ReadMe.md

In KatrionaGoldmann/omicAnnotations: Use databases to annotate genomic and transcriptomic data

omicAnnotations

This package pools together information from different databases and APIs in order to annotate SNPs and genes. In particular this uses databases by:

         

To install:

devtools::install_github("KatrionaGoldmann/omicAnnotations")

Gene Annotations

First, if you want to include associated diseases from disGeNET you will need to get an api_key. To get this sign up and get your API key either from the API directly or run:

api_key <- get_api_key(email="your email", password="your password")

For example for the entire gene summary:

gene_df <- gene_summary(genes=c("MS4A1", "FMOD", "FGF1", "SLAMF6"), 
                        diseases="C20", 
                        disease_api_token=api_key)

## [1] "Annotating from self-curated data..."
## [1] "Getting gene summaries..."
## [1] "Finding associated diseases..."

gene_df <- gene_df[, c("Gene", "description", "summary", "Associated_diseases")]

kable(gene_df, format = "markdown", row.names = FALSE)

Gene description summary Associated_diseases MS4A1 membrane spanning 4-domains A1 This gene encodes a member of the membrane-spanning 4A gene family. Members of this nascent protein family are characterized by common structural features and similar intron/exon splice boundaries and display unique expression patterns among hematopoietic cells and nonlymphoid tissues. This gene encodes a B-lymphocyte surface molecule which plays a role in the development and differentiation of B-cells into plasma cells. This family member is localized to 11q12, among a cluster of family members. Alternative splicing of this gene results in two transcript variants which encode the same protein. [provided by RefSeq, Jul 2008] Common Variable Immunodeficiency; Acquired Hypogammaglobulinemia; Immunoglobulin Deficiency, Late-Onset FMOD fibromodulin Fibromodulin belongs to the family of small interstitial proteoglycans. The encoded protein possesses a central region containing leucine-rich repeats with 4 keratan sulfate chains, flanked by terminal domains containing disulphide bonds. Owing to the interaction with type I and type II collagen fibrils and in vitro inhibition of fibrillogenesis, the encoded protein may play a role in the assembly of extracellular matrix. It may also regulate TGF-beta activities by sequestering TGF-beta into the extracellular matrix. Sequence variations in this gene may be associated with the pathogenesis of high myopia. Alternative splicing results in multiple transcript variants. [provided by RefSeq, Jun 2013] FGF1 fibroblast growth factor 1 The protein encoded by this gene is a member of the fibroblast growth factor (FGF) family. FGF family members possess broad mitogenic and cell survival activities, and are involved in a variety of biological processes, including embryonic development, cell growth, morphogenesis, tissue repair, tumor growth and invasion. This protein functions as a modifier of endothelial cell migration and proliferation, as well as an angiogenic factor. It acts as a mitogen for a variety of mesoderm- and neuroectoderm-derived cells in vitro, thus is thought to be involved in organogenesis. Multiple alternatively spliced variants encoding different isoforms have been described. [provided by RefSeq, Jan 2009] SLAMF6 SLAM family member 6 The protein encoded by this gene is a type I transmembrane protein, belonging to the CD2 subfamily of the immunoglobulin superfamily. This encoded protein is expressed on Natural killer (NK), T, and B lymphocytes. It undergoes tyrosine phosphorylation and associates with the Src homology 2 domain-containing protein (SH2D1A) as well as with SH2 domain-containing phosphatases (SHPs). It functions as a coreceptor in the process of NK cell activation. It can also mediate inhibitory signals in NK cells from X-linked lymphoproliferative patients. Alternative splicing results in multiple transcript variants encoding distinct isoforms.[provided by RefSeq, May 2010]

Publications

You can check for publications focusing on genes with given terms. Either using associated_publications:

gene_pubs <- associated_publications(genes=c("FGF1"), 
                                     keywords=c("rheumatoid"), 
                                     split="OR", 
                                     verbose=TRUE)

kable(gene_pubs, format = "markdown", row.names=FALSE)

