Back

Discovery of biased orientations of human DNA motif sequences affecting enhancer-promoter interactions and transcription of genes

Osato, N.

2019-07-13 bioinformatics
10.1101/290825 bioRxiv
Show abstract

BackgroundChromatin interactions are essential in enhancer-promoter interactions (EPIs) and transcriptional regulation. CTCF and cohesin proteins located at chromatin interaction anchors and other DNA-binding proteins such as YY1, ZNF143, and SMARCA4 are involved in chromatin interactions. However, there is still no good overall understanding of proteins associated with chromatin interactions and insulator functions. ResultsHere, I describe a systematic and comprehensive approach for discovering DNA-binding motifs of transcription factors (TFs) that affect EPIs and gene expression. This analysis identified 96 biased orientations [64 forward-reverse (FR) and 52 reverse-forward (RF)] of motifs that significantly affected the expression level of putative transcriptional target genes in monocytes, T cells, HMEC, and NPC and included CTCF, cohesin (RAD21 and SMC3), YY1, and ZNF143; some TFs have more than one motif in databases; thus, the total number is smaller than the sum of FRs and RFs. KLF4, ERG, RFX, RFX2, HIF1, SP1, STAT3, and AP1 were associated with chromatin interactions. Many other TFs were also known to have chromatin-associated functions. The predicted biased orientations of motifs were compared with chromatin interaction data. Correlations in expression level of nearby genes separated by the motif sites were then examined among 53 tissues. ConclusionOne hundred FR and RF orientations associated with chromatin interactions and functions were discovered. Most TFs showed weak directional biases at chromatin interaction anchors and were difficult to identify using enrichment analysis of motifs. These findings contribute to the understanding of chromatin-associated motifs involved in transcriptional regulation, chromatin interactions/regulation, and histone modifications.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 10%
18.5%
2
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 0.7%
8.3%
3
Epigenetics
43 papers in training set
Top 0.1%
8.3%
4
Gene
41 papers in training set
Top 0.1%
4.8%
5
BMC Bioinformatics
383 papers in training set
Top 2%
3.9%
6
Computational and Structural Biotechnology Journal
216 papers in training set
Top 1%
3.9%
7
Frontiers in Genetics
197 papers in training set
Top 2%
3.6%
50% of probability mass above
8
Genes
126 papers in training set
Top 0.3%
3.6%
9
Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms
14 papers in training set
Top 0.1%
3.6%
10
Scientific Reports
3102 papers in training set
Top 38%
3.6%
11
Database
51 papers in training set
Top 0.2%
3.6%
12
PeerJ
261 papers in training set
Top 5%
2.1%
13
BMC Genomic Data
12 papers in training set
Top 0.1%
1.9%
14
F1000Research
79 papers in training set
Top 2%
1.7%
15
International Journal of Molecular Sciences
453 papers in training set
Top 10%
1.3%
16
Journal of Bioinformatics and Systems Biology
14 papers in training set
Top 0.3%
1.2%
17
Bioinformatics
1061 papers in training set
Top 8%
1.2%
18
Briefings in Bioinformatics
326 papers in training set
Top 5%
1.1%
19
Cell Cycle
14 papers in training set
Top 0.3%
0.9%
20
Genomics
60 papers in training set
Top 2%
0.9%
21
Biochemistry and Biophysics Reports
28 papers in training set
Top 1%
0.8%
22
Epigenetics & Chromatin
42 papers in training set
Top 0.3%
0.8%
23
BMC Medical Genomics
36 papers in training set
Top 1%
0.7%
24
PLOS Computational Biology
1633 papers in training set
Top 25%
0.7%
25
Epigenomics
10 papers in training set
Top 0.1%
0.7%
26
Nucleic Acids Research
1128 papers in training set
Top 20%
0.6%
27
Computational Biology and Chemistry
23 papers in training set
Top 0.7%
0.6%
28
BioData Mining
15 papers in training set
Top 1%
0.6%