Low early B-cell factor 1 (EBF1) activity in human subcutaneous adipose tissue is linked to a pernicious metabolic profile

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

“…This result reinforces the previously known importance of methylation in these biological processes and validates the ability of DeepCpG to identify motifs that are specifically associated with methylation. DeepCpG also identified important tissue-associated motifs such as EBF1 in adipose, ASCL2 in muscle, and FOXA1, TCF12, and NRF1 in pancreatic islets, which have also been shown to be involved in regulating differentiation and development in their respective cell types [72][73][74][75][76][77][78][79][80][81][82][83][84][85][86].…”

“…On the other hand, DeepCpG, a deep neural network method, was able to identify differences in transcription factor motifs associated with prediction among the different tissues. For example, DeepCpG identified motifs of TFs important to a tissue type, such as EBF1 in adipose [38,39], ASCL2 in muscle [40], and FOXA1 in pancreatic islets [41,42], which have all been reported to be involved in regulating differentiation and development in their respective cell types. Thus, despite its relatively poor performance, DeepCpG may be superior for identifying tissue-specific differences.…”

“…Unlike BoostMe and random forests, DeepCpG was also able to learn motifs that are known to play key regulatory roles in their respective tissues based on previous biological findings. For example, EBF1 was highly reproducible in both adipose tissues, and is known to be an adipocyte-expressed transcription factor that regulates adipocyte morphology and lipolysis [72,73]. ASCL2, which may inhibit myogenesis by antagonizing myogenic regulatory factors [74], was identified in both muscle tissues.…”