Gene Publications FGF1 The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1.; sICAM-1 as potential additional parameter in the discrimination of the Sjögren syndrome and non-autoimmune sicca syndrome: a pilot study.; [Effects of Huatan Tongluo Recipe on IL-1β-induced Proliferation of Rheumatoid Arthritis Synovial Fibroblasts and the Production of TNF-α and aFGF].; Fibroblast growth factors, fibroblast growth factor receptors, diseases, and drugs.; VEGF, FGF1, FGF2 and EGF gene polymorphisms and psoriatic arthritis.; Transcription factor Ets-1 regulates fibroblast growth factor-1-mediated angiogenesis in vivo: role of Ets-1 in the regulation of the PI3K/AKT/MMP-1 pathway.; Induction of RANKL expression and osteoclast maturation by the binding of fibroblast growth factor 2 to heparan sulfate proteoglycan on rheumatoid synovial fibroblasts.; Acidic fibroblast growth factor in synovial cells.; Characterization of tissue outgrowth developed in vitro in patients with rheumatoid arthritis: involvement of T cells in the development of tissue outgrowth.; Fibroblast growth factor-1 (FGF-1) enhances IL-2 production and nuclear translocation of NF-kappaB in FGF receptor-bearing Jurkat T cells.; A novel in vitro assay for human angiogenesis.; Expression and functional expansion of fibroblast growth factor receptor T cells in rheumatoid synovium and peripheral blood of patients with rheumatoid arthritis.; Detection of T cells responsive to a vascular growth factor in rheumatoid arthritis.; Coexpression of phosphotyrosine-containing proteins, platelet-derived growth factor-B, and fibroblast growth factor-1 in situ in synovial tissues of patients with rheumatoid arthritis and Lewis rats with adjuvant or streptococcal cell wall arthritis.; Platelet-derived growth factors and heparin-binding (fibroblast) growth factors in the synovial tissue pathology of rheumatoid arthritis.; Fibroblast growth factors: from genes to clinical applications.; Production of platelet derived growth factor B chain (PDGF-B/c-sis) mRNA and immunoreactive PDGF B-like polypeptide by rheumatoid synovium: coexpression with heparin binding acidic fibroblast growth factor-1.; Detection of high levels of heparin binding growth factor-1 (acidic fibroblast growth factor) in inflammatory arthritic joints.

Or gene_summary:

gene_df <- gene_summary(genes=c("FGF1"), 
                        associated_diseases =FALSE,
                        gene_description=FALSE, 
                        publications = TRUE)

## [1] "Annotating from self-curated data..."
## [1] "Getting publications from PubMed..."

kable(gene_df, format = "markdown", row.names=FALSE)

Gene Type Curated_description Publications FGF1 Fibroblast Growth Factors FGF/FGFR Pathways in Multiple Sclerosis and in Its Disease Models.; The transcriptomic profiling of SARS-CoV-2 compared to SARS, MERS, EBOV, and H1N1.; sICAM-1 as potential additional parameter in the discrimination of the Sjögren syndrome and non-autoimmune sicca syndrome: a pilot study.; Oligodendroglial fibroblast growth factor receptor 1 gene targeting protects mice from experimental autoimmune encephalomyelitis through ERK/AKT phosphorylation.; [Effects of Huatan Tongluo Recipe on IL-1β-induced Proliferation of Rheumatoid Arthritis Synovial Fibroblasts and the Production of TNF-α and aFGF].; Dysregulation of pathways involved in the processing of cancer and microenvironment information in MCA + TPA transformed C3H/10T1/2 cells.; Fibroblast growth factors, fibroblast growth factor receptors, diseases, and drugs.; VEGF, FGF1, FGF2 and EGF gene polymorphisms and psoriatic arthritis.; Cutaneous gene expression by DNA microarray in murine sclerodermatous graft-versus-host disease, a model for human scleroderma.; Transcription factor Ets-1 regulates fibroblast growth factor-1-mediated angiogenesis in vivo: role of Ets-1 in the regulation of the PI3K/AKT/MMP-1 pathway.; Angiocidal effect of Cyclosporin A: a new therapeutic approach for pathogenic angiogenesis.; Induction of RANKL expression and osteoclast maturation by the binding of fibroblast growth factor 2 to heparan sulfate proteoglycan on rheumatoid synovial fibroblasts.; Acidic fibroblast growth factor in synovial cells.; Characterization of tissue outgrowth developed in vitro in patients with rheumatoid arthritis: involvement of T cells in the development of tissue outgrowth.; Lack of FGF-1 overexpression during autoimmune nephritis in the kidneys of MRL lpr/lpr mice.; Fibroblast growth factor-1 (FGF-1) enhances IL-2 production and nuclear translocation of NF-kappaB in FGF receptor-bearing Jurkat T cells.; Cloning and characterization of a novel upstream untranslated exon of the mouse Fgf-1 gene.; Cloning and characterization of the mouse Fgf-1 gene.; A novel in vitro assay for human angiogenesis.; Expression and functional expansion of fibroblast growth factor receptor T cells in rheumatoid synovium and peripheral blood of patients with rheumatoid arthritis.; Environmental influences on fatty acid composition of membranes from autoimmune MRL lpr/lpr mice.; Costimulation of human CD4+ T cells by fibroblast growth factor-1 (acidic fibroblast growth factor).; Detection of T cells responsive to a vascular growth factor in rheumatoid arthritis.; Coexpression of phosphotyrosine-containing proteins, platelet-derived growth factor-B, and fibroblast growth factor-1 in situ in synovial tissues of patients with rheumatoid arthritis and Lewis rats with adjuvant or streptococcal cell wall arthritis.; Platelet-derived growth factors and heparin-binding (fibroblast) growth factors in the synovial tissue pathology of rheumatoid arthritis.; Fibroblast growth factors: from genes to clinical applications.; Production of platelet derived growth factor B chain (PDGF-B/c-sis) mRNA and immunoreactive PDGF B-like polypeptide by rheumatoid synovium: coexpression with heparin binding acidic fibroblast growth factor-1.; Detection of high levels of heparin binding growth factor-1 (acidic fibroblast growth factor) in inflammatory arthritic joints.

