ChIPseq in Bioconductor exercises (part 3)
Rockefeller University, Bioinformatics Resource Centre
https://rockefelleruniversity.github.io/RU_ChIPseq/
ChIPseq functional annotation and motif identification.
In this practical we will investigate the motifs and pathways associated with the Ebf1 transcription factor in proB-cells and B-cells.
Pro-B-Cell Ebf1 data has been extracted from GSM499030.
B-Cell Ebf1 data has been extracted from GSE35857.
Data/ebf1_proB.bed
Data/ebf1_B.bed
Load the ebf1 peaks for proB and B cells into R.
Find peaks in B cells which we also present in proB cells.
Extract the sequence 100bp (+/-50bp) around the geometric centre of these peaks, write to FASTA and submit to Meme-ChIP.
Results files from Meme-ChIP can be found here
- Annotate common B cell peaks to genes with TSS set as +/- 500bp
## >> preparing features information... 2025-06-05 20:18:15
## >> identifying nearest features... 2025-06-05 20:18:19
## >> calculating distance from peak to TSS... 2025-06-05 20:18:19
## >> assigning genomic annotation... 2025-06-05 20:18:19
## >> adding gene annotation... 2025-06-05 20:18:33## 'select()' returned 1:many mapping between keys and columns## >> assigning chromosome lengths 2025-06-05 20:18:34
## >> done... 2025-06-05 20:18:34- Create a barplot of the occurrence of differing annotations (“Promoter”, “Distal Intergenic” and “other”).
- Perform a geneset enrichment test of genes with peaks in promoter against BP GO terms using clusterProfiler. Make a dotplot and an enrichment map of the results. Finally export the result.
## 66 genes were dropped because they have exons located on both strands of the same
## reference sequence or on more than one reference sequence, so cannot be
## represented by a single genomic range.
## Use 'single.strand.genes.only=FALSE' to get all the genes in a GRangesList
## object, or use suppressMessages() to suppress this message.
## Warning in emapplot.enrichResult(x, showCategory = showCategory, ...): Use 'cex.params = list(category_label = your_value)' instead of 'cex_label_category'.
## The cex_label_category parameter will be removed in the next version.
- Identify Ebf1 peaks only in Bcells and Ebf1 peaks only in proB cells.
8 Using rGREAT identify the MSigDB Pathways enriched in Ebf1 peaks only in Bcells and Ebf1 peaks only in proB cells.
## [1] "GO" "Phenotype Data and Human Disease"
## [3] "Pathway Data" "Gene Expression"
## [5] "Regulatory Motifs" "Gene Families"## The default enrichment table does not contain informatin of associated genes for
## each input region. You can set `download_by = 'tsv'` to download the complete
## table, but note only the top 500 regions can be retreived. See the following
## link:
##
## https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655401/Export#Export-GlobalExport
##
## Except the additional gene-region association column if taking 'tsv' as the
## source of result, all other columns are the same if you choose 'json' (the
## default) as the source. Or you can try the local GREAT analysis with the function
## `great()`.