can run roadmap_dn.r as below CMD command line:

• The 127 cell type-specific RoadMap BED files will download at db/roadmap folder
• This process takes about ~30 min that depends on your download speed.

Rscript roadmap_dn.r

> roadmap_filt.r

To filter the RoadMap data by Enhancers, you can run roadmap_filt.r as below CMD command line:

• The result file would be saved as data/roadmap_enh.bed
• This process takes ~3 min that depends on your computer processor speed.

Rscript roadmap_filt.r

Q2. If you have memory problem..

When running the roadmap_filt.r function, it stop with not enough memory error, You can use roadmap_filt_dtr.r function for limited memory usage (~3.8 GB).

Rscript roadmap_filt_dtr.r

$ bedtools merge/ bedtools closest

To avoid multiple count of enhancers as well as to reduce file size and to achieve faster process, merge RoadMap enhancer information using a BASH tool bedtools. Here is the BASH pipeline for bedtools sort and bedtools merge. Then, to identify T1D SNPs occupied in RoadMap enhancers, you can use BASH tool bedtools intersect as below code:

• Compressed file size of roadmap_enh.bed.gz is >139 MB.
• Compressed file size of roadmap_enh_merer.bed.gz is about 3.7 MB.
• Removing NA values, data/seedSNP_5245_bm.bed file is updated version from the data/seedSNP_5245.bed file.

bedtools sort -i db/roadmap_enh.bed | bedtools merge -i stdin -c 1 -o count > db/roadmap_enh_merge.bed
bedtools sort -i data/seedSNP_5245_bm.bed | bedtools closest -d -a stdin -b db/roadmap_enh_merge.bed > data/roadmap_dist.tsv

> src/bedtools_closest.r

To prioritize RoadMap enhancer occupied SNPs, you can run src/bedtools_closestroadmap.r as below CMD command line:

• data/roadmap_dist_df.tsv file is obtained that is for enhancer annotated file .
• data/snp_484_roadmap_dist.bed file is obtained that is for BED format file for USCS browser.
• Usage: Rscript src/bedtools_closest.r [bedtools_closest_result_file_path] [double_line_result]

Rscript src/bedtools_closest.r data/roadmap_dist.tsv False

Row number = 5245 Enhancer occupied by SNPs = 589 SNPs in RoadMap enhancers = 1688

File write: data/roadmap_dist_df.tsv File write: data/snp_1688_roadmap_dist.bed

Job done for 0.2 sec

• data/roadmap_dist_df.tsv -> Supplementary Table 2

Q3. How about just use not merged roadmap_enh.bed file?

Instead of merge file, when you use original db/roadmap_enh.bed file, you can find a lot of duplicated enhancers regions.

bedtools sort -i db/roadmap_enh.bed | bedtools closest -d -a data/seedSNP_1817.bed -b stdin > data/roadmap_dist2.tsv

3. ENCODE ChIP-seq data download and filter

The ENCODE ChIP-seq for regulatory transcription factor binding site (Reg-TFBS) cluster data can downloaded wgEncodeRegTfbsClusteredV3 data from UCSC FTP (68 MB) or bioconductor data("wgEncodeTfbsV3"). Here, we assume having downloaded UCSC FTP file wgEncodeRegTfbsClusteredV3.bed.gz (81 MB).

Rsciprt encode_dn.r

Directory generated: db/encode/ trying URL 'http://hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeRegTfbsClustered/wgEncodeRegTfbsClusteredV3.bed.gz' Content type 'application/x-gzip' length 84986946 bytes (81.0 MB)

downloaded 81.0 MB

db/encode/wgEncodeRegTfbsClusteredV3.bed.gz

Job done for 1.8 min

$ bedtools merge | bedtools closest

To identify TFBS occupied SNPs, you can use bedtools merge and bedtools closest as following code:

• Merging the ENCODE TFBS data give you benefits such as avoiding multiple count of enhancers as well as reducing file size and achieving faster process

bedtools merge -i db/wgEncodeRegTfbsClusteredV3.bed.gz -c 1 -o count > db/encode_tfbs_merge.bed
bedtools sort -i data/seedSNP_5245_bm.bed | bedtools closest -d -a stdin -b db/encode_tfbs_merge.bed > data/encode_dist.tsv

> src/bedtools_closest.r

To prioritize ENCODE Reg-TFBS occupied SNPs, you can run src/bedtools_closestroadmap.r as below CMD command line:

• data/roadmap_dist_df.tsv file is obtained that is for enhancer annotated file .
• data/snp_enh_484.bed file is obtained that is for BED format file for USCS browser.
• Usage: Rscript src/bedtools_closest_roadmap.r [bedtools_closest_result_file_path] [double_line_result]

Rscript src/bedtools_closest.r data/encode_dist.tsv False

Row number = 5245 Enhancer occupied by SNPs = 653 SNPs in RoadMap enhancers = 1253

File write: data/encode_dist_df.tsv File write: data/snp_1253_encode_dist.bed

Job done for 0.2 sec

• data/encode_dist_df.tsv -> Supplementary Table 2

4. Regulome DB data download and filter

The RegulomeDB provides category scores for SNPs by evidences (see Regulome score.txt), including eQTL, TF binding, matched TF motif, matched DNase Footprint, and DNase peak. In this study, we stringently filtered and used high-score (≥ 2b) SNPs for our study. Before you start, you can download the files from Regulome DB download page. To make this process faster, you can convert the downloaded files to RDS format.

• RegulomeDB.dbSNP132.Category1.txt.gz (2 MB)
• RegulomeDB.dbSNP132.Category2.txt.gz (39.3 MB)
• Or you can download total dataset: RegulomeDB.dbSNP141.txt.gz (2.8 GB)

Rscript regulome_dn.r

In dir.create(file.path(dir)) : 'db\regulome' already exists trying URL 'http://www.regulomedb.org/downloads/RegulomeDB.dbSNP132.Category1.txt.gz' Content type 'application/gzip' length 2096454 bytes (2.0 MB)

downloaded 2.0 MB

trying URL 'http://www.regulomedb.org/downloads/RegulomeDB.dbSNP132.Category2.txt.gz' Content type 'application/gzip' length 41253483 bytes (39.3 MB)

downloaded 39.3 MB

Job process: 52.2 sec File write: db/regulome/RegulomeDB.dbSNP132.Category1.txt.gz.rds File write: db/regulome/RegulomeDB.dbSNP132.Category2.txt.gz.rds Job done for 1.2 min

Here we converted the download files to RDS format files to achieve fast loading speed. Use the RegulomeDB RDS files, you can filter and analyze the dataset by using regulome.r as following command line:

Rscript regulome.r data/seedSNP_5245_bm.bed db/RegulomeDB.dbSNP132.Category1.txt.rds db/RegulomeDB.dbSNP132.Category2.txt.rds

Input SNPs number = 5245 Input regulome files = 2

Read input file 1: db/RegulomeDB.dbSNP132.Category1.txt.rds Table row = 39432, col = 5 Job process: 0.3 sec

Read input file 2: db/RegulomeDB.dbSNP132.Category2.txt.rds Table row = 407796, col = 5 Job process: 6.1 sec

Regulome score >=2b, SNPs = 430528 Functional motifs (1f_only-2b) = 395823

• Regulome >=2b SNPs = 301
• SNPs with functional motifs (1f_only-2b) = 138

File write: data/regulome_301.tsv File write: data/snp_301_regulome2b.bed Job done: 2019-08-15 17:43:22 for 6.4 sec

The result files are save at data/ folder:

• data/regulome_301.tsv -> Supplementary Table 2
• data/snp_301_regulome2b.bed

Venn analysis to identify core SNPs

Summary for SNPs with RoadMap annotation, ENCODE ChIP-seq, and RegulomeDB. This R code for Venn analysis uses Bioconductor limma R package. The installation of the limma package as below:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("limma", version = "3.8")

To prioritize the SNPs, you can run venn.r as below CMD command line with these files:

• data/snp_1688_roadmap_dist.bed
• data/snp_1253_encode_dist.bed
• data/snp_301_regulome2b.bed

> venn.r

Rscript venn.r data/snp_1688_roadmap_dist.bed data/snp_1253_encode_dist.bed data/snp_301_regulome2b.bed

[1] "snp_1688_roadmap_dist" [1] "snp_1253_encode_dist" [1] "snp_301_regulome2b" File write: data/venn.tsv File write: data/vennCounts.tsv

Euler fit is done. Figure draw: fig/euler_snp_1688_roadmap_dist_snp_1253_encode_dist.png File write: data/snp_79_core.bed

Job done for 0.5 sec

The result files are generated as below:

• venn_tfbs.tsv: binary SNP overlap table
• vennCounts_tfbs.tsv: overlapped SNP numbers
• snp_79_core.bed

The result figure is generated as below:

5. GTEx eQTL data download and filter

The Genotype-Tissue Expression (GTEx) project is a public resource to study tissue-specific gene expression and their regulation by SNPs. GTEx version 7 includes 11,688 samples, 53 tissues and 714 donors. You can download GTEx eQTL data GTEx_Analysis_v7_eQTL.tar.gz (915 MB) and filter by statistical criteria p < 3e-04. The GTEx_Analysis_v7_eQTL.tar.gz compressed file includes:

• 48 files with db/GTEx_Analysis_v7_eQTL/*.egenes.txt extensions
• 48 files with db/GTEx_Analysis_v7_eQTL/*.signif_variant_gene_pairs.txt extensions

And we need SNP annotations to achieve Rsid for GTEx ids.

• db/GTEx_Analysis_2016-01-15_v7_WholeGenomeSeq_635Ind_PASS_AB02_GQ20_HETX_MISS15_PLINKQC.lookup_table.txt.gz (440 MB)
• Nominal p-values from GTEx data were generated for each variant-gene pair by testing the alternative hypothesis that the slope of a linear regression model between genotype and expression deviates from 0.

> gtex_dn.r [and] gtex_filt.r

Rscript gtex_dn.r

(1/2) Download eQTL data trying URL 'https://storage.googleapis.com/gtex_analysis_v7/single_tissue_eqtl_data/GTEx_Analysis_v7_eQTL.tar.gz' Content type 'application/x-tar' length 959746583 bytes (915.3 MB)

downloaded 915.3 MB

File write: db/gtex_files.txt

(2/2) Download SNPid annotation file trying URL 'https://storage.googleapis.com/gtex_analysis_v7/reference/GTEx_Analysis_2016-01-15_v7_WholeGenomeSeq_635Ind_PASS_AB02_GQ20_HETX_MISS15_PLINKQC.lookup_table.txt.gz' Content type 'application/gzip' length 461926948 bytes (440.5 MB)

downloaded 440.5 MB

Job done for 1.2 min

> 50 min The downloaded files were converted to RDS files.

Rscript gtex_rds.r

• Loading GTEx BED files
• File reading... (1/48) Adipose_Subcutaneous ... (48/48) Whole_Blood NULL gte.df.pval_nominal Min. :0.000e+00 1st Qu.:0.000e+00 Median :1.111e-07 Mean :7.398e-06 3rd Qu.:5.219e-06 Max. :8.005e-04
• GTEx table, rows= 36781356 cols= 13
• BED file read complete. Job process: 9.3 min
• Loading annotation file - Annotation file read complete. Job process: 35.5 min
• Annotation file, rows= 40738696 cols= 7
• GTEx annotation, rows= 36781356 cols= 9

Job process: 50.7 min

• Saving as RDS file..

Job process: 52 min

Job done for 52 min

To filter the GTEx data by p <5e-08, I executed following code:

Rscript gtex_filt.r 5e-08

p-value threshold = 5e-08

(1/3) Loading GTEx RDS file

• GTEx table, rows= 36781356 cols= 9
• BED file read complete. Job process: 53.3 sec

(2/3) Filtering by nominal p-value gte.sig.pval_nominal Min. :0.000e+00 1st Qu.:0.000e+00 Median :3.120e-12 Mean :3.897e-09 3rd Qu.:1.429e-09 Max. :5.000e-08

• GTEx significant, rows= 17113536 cols= 9
• Job process: 1.1 min

File write: db/gtex_signif.tsv Job done for 3.8 min

The result file size are huge and the process takes long time (~50 min)

• gtex_signif_5e-8.tsv.rds (322 MB)
• This file was compressed by zip as three separated <100 MB files.
  1. gtex_signif_5e-8.tsv.zip
  2. gtex_signif_5e-8.tsv.z01
  3. gtex_signif_5e-8.tsv.gz.z02

To identify T1D SNPs

Rscript gtex.r data/seedSNP_5245_bm.bed db/gtex_signif_5e-8.tsv.rds

Input SNPs number = 5,245

(1/3) Loading GTEx significant file

• gtex_signif_5e-8.tsv.rds: rows= 17,113,536 cols= 9
• Job process: 29.3 sec

(2/3) eQTL SNP filteration

• Overlapped table, rows= 137,762 cols= 9
• eQTL SNPs = 2,676
• Associated genes = 192