Enriched pathways

Looks for enriched pathways with gene sets using enrichR.

lymphoid_pathways <- enriched_pathways(
  genes=c("LAMP5", "LINC01480", "FAM92B", "SLAMF6", "CEP128",
          "FKBP11", "CRTAM", "ISG20", "ZBP1", "TMEM229B",
          "FAM46C", "XBP1", "APOBEC3G", "TNIK", "CD2", "SP140",
          "ACOXL", "PTPRCAP", "PDCD1", "KCNN3", "GZMK",
          "IGFLR1", "SH2D2A", "PIM2", "TPST2"),
  libraries = c('Pathways'),
  dbs=NULL,
  check_for_updates = FALSE)

If that doesn’t work it may be because the website is down. This happens occasionally. You can check by using:

listEnrichrDbs()

Plots

lymphoid_pathways$plot

eQTL Catalogue

eqtl_table <- associated_eqtl(genes=c("ENSG00000164308"), p_cutoff=1)

## [1] "Looking at SNPs"
## [1] "Looking at Genes"

kable(eqtl_table, row.names=F)

rsid chromosome molecular\_trait\_id gene\_id tissue qtl\_group pvalue neg\_log10\_pvalue se beta median\_tpm study\_id type alt position ac maf variant ref r2 an rs57584041 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.142464 0.8462949 0.862435 1.2793100 14.576 Alasoo\_2018 SNP C 95877044 5 0.0297619 chr5\_95877044\_T\_C T 0.87667 168 rs6556892 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.299611 0.5234422 0.338419 0.3536530 14.576 Alasoo\_2018 SNP A 95878071 56 0.3333330 chr5\_95878071\_C\_A C 0.92606 168 rs55763081 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.324924 0.4882182 1.103560 -1.0940300 14.576 Alasoo\_2018 SNP G 95876702 3 0.0178571 chr5\_95876702\_A\_G A 0.81827 168 rs61540882 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.325028 0.4880792 1.103460 -1.0936900 14.576 Alasoo\_2018 INDEL T 95876577 3 0.0178571 chr5\_95876577\_TAAA\_T TAAA 0.81754 168 rs796285486 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.325028 0.4880792 1.103460 -1.0936900 14.576 Alasoo\_2018 INDEL T 95876577 3 0.0178571 chr5\_95876577\_TAAA\_T TAAA 0.81754 168 rs154457 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.347034 0.4596280 0.376044 0.3560110 14.576 Alasoo\_2018 SNP A 95876181 130 0.2261900 chr5\_95876181\_G\_A G 0.93729 168 rs154458 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.354738 0.4500923 0.375816 0.3501150 14.576 Alasoo\_2018 SNP T 95876288 130 0.2261900 chr5\_95876288\_C\_T C 0.94067 168 rs154456 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.355252 0.4494635 0.375702 0.3496330 14.576 Alasoo\_2018 SNP T 95876161 130 0.2261900 chr5\_95876161\_A\_T A 0.94121 168 rs144088066 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.358421 0.4456066 1.143510 -1.0571400 14.576 Alasoo\_2018 INDEL C 95878406 3 0.0178571 chr5\_95878406\_CTCT\_C CTCT 0.74292 168 rs17085223 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.363755 0.4391910 1.139710 -1.0419100 14.576 Alasoo\_2018 SNP T 95877713 3 0.0178571 chr5\_95877713\_G\_T G 0.77062 168 rs113842599 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.402339 0.3954079 1.153880 -0.9722280 14.576 Alasoo\_2018 SNP G 95878112 3 0.0178571 chr5\_95878112\_C\_G C 0.70249 168 rs749046156 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.409355 0.3878999 0.958131 0.7952580 14.576 Alasoo\_2018 INDEL GT 95877403 5 0.0297619 chr5\_95877403\_G\_GT G 0.65265 168 rs397957177 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.409355 0.3878999 0.958131 0.7952580 14.576 Alasoo\_2018 INDEL GT 95877403 5 0.0297619 chr5\_95877403\_G\_GT G 0.65265 168 rs1256088833 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.482832 0.3162040 1.163830 -0.8210970 14.576 Alasoo\_2018 INDEL G 95877403 2 0.0119048 chr5\_95877403\_GT\_G GT 0.58191 168 rs111471052 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.482832 0.3162040 1.163830 -0.8210970 14.576 Alasoo\_2018 INDEL G 95877403 2 0.0119048 chr5\_95877403\_GT\_G GT 0.58191 168 rs154454 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.537536 0.2695924 0.385196 0.2386650 14.576 Alasoo\_2018 SNP G 95875943 133 0.2083330 chr5\_95875943\_C\_G C 0.96320 168 rs11372327 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.710863 0.1482141 0.818859 -0.3047850 14.576 Alasoo\_2018 INDEL AC 95878741 162 0.0357143 chr5\_95878741\_A\_AC A 0.91136 168 rs397998782 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.710863 0.1482141 0.818859 -0.3047850 14.576 Alasoo\_2018 INDEL AC 95878741 162 0.0357143 chr5\_95878741\_A\_AC A 0.91136 168 rs154459 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.738887 0.1314220 0.332555 -0.1112910 14.576 Alasoo\_2018 SNP T 95876578 72 0.4285710 chr5\_95876578\_A\_T A 0.94702 168 rs154455 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.803819 0.0948417 0.335028 -0.0835397 14.576 Alasoo\_2018 SNP T 95876057 74 0.4404760 chr5\_95876057\_C\_T C 0.96802 168