## [1] "PANTHER Pathway" "BioCyc Pathway" "MSigDB Pathway"## ID
## 1 KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY
## 2 PID_BCR_5PATHWAY
## 3 REACTOME_INTERFERON_ALPHA_BETA_SIGNALING
## 4 REACTOME_DOWNSTREAM_SIGNALING_EVENTS_OF_B_CELL_RECEPTOR_BCR
## 5 REACTOME_SIGNALLING_BY_NGF
## 6 REACTOME_SIGNALING_BY_THE_B_CELL_RECEPTOR_BCR
## 7 REACTOME_SIGNALING_BY_SCF_KIT
## 8 PID_RAC1_REG_PATHWAY
## 9 REACTOME_IMMUNE_SYSTEM
## 10 REACTOME_PIP3_ACTIVATES_AKT_SIGNALING
## name
## 1 B cell receptor signaling pathway
## 2 BCR signaling pathway
## 3 Genes involved in Interferon alpha/beta signaling
## 4 Genes involved in Downstream Signaling Events Of B Cell Receptor (BCR)
## 5 Genes involved in Signalling by NGF
## 6 Genes involved in Signaling by the B Cell Receptor (BCR)
## 7 Genes involved in Signaling by SCF-KIT
## 8 Regulation of RAC1 activity
## 9 Genes involved in Immune System
## 10 Genes involved in PIP3 activates AKT signaling
## Binom_Genome_Fraction Binom_Expected Binom_Observed_Region_Hits Binom_Fold_Enrichment
## 1 0.005806883 12.531250 37 2.952618
## 2 0.006121690 13.210610 38 2.876477
## 3 0.001747780 3.771710 19 5.037503
## 4 0.006124732 13.217170 37 2.799389
## 5 0.020280090 43.764440 83 1.896517
## 6 0.009296625 20.062120 48 2.392569
## 7 0.006275704 13.542970 37 2.732045
## 8 0.004501446 9.714120 30 3.088288
## 9 0.056859840 122.703500 182 1.483250
## 10 0.002103194 4.538692 19 4.186228
## Binom_Region_Set_Coverage Binom_Raw_PValue Binom_Adjp_BH Hyper_Total_Genes
## 1 0.017145510 1.434094e-08 8.743757e-06 74
## 2 0.017608900 1.799178e-08 8.743757e-06 63
## 3 0.008804449 1.988724e-08 8.743757e-06 48
## 4 0.017145510 5.366261e-08 1.536739e-05 90
## 5 0.038461540 5.825394e-08 1.536739e-05 209
## 6 0.022242820 7.137937e-08 1.569156e-05 119
## 7 0.017145510 9.693786e-08 1.826586e-05 73
## 8 0.013901760 1.258219e-07 2.074489e-05 38
## 9 0.084337350 1.415786e-07 2.074913e-05 792
## 10 0.008804449 3.277231e-07 4.075300e-05 27
## Hyper_Expected Hyper_Observed_Gene_Hits Hyper_Fold_Enrichment Hyper_Gene_Set_Coverage
## 1 9.909689 25 2.522784 0.008523696
## 2 8.436627 22 2.607677 0.007500852
## 3 6.427906 11 1.711288 0.003750426
## 4 12.052320 27 2.240232 0.009205592
## 5 27.988170 57 2.036574 0.019434030
## 6 15.935850 35 2.196306 0.011933170
## 7 9.775774 25 2.557342 0.008523696
## 8 5.088759 13 2.554650 0.004432322
## 9 106.060500 141 1.329431 0.048073640
## 10 3.615697 14 3.872006 0.004773270
## Hyper_Term_Gene_Coverage Hyper_Raw_PValue Hyper_Adjp_BH
## 1 0.3378378 6.065365e-06 3.200087e-04
## 2 0.3492063 1.184083e-05 4.338349e-04
## 3 0.2291667 4.924903e-02 1.840212e-01
## 4 0.3000000 3.089307e-05 8.489158e-04
## 5 0.2727273 7.317380e-08 2.244281e-05
## 6 0.2941176 3.668240e-06 2.556315e-04
## 7 0.3424658 4.597072e-06 3.031769e-04
## 8 0.3421053 8.932663e-04 1.132902e-02
## 9 0.1780303 2.077171e-04 4.005688e-03
## 10 0.5185185 2.083329e-06 1.962794e-04## [1] "GO" "Phenotype Data and Human Disease"
## [3] "Pathway Data" "Gene Expression"
## [5] "Regulatory Motifs" "Gene Families"## The default enrichment table does not contain informatin of associated genes for
## each input region. You can set `download_by = 'tsv'` to download the complete
## table, but note only the top 500 regions can be retreived. See the following
## link:
##
## https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655401/Export#Export-GlobalExport
##
## Except the additional gene-region association column if taking 'tsv' as the
## source of result, all other columns are the same if you choose 'json' (the
## default) as the source. Or you can try the local GREAT analysis with the function
## `great()`.