File write: data/gtex_5e-08_2676.tsv

(3/3) eQTL SNP BED file generation

• GTEx SNP BED, rows= 2,676 cols= 4
• eQTL SNPs = 2,676

File write: data/snp_2676_gtex.bed

Job done for 33.8 sec

The result files of criteria 5e-08 are here:

• gtex_5e-08_2676.tsv -> Supplementary Table 3
• snp_2676_gtex.bed

Venn analysis and overlap SNPs

To prioritize the eQTL SNPs among the 26 high-probability causal enhancer SNPs, you can run venn.r as below CMD command line with these files:

• data/snp_570_roadmap_encode.bed - Enhancer occupied SNP list <- manually generated
• data/snp_79_core.bed - High-probability causal enhancer SNP list
• data/snp_2676_gtex.bed - eQTL SNP list

> venn.r

Rscript venn.r data/snp_570_roadmap_encode.bed data/snp_79_core.bed data/snp_2676_gtex.bed

The result files are generated as below:

• venn.tsv > venn_gtex.tsv: binary SNP overlap table
• vennCounts.tsv > vennCounts_gtex.tsv: overlapped SNP numbers
• snp_79_core.bed > snp_79_core_tfbs.bed: SNP BED format file

The result figure is generated as below:

5-1. Nearest gene approach

downloading Ensembl gene location data

To identify nearest genes from the eQTL SNPs, firstly you need to download gene location data from Ensembl database biomart (version=Grch37).

Rscript src/biomart_gene.r

• Ensembl table, rows= 63677 cols= 5

• File write: db/ensembl_gene_ann.tsv

• Filter result, rows= 57736 cols= 5

• File write: db/ensembl_gene.bed

Job done for 17.1 sec

$ bedtools closest

To identify nearest genes from the eQTL SNPs, you can use bedtools merge and bedtools closest as following BASH codes:

bedtools sort -i db/ensembl_gene.bed | bedtools closest -d -a data/seedSNP_5245_bm.bed -b stdin > data/seedSNP_nearest.tsv

bedtools sort -i db/ensembl_gene.bed | bedtools closest -d -a data/snp_2676_gtex.bed -b stdin > data/gtex_nearest.tsv

> src/bedtools_closest.r

To prioritize RoadMap enhancer occupied SNPs, you can run src/bedtools_closestroadmap.r as below CMD command line:

• Usage: Rscript src/bedtools_closest_gtex.r [bedtools_closest_result_file_path]

Rscript src/bedtools_closest_gtex.r data/seedSNP_nearest.tsv

Row number = 5861 Input SNPs = 5245 Nearest genes = 374

File write: data/seedSNP_nearest_df.tsv

Job done for 0.4 sec

Rscript src/bedtools_closest_gtex.r data/gtex_nearest.tsv

Row number = 2909 Input SNPs = 2676 Nearest genes = 308

File write: data/gtex_nearest_df.tsv

Job done for 0.2 sec

• data/gtex_nearest_df.tsv -> Supplementary Table 4

> src/gtex_overlap.r

To identify the eQTL SNPs occupied on TFBS binding enhancers, you can run src/gtex_overlap.r as below CMD command line:

Rscript gtex_overlap.r data/snp_570_roadmap_encode.bed data/gtex_5e-08_2676.tsv data/gtex_nearest_df.tsv

(1/2) Read files..

• data/snp_570_roadmap_encode.bed, rows= 570 cols= 4
• data/gtex_5e-08_2676.tsv, rows= 137762 cols= 9
• data/gtex_nearest_df.tsv, rows= 1952 cols= 7

SNPs= 2676 Genes= 192 (Nearest= 91)

(2/2) Overlap the two files..

