Coexpression cluster:C730

Full id: C730_migratory_cord_Mast_B_b_CD14_CD19

Phase1 CAGE Peaks

Hg19::chr11:102188181..102188222,+	p1@BIRC3
Hg19::chr12:97286249..97286254,+	p@chr12:97286249..97286254 +
Hg19::chr13:44272246..44272260,-	p@chr13:44272246..44272260 -
Hg19::chr2:231808435..231808449,+	p@chr2:231808435..231808449 +
Hg19::chr2:231808494..231808516,+	p@chr2:231808494..231808516 +
Hg19::chr2:231808538..231808549,+	p@chr2:231808538..231808549 +
Hg19::chr2:231808553..231808564,+	p@chr2:231808553..231808564 +
Hg19::chr3:119278446..119278477,-	p1@CD80
Hg19::chr3:119278493..119278513,-	p3@CD80
Hg19::chr3:71114563..71114584,-	p49@FOXP1
Hg19::chr6:138192133..138192144,+	p19@TNFAIP3

Enriched pathways on this co-expression cluster<b>Summary:</b><br>Canonical pathway gene sets were compiled from Reactome, Wikipathways and KEGG. For the major signaling pathways, the transcriptionally-regulated genes (downstream targets) were obtained from Netpath. Combined, the canonical pathways and downstream targets totaled 489 human gene sets. The corresponding M. musculus gene sets were inferred by homology using the HomoloGene database. Enrichment for each of the canonical 489 pathways and gene sets included in the co-expression cluster was assessed by the hypergeometric probability. The resulting P values were also then adjusted by the Benjamini-Hochberg method for multiple comparisons.<br><b>Analyst: </b>Emmanuel Dimont<br><br>link to source dataset<br>data

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p.value	FDR	nGenes	nPathway	Name
2.51303723965872e-05	0.0159075257270397	2	59	NOD-like receptor signaling pathway (KEGG):04621
0.000142368937182235	0.030165417735855	2	140	Regulation of toll-like receptor signaling pathway (Wikipathways):WP1449
0.000256622577651287	0.0324884183306529	2	188	TNF-alpha/NF-kB Signaling Pathway (Wikipathways):WP231
0.000190618753465118	0.030165417735855	2	162	TNF alpha/NF-kB down reg. targets (Netpath):NetPath_9
0.000438747620965328	0.0462878740118421	2	246	B Cell Receptor up reg. targets (Netpath):NetPath_12
0.000174509717997661	0.030165417735855	2	155	IL-1 up reg. targets (Netpath):NetPath_13

Enriched Gene Ontology terms on this co-expression cluster<b>Summary:</b> Results for GOStat analysis on co-expressed clusters. Each cluster with promoters mapping to at least two different genes was analysed with GOStat (PMID: 14962934) with default parameter. <br><b>Analyst:</b> Erik Arner<br><br>link to source dataset<br>data

