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Transcription factor binding motifs
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The human CGGBP1 binds to GC-rich regions and interspersed repeats, maintains homeostasis of stochastic cytosine methylation and determines DNA-binding of CTCF. Interdependence between regulation of cytosine methylation and CTCF occupancy by CGGBP1 remains unknown.
21p
vihagrid
30-01-2023
7
3
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Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge” in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance.
17p
vigalileogalilei
27-02-2022
19
1
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Understanding the contributions of transcription factor DNA binding sites to transcriptional enhancers is a significant challenge. We developed Quantitative enhancer-FACS-Seq for highly parallel quantification of enhancer activities from a genomically integrated reporter in Drosophila melanogaster embryos. We investigate the contributions of the DNA binding motifs of four poorly characterized TFs to the activities of twelve embryonic mesodermal enhancers.
24p
viarchimedes
26-01-2022
10
0
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The binding of transcription factors (TF) to genomic targets is critical in the regulation of gene expression. Short, double-stranded DNA sequence motifs are routinely implicated in TF recruitment, but many questions remain on how binding site specificity is governed.
15p
viarchimedes
26-01-2022
12
0
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Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.
18p
viarchimedes
26-01-2022
9
0
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It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types.
8p
vilarryellison
29-10-2021
10
0
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Topological association domains (TADs) are chromosomal domains characterised by frequent internal DNA-DNA interactions. The transcription factor CTCF binds to conserved DNA sequence patterns called CTCF binding motifs to either prohibit or facilitate chromosomal interactions. TADs and CTCF binding motifs control gene expression, but they are not yet well defined in the bovine genome. In this paper, we sought to improve the annotation of bovine TADs and CTCF binding motifs, and assess whether the new annotation can reduce the search space for cis-regulatory variants.
17p
vibeauty
23-10-2021
21
0
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A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57–74, 2012; Nat 507:462–70, 2014; Nat 507:455–61, 2014; Nat 518:317–30, 2015).
9p
vibeauty
23-10-2021
8
1
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Identification of transcription factor binding sites (also called ‘motif discovery’) in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites.
14p
viwyoming2711
16-12-2020
18
1
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The Hedgehog (Hh) signaling pathway, acting through three homologous transcription factors (GLI1, GLI2, GLI3) in vertebrates, plays multiple roles in embryonic organ development and adult tissue homeostasis. At the level of the genome, GLI factors bind to specific motifs in enhancers, some of which are hundreds of kilobases removed from the gene promoter.
11p
viwyoming2711
16-12-2020
13
1
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Identifying repeated factors that occur in a string of letters or common factors that occur in a set of strings represents an important task in computer science and biology. Such patterns are called motifs, and the process of identifying them is called motif extraction.
12p
vikentucky2711
26-11-2020
10
1
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Familial binding profiles (FBPs) represent the average binding specificity for a group of structurally related DNA-binding proteins. The construction of such profiles allows the classification of novel motifs based on similarity to known families, can help to reduce redundancy in motif databases and de novo prediction algorithms, and can provide valuable insights into the evolution of binding sites.
12p
vikentucky2711
26-11-2020
14
2
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In most sequenced organisms the number of known regulatory genes (e.g., transcription factors (TFs)) vastly exceeds the number of experimentally-verified regulons that could be associated with them. At present, identification of TF regulons is mostly done through comparative genomics approaches.
10p
vikentucky2711
24-11-2020
9
1
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For many years now, binding preferences of Transcription Factors have been described by so called motifs, usually mathematically defined by position weight matrices or similar models, for the purpose of predicting potential binding sites.
14p
vikentucky2711
24-11-2020
13
4
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Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question.
15p
vioklahoma2711
19-11-2020
11
2
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Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease.
12p
vioklahoma2711
19-11-2020
9
0
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Topic models are statistical algorithms which try to discover the structure of a set of documents according to the abstract topics contained in them. Here we try to apply this approach to the discovery of the structure of the transcription factor binding sites (TFBS) contained in a set of biological sequences, which is a fundamental problem in molecular biology research for the understanding of transcriptional regulation.
10p
vioklahoma2711
19-11-2020
12
2
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Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process.
10p
vioklahoma2711
19-11-2020
11
1
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Gene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks.
11p
viflorida2711
30-10-2020
11
2
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De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs.
13p
viconnecticut2711
29-10-2020
13
1
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