Multi-task learning uncovers robust translation cis-regulatory features
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE201766 | GEO | 2022/04/28
REPOSITORIES: GEO
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Project description:Multi-task learning uncovers robust translation cis-regulatory features
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Project description:Gene expression is determined by genomic elements called enhancers, which contain short motifs bound by different transcription factors (TFs). However, how enhancer sequences and TF motifs relate to enhancer activity is unknown and general sequence requirements for enhancers or comprehensive sets of important enhancer sequence elements have remained elusive. Here, we computationally dissect thousands of functional enhancer sequences from three different Drosophila cell lines. We find that the enhancers display distinct cis-regulatory sequence signatures, which are predictive of the enhancersM-bM-^@M-^Y cell type-specific or broad activities. These signatures contain transcription factor motifs and a novel class of enhancer sequence elements, dinucleotide repeat motifs (DRMs). DRMs are highly enriched in enhancers, particularly in enhancers that are broadly active across different cell types. We experimentally validate the importance of the identified TF motifs and DRMs for enhancer function and show that they can be sufficient to create an active enhancer de novo from non-functional sequence. The function of DRMs as a novel class of general enhancer features that are also enriched in human regulatory regions might explain their implication in several diseases and provides important insights into gene regulation. STARR-seq was performed in BG3 cells with paired-end sequencing in two replicates and respective inputs.
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Project description:Gene expression is determined by genomic elements called enhancers, which contain short motifs bound by different transcription factors (TFs). However, how enhancer sequences and TF motifs relate to enhancer activity is unknown and general sequence requirements for enhancers or comprehensive sets of important enhancer sequence elements have remained elusive. Here, we computationally dissect thousands of functional enhancer sequences from three different Drosophila cell lines. We find that the enhancers display distinct cis-regulatory sequence signatures, which are predictive of the enhancers’ cell type-specific or broad activities. These signatures contain transcription factor motifs and a novel class of enhancer sequence elements, dinucleotide repeat motifs (DRMs). DRMs are highly enriched in enhancers, particularly in enhancers that are broadly active across different cell types. We experimentally validate the importance of the identified TF motifs and DRMs for enhancer function and show that they can be sufficient to create an active enhancer de novo from non-functional sequence. The function of DRMs as a novel class of general enhancer features that are also enriched in human regulatory regions might explain their implication in several diseases and provides important insights into gene regulation.
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2017-09-27 | GSE104252 | GEO
Project description:Regulation of gene expression is mediated by combinations of DNA binding transcription factors that work in concert to recruit transcriptional machinery. Each cell type expresses hundreds of sequence-specific transcription factors, many of which recognize identical or similar DNA sequences. Such factors can play both redundant and non-redundant roles, but mechanisms determining overlapping or distinct biological outcomes are largely unknown. Here, we implement a machine learning approach to investigate how local combinations of sequence motifs influence the genome wide binding patterns of different members of the AP-1 transcription factor family in macrophages. Significant motifs associated with family member specific binding patterns were validated by assessing effects of motif mutations in different strains of mice. We further confirmed the prediction of PPARg to be preferentially associated with the specific binding pattern of cJun using PPARg knockout macrophages. Together, our results provide evidence that unique binding patterns of AP-1 family members result in part from the corresponding unique ensembles of nearby regulatory elements embedded within enhancers and promoters, and that these elements can be identified by machine learning models trained using genomic sequence.
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