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GO ID	GO name	FDR corrected p-value
GO:0006916	anti-apoptosis	0.00846701689959302
GO:0045627	positive regulation of T-helper 1 cell differentiation	0.00846701689959302
GO:0043066	negative regulation of apoptosis	0.00846701689959302
GO:0043069	negative regulation of programmed cell death	0.00846701689959302
GO:0045425	positive regulation of granulocyte macrophage colony-stimulating factor biosynthetic process	0.00846701689959302
GO:0045625	regulation of T-helper 1 cell differentiation	0.00846701689959302
GO:0045624	positive regulation of T-helper cell differentiation	0.00846701689959302
GO:0045423	regulation of granulocyte macrophage colony-stimulating factor biosynthetic process	0.00846701689959302
GO:0045063	T-helper 1 cell differentiation	0.00846701689959302
GO:0048869	cellular developmental process	0.00846701689959302
GO:0030154	cell differentiation	0.00846701689959302
GO:0032604	granulocyte macrophage colony-stimulating factor production	0.00846701689959302
GO:0042253	granulocyte macrophage colony-stimulating factor biosynthetic process	0.00846701689959302
GO:0043124	negative regulation of I-kappaB kinase/NF-kappaB cascade	0.0088037535985753
GO:0050794	regulation of cellular process	0.0088037535985753
GO:0045622	regulation of T-helper cell differentiation	0.0088037535985753
GO:0050789	regulation of biological process	0.0088037535985753
GO:0002294	CD4-positive, alpha-beta T cell differentiation during immune response	0.0088037535985753
GO:0002292	T cell differentiation during immune response	0.0088037535985753
GO:0042093	T-helper cell differentiation	0.0088037535985753
GO:0002366	leukocyte activation during immune response	0.0088037535985753
GO:0002285	lymphocyte activation during immune response	0.0088037535985753
GO:0002293	alpha-beta T cell differentiation during immune response	0.0088037535985753
GO:0002286	T cell activation during immune response	0.0088037535985753
GO:0002263	cell activation during immune response	0.0088037535985753
GO:0043367	CD4-positive, alpha beta T cell differentiation	0.00896639012394077
GO:0045086	positive regulation of interleukin-2 biosynthetic process	0.00896639012394077
GO:0042088	T-helper 1 type immune response	0.00943176012279184
GO:0065007	biological regulation	0.00993040632193333
GO:0042981	regulation of apoptosis	0.0100587672340465
GO:0043067	regulation of programmed cell death	0.0100587672340465
GO:0045076	regulation of interleukin-2 biosynthetic process	0.0100587672340465
GO:0015026	coreceptor activity	0.0100587672340465
GO:0042094	interleukin-2 biosynthetic process	0.0100587672340465
GO:0046632	alpha-beta T cell differentiation	0.0100587672340465
GO:0050731	positive regulation of peptidyl-tyrosine phosphorylation	0.0103901080152437
GO:0046631	alpha-beta T cell activation	0.0112976251774761
GO:0032623	interleukin-2 production	0.0121571738180355
GO:0042327	positive regulation of phosphorylation	0.0129724438466746
GO:0045937	positive regulation of phosphate metabolic process	0.0136838464624472
GO:0001934	positive regulation of protein amino acid phosphorylation	0.0136838464624472
GO:0033240	positive regulation of amine metabolic process	0.0136838464624472
GO:0045764	positive regulation of amino acid metabolic process	0.0136838464624472
GO:0009966	regulation of signal transduction	0.0136838464624472
GO:0050730	regulation of peptidyl-tyrosine phosphorylation	0.0136838464624472
GO:0032502	developmental process	0.0143908452919003
GO:0006915	apoptosis	0.0145299318049594
GO:0012501	programmed cell death	0.0145299318049594
GO:0008219	cell death	0.0154497464175671
GO:0016265	death	0.0154497464175671
GO:0042108	positive regulation of cytokine biosynthetic process	0.0173298119070996
GO:0030217	T cell differentiation	0.0173298119070996
GO:0018108	peptidyl-tyrosine phosphorylation	0.017416768372748
GO:0018212	peptidyl-tyrosine modification	0.0177337429088587
GO:0001932	regulation of protein amino acid phosphorylation	0.0177337429088587
GO:0033238	regulation of amine metabolic process	0.0177337429088587
GO:0006521	regulation of amino acid metabolic process	0.0177337429088587
GO:0045727	positive regulation of translation	0.0200738668320086
GO:0048523	negative regulation of cellular process	0.0203475436230452
GO:0008270	zinc ion binding	0.0207769642665428
GO:0048519	negative regulation of biological process	0.0209150828709926
GO:0042035	regulation of cytokine biosynthetic process	0.0209150828709926
GO:0031328	positive regulation of cellular biosynthetic process	0.0209150828709926
GO:0042325	regulation of phosphorylation	0.0216146228805791
GO:0030098	lymphocyte differentiation	0.0216146228805791
GO:0048468	cell development	0.0216146228805791
GO:0042089	cytokine biosynthetic process	0.021638507126761
GO:0042107	cytokine metabolic process	0.021638507126761
GO:0051174	regulation of phosphorus metabolic process	0.021638507126761
GO:0019220	regulation of phosphate metabolic process	0.021638507126761
GO:0009891	positive regulation of biosynthetic process	0.0216419680822234
GO:0002250	adaptive immune response	0.0228471846603561
GO:0002460	adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains	0.0228471846603561
GO:0051247	positive regulation of protein metabolic process	0.0251974450950482
GO:0002521	leukocyte differentiation	0.0263181054783823
GO:0046914	transition metal ion binding	0.0263418696564084
GO:0042110	T cell activation	0.0279032791360802
GO:0009968	negative regulation of signal transduction	0.0290930201859787
GO:0007165	signal transduction	0.0290930201859787
GO:0043122	regulation of I-kappaB kinase/NF-kappaB cascade	0.0295843340928558
GO:0001816	cytokine production	0.030565078760423
GO:0018193	peptidyl-amino acid modification	0.0323184193172839
GO:0007154	cell communication	0.0346449739125504
GO:0007249	I-kappaB kinase/NF-kappaB cascade	0.0362133505252718
GO:0045595	regulation of cell differentiation	0.0370664343864876
GO:0009967	positive regulation of signal transduction	0.038909666557327
GO:0046649	lymphocyte activation	0.0389617623384476
GO:0007242	intracellular signaling cascade	0.039422872973318
GO:0030097	hemopoiesis	0.0395500346220457
GO:0004842	ubiquitin-protein ligase activity	0.0398340772845374
GO:0008639	small protein conjugating enzyme activity	0.0398732590749438
GO:0005634	nucleus	0.0399241594957774
GO:0048534	hemopoietic or lymphoid organ development	0.0406484365205676
GO:0045321	leukocyte activation	0.041830301086174
GO:0002520	immune system development	0.0418461718831951
GO:0006417	regulation of translation	0.0418616318815704
GO:0031326	regulation of cellular biosynthetic process	0.0447778243296604
GO:0043687	post-translational protein modification	0.0452550749111929
GO:0043169	cation binding	0.0452550749111929
GO:0001775	cell activation	0.045596969066149
GO:0009889	regulation of biosynthetic process	0.046856999686847