## [1] "PANTHER Pathway" "BioCyc Pathway" "MSigDB Pathway"## ID
## 1 REACTOME_METABOLISM_OF_PORPHYRINS
## 2 PID_PDGFRBPATHWAY
## 3 KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY
## 4 REACTOME_HEMOSTASIS
## 5 REACTOME_IMMUNE_SYSTEM
## 6 PID_PI3KCIPATHWAY
## 7 PID_BCR_5PATHWAY
## 8 KEGG_TASTE_TRANSDUCTION
## 9 KEGG_FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS
## 10 REACTOME_DEPOSITION_OF_NEW_CENPA_CONTAINING_NUCLEOSOMES_AT_THE_CENTROMERE
## name
## 1 Genes involved in Metabolism of porphyrins
## 2 PDGFR-beta signaling pathway
## 3 Natural killer cell mediated cytotoxicity
## 4 Genes involved in Hemostasis
## 5 Genes involved in Immune System
## 6 Class I PI3K signaling events
## 7 BCR signaling pathway
## 8 Taste transduction
## 9 Fc gamma R-mediated phagocytosis
## 10 Genes involved in Deposition of New CENPA-containing Nucleosomes at the Centromere
## Binom_Genome_Fraction Binom_Expected Binom_Observed_Region_Hits Binom_Fold_Enrichment
## 1 0.0003517876 1.589376 28 17.616970
## 2 0.0120526400 54.453810 144 2.644443
## 3 0.0066675330 30.123920 91 3.020856
## 4 0.0357002700 161.293800 284 1.760762
## 5 0.0568598400 256.892800 399 1.553177
## 6 0.0037282650 16.844300 62 3.680770
## 7 0.0061216900 27.657790 82 2.964806
## 8 0.0021354530 9.647976 45 4.664191
## 9 0.0072730230 32.859520 88 2.678067
## 10 0.0014112860 6.376189 35 5.489173
## Binom_Region_Set_Coverage Binom_Raw_PValue Binom_Adjp_BH Hyper_Total_Genes
## 1 0.006197432 2.831283e-25 3.734462e-22 14
## 2 0.031872510 2.554682e-24 1.684813e-21 127
## 3 0.020141660 2.290614e-19 1.007107e-16 101
## 4 0.062859670 3.066842e-19 1.011291e-16 424
## 5 0.088313410 1.308965e-17 3.453050e-15 792
## 6 0.013722890 1.835537e-17 4.035122e-15 48
## 7 0.018149620 3.742359e-17 7.051674e-15 63
## 8 0.009960159 1.186513e-16 1.956263e-14 26
## 9 0.019477640 9.529404e-16 1.396587e-13 92
## 10 0.007746791 2.642269e-15 3.485153e-13 51
## Hyper_Expected Hyper_Observed_Gene_Hits Hyper_Fold_Enrichment Hyper_Gene_Set_Coverage
## 1 3.011962 8 2.6560760 0.001697793
## 2 27.322800 67 2.4521640 0.014219020
## 3 21.729160 36 1.6567600 0.007640068
## 4 91.219430 157 1.7211250 0.033319190
## 5 170.391000 250 1.4672140 0.053056030
## 6 10.326730 26 2.5177380 0.005517827
## 7 13.553830 40 2.9511950 0.008488964
## 8 5.593644 8 1.4301950 0.001697793
## 9 19.792900 43 2.1724970 0.009125637
## 10 10.972150 5 0.4556993 0.001061121
## Hyper_Term_Gene_Coverage Hyper_Raw_PValue Hyper_Adjp_BH
## 1 0.57142860 3.888215e-03 2.182364e-02
## 2 0.52755910 1.008313e-14 1.329965e-11
## 3 0.35643560 7.711774e-04 6.826731e-03
## 4 0.37028300 1.334306e-13 8.799748e-11
## 5 0.31565660 1.298131e-11 4.280587e-09
## 6 0.54166670 7.359506e-07 4.044662e-05
## 7 0.63492060 7.825095e-13 3.440433e-10
## 8 0.30769230 1.788232e-01 3.428311e-01
## 9 0.46739130 6.382067e-08 6.475343e-06
## 10 0.09803922 9.916507e-01 1.000000e+00