• TFBS overlap, rows= 41441 cols= 10

SNPs= 348 Genes= 140 (Nearest= 63)

File write: data/snp_348_gtex_enh.bed

6. lncRNASNP2 data download and filter

Human SNPs located in long non-coding RNAs (lncRNAs) are archived in lncRNASNP2 database. You can download these data at the download page:

• lncRNASNP2_snplist.txt.gz - SNP list includes the list of human SNPs in lncRNASNP database.
• lncrnas.txt.gz - lncRNA list includes the list of human lncRNAs in lncRNASNP database.
• lncrna-diseases_experiment.txt.gz - Experimental validated lncRNA-associated diseases includes all experiment validated lncRNA-associated diseases.
• Rscript lncrnasnp.r [SNP_BED_file_path] [lncRNAsnp2_SNP_list_file_path] [lncRNAsnp2_lncRNA_list_file_path] [lncRNAsnp2_diseases_list_file_path]

Rscript lncrnasnp_dn.r

1: package 'data.table' was built under R version 3.5.2

2: package 'GenomeInfoDb' was built under R version 3.5.2

trying URL 'http://bioinfo.life.hust.edu.cn/static/lncRNASNP2/downloads/snps_mod.txt'

Content type 'text/plain; charset=GBK' length 477785336 bytes (455.7 MB)

downloaded 455.7 MB

trying URL 'http://bioinfo.life.hust.edu.cn/static/lncRNASNP2/downloads/lncrnas.txt'

Content type 'text/plain; charset=GBK' length 7005411 bytes (6.7 MB)

downloaded 6.7 MB

trying URL 'http://bioinfo.life.hust.edu.cn/static/lncRNASNP2/downloads/lncRNA_associated_disease_experiment.txt'

Content type 'text/plain; charset=GBK' length 31542 bytes (30 KB)

downloaded 30 KB

Job process: 1.4 min

File write: db/lncRNASNP2_snplist.txt.rds

File write: db/lncrnas.txt.rds

File write: db/lncrna-diseases_experiment.txt.rds

Job done for 2.2 min

To identify lncRNA overlapped longevity SNPs:

Rscript lncrnasnp.r data/seedSNP_5245_bm.bed db/lncRNASNP2_snplist.txt.rds db/lncrnas.txt.rds db/lncrna-diseases_experiment.txt.rds

(1/3) Read files..

• data/seedSNP_5245_bm.bed; Job process: 0.1 sec
• db/lncRNASNP2_snplist.txt.rds; Job process: 17.3 sec
• db/lncrnas.txt.rds; Job process: 17.5 sec
• db/lncrna-diseases_experiment.txt.rds; Job process: 17.5 sec

path	nrow	ncol
data/seedSNP_5245_bm.bed	5245	4
db/lncRNASNP2_snplist.txt.rds	10205295	2
db/lncrnas.txt.rds	141271	4
db/lncrna-diseases_experiment.txt.rds	753	3

• Job process: 17.7 sec

(2/3) Overlapping lncRNA to my SNP list and binding annotation..

lncRNA	SNPs
147	261
File write: data/snp_261_lncrnasnp.bed

(3/3) Annotating SNPs in lncRNAs

File write: data/lncrnasnp_261.tsv

Job done for 22.8 sec

• data/snp_261_lncrnasnp.bed - 261 SNPs BED file
• data/lncrnasnp_261.tsv -> Supplementary Table 5

> venn.r

Rscript venn.r data/snp_79_core_tfbs.bed data/snp_348_gtex_enh.bed data/snp_261_lncrnasnp.bed

[1] "snp_79_core_tfbs" [1] "snp_348_gtex_enh" [1] "snp_261_lncrnasnp" File write: data/venn.tsv File write: data/vennCounts.tsv

Euler fit is done. Figure draw: fig/euler_snp_79_core_snp_348_gtex_enh.png File write: data/snp_7_core.bed

Job done for 0.3 sec

• venn.tsv -> venn_lncrnasnp.tsv: binary SNP overlap table
• vennCounts.tsv -> vennCounts_lncrnasnp.tsv: overlapped SNP numbers
• snp_7_core.bed -> snp_7_core_lncrnasnp.bed: SNP BED format file

Rscript venn.r data/seedSNP_5245_bm.bed data/snp_2676_gtex.bed data/snp_1253_encode_dist.bed data/snp_1688_roadmap_dist.bed data/snp_301_regulome2b.bed data/snp_261_lncrnasnp.bed

[1] "seedSNP_5245_bm" [1] "snp_2676_gtex" [1] "snp_1253_encode_dist" [1] "snp_1688_roadmap_dist" [1] "snp_301_regulome2b" [1] "snp_261_lncrnasnp"

Message: Can't plot Venn diagram for more than 5 sets. File write: data/venn.tsv File write: data/vennCounts.tsv

Job done for 0.3 sec

• venn.tsv -> summary.tsv : Summary file for this analysis.
• vennCounts.tsv -> summaryCount.tsv : Summary SNP numbers.