Enriched sample ontology terms on this co-expression cluster<b>Summary:</b>To summarize promoter activities (expression profile of a TSS region) across ~1000 samples, we performed enrichment analysis based on FANTOM5 Sample Ontology (FF ontology). The question here is “in which type of samples the promoter is more active”. To answer this question, we compared expressions (TPMs) in the samples associated with a sample ontology term and the rest of the samples by using the Mann-Whitney rank sum test. To summarize ontologies enriched in this co-expression cluster, we ran the same analysis on an averaged expression profile of all promoters that make up. <b>Analyst:</b> Hideya Kawaji <br><br>links to source dataset<br><br>cell_data<br>uberon_data<br><br>

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Cell Type

Ontology term	p-value	n
leukocyte	1.79e-57	140
hematopoietic lineage restricted progenitor cell	6.21e-49	124
nongranular leukocyte	1.58e-46	119
hematopoietic stem cell	2.00e-46	172
angioblastic mesenchymal cell	2.00e-46	172
hematopoietic cell	1.94e-44	182
hematopoietic oligopotent progenitor cell	6.45e-44	165
hematopoietic multipotent progenitor cell	6.45e-44	165
myeloid leukocyte	3.79e-26	76
lymphocyte	1.04e-23	53
common lymphoid progenitor	1.04e-23	53
granulocyte monocyte progenitor cell	2.88e-23	71
myeloid lineage restricted progenitor cell	3.10e-23	70
lymphoid lineage restricted progenitor cell	3.50e-23	52
macrophage dendritic cell progenitor	1.07e-21	65
monopoietic cell	1.69e-20	63
monocyte	1.69e-20	63
monoblast	1.69e-20	63
promonocyte	1.69e-20	63
myeloid cell	3.92e-20	112
common myeloid progenitor	3.92e-20	112
CD14-positive, CD16-negative classical monocyte	8.41e-19	42
classical monocyte	1.30e-18	45
lymphocyte of B lineage	5.07e-14	24
pro-B cell	5.07e-14	24
T cell	1.42e-09	25
pro-T cell	1.42e-09	25
mature alpha-beta T cell	4.10e-08	18
alpha-beta T cell	4.10e-08	18
immature T cell	4.10e-08	18
mature T cell	4.10e-08	18
immature alpha-beta T cell	4.10e-08	18
B cell	6.23e-08	14
dendritic cell	5.88e-07	10
conventional dendritic cell	7.92e-07	8

Uber Anatomy

Ontology term	p-value	n
hematopoietic system	6.15e-31	102
blood island	6.15e-31	102
hemolymphoid system	1.23e-30	112
bone marrow	5.98e-21	80
immune system	6.94e-19	115
bone element	5.68e-17	86
skeletal element	9.95e-12	101
skeletal system	9.95e-12	101
blood	2.00e-07	15
haemolymphatic fluid	2.00e-07	15
organism substance	2.00e-07	15

Overrepresented TFBS (DNA) motifs on this co-expression cluster<b>Summary:</b>The values shown are the p-values for overrepresentation of the motif in this coexpression cluster. So a small p-value means a strong overrepresentation. <b>Analyst:</b> Michiel de Hoon <br><br>link to source data <br> Novel motifs <br>data <br><br> Jaspar motifs <br>data

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Novel motifs

JASPAR motifs

Motifs	-log10(p-value)
{{{tfbs_overrepresentation_jaspar}}}

ENCODE TF ChIP-seq peak enrichment analysis<b>Summary:</b> For each TF and each co-expression cluster, the number of promoters with ENCODE TF ChIP signal was compared with the rest of promoters from the robust set using Fisher's exact test. Clusters with significant ChIP enrichment (q <= 0.05) after Benjamini-Hochberg correction were retained. <br><b>Analyst:</b> Erik Arner<br><br>link to source dataset<br><br>data

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(#promoters = Number of promoters in this coexpression cluster that have ChIP signal of the TF)

TF	#promoters	Enrichment	p-value	q-value
CHD2#1106	5	4.70182856096221	0.00236388215455891	0.0133963583667035
MEF2A#4205	7	11.9275105788644	3.34835558215856e-07	1.66444030768709e-05
MYC#4609	7	3.32327028193326	0.00150133599624924	0.00959068017079174
NFKB1#4790	10	4.98914856744894	3.69910541950576e-07	1.8223154288102e-05
POU2F2#5452	7	5.79480621856342	4.24433119609668e-05	0.000759550558240475
SPI1#6688	6	4.47508555010331	0.000874650134001153	0.00652482752463903
TBP#6908	8	2.69583322697919	0.00204085211034922	0.0118505812049795
YY1#7528	7	3.12529047717973	0.00219383950773288	0.0126440265078149
ZBTB33#10009	4	11.5144454654477	0.000274343075920951	0.00292517779541686

Relative expression of the co-expression cluster<b>Summary:</b>Co-expression clusters are compared against FANTOM5 samples to obtain relative expression. <br><b>Analyst:</b>NA<br><br>link to data source<br> data

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This analysis result is provided for C0 - C305 clusters.