SNP Annotations

omicAnnotations can also be used to find out more info about SNPs.

GWAS Catalogue

gwas_traits <- associated_traits(snps = c("rs2910686", "rs7329174"))

kable(gwas_traits, row.names = F)

SNPs Associated\_traits rs2910686 neutrophil count; ankylosing spondylitis; crohn’s disease; psoriasis; sclerosing cholangitis; ulcerative colitis rs7329174 systemic lupus erythematosus; crohn’s disease

eQTL Catalogue

eqtl_table <- associated_eqtl(snps = c("rs2910686", "rs7329174"),
                              p_cutoff = 0.05)

## [1] "Looking at SNPs"
## [1] "Looking at Genes"

kable(eqtl_table, row.names = F)

rsid chromosome molecular\_trait\_id gene\_id tissue qtl\_group pvalue neg\_log10\_pvalue se beta median\_tpm study\_id type alt position ac maf variant ref r2 an rs2910686 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.0000000 32.033736 0.1084150 2.3491000 14.576 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs2910686 5 ENSG00000164308 ENSG00000164308 CL\_0000235 macrophage\_IFNg 0.0000000 28.094117 0.1188220 2.2120400 14.576 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs2910686 5 ENSG00000164307 ENSG00000164307 CL\_0000235 macrophage\_IFNg 0.0001333 3.875124 0.0474250 -0.1917860 50.151 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs7329174 13 ENSG00000102760 ENSG00000102760 UBERON\_0001013 adipose\_naive 0.0006357 3.196736 0.0714243 0.2470190 445.066 FUSION SNP G 40983974 38 0.0701107 rs7329174 A 1.00000 542 rs2910686 5 ENSG00000247121 ENSG00000247121 CL\_0000235 macrophage\_IFNg 0.0007469 3.126725 0.0779628 -0.2749620 1.260 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs2910686 5 ENSG00000164307 ENSG00000164307 CL\_0000235 macrophage\_IFNg 0.0008048 3.094338 0.0611752 -0.2143270 50.151 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs2910686 5 ENSG00000113441 ENSG00000113441 CL\_0000235 macrophage\_IFNg 0.0012984 2.886608 0.0291856 0.0978188 14.738 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168 rs7329174 13 ENSG00000278390 ENSG00000278390 UBERON\_0009834 brain 0.0053281 2.273430 0.0627828 -0.1757720 3.801 BrainSeq SNP G 40983974 39 0.0407098 rs7329174 A 0.93510 958 rs7329174 13 ENSG00000102743 ENSG00000102743 UBERON\_0001013 adipose\_naive 0.0291754 1.534983 0.0500392 -0.1097560 3.886 FUSION SNP G 40983974 38 0.0701107 rs7329174 A 1.00000 542 rs2910686 5 ENSG00000113441 ENSG00000113441 CL\_0000235 macrophage\_naive 0.0482241 1.316736 0.0258004 0.0518735 14.738 Alasoo\_2018 SNP C 96916885 68 0.4047620 rs2910686 T 0.99787 168

GTex

g2s <- data.frame("Genes"=c("ERAP2", "ERAP2", "HLA-DRB9"), 
                  "Snps"=c("chr5_96916728_G_A", "chr5_96916885_T_C", 
                           "chr6_32620055_A_G"))

df <- gtex_eqtl(gene_snp_pairs = g2s)

library(ComplexHeatmap)

hm <- gtex_heatmap(df)
draw(hm, heatmap_legend_side = "left")

KatrionaGoldmann/omicAnnotations documentation built on June 23, 2022, 2:15 p.m.