Mutations and Binding Sites of Human Transcription Factors

Kamanu, Frederick; Medvedeva, Yulia A.; Schaefer, Ulf; Janković, Boris R.; Archer, John; Bajic, Vladimir B.

doi:10.3389/fgene.2012.00100

Cited by 13 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Condition-specific binding preferences are an important biological property of certain TFs [45] . Polymorphisms and de novo mutations may also alter a sequence of a particular binding site complicating known-TFBS discovery [46] , [47] . Hence, we reasoned that certain ab initio MFs might reflect condition-driven, or protein complex-dependent, deviations from known TFBS models.…”

Section: Resultsmentioning

confidence: 99%

Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

et al. 2014

Self Cite

View full text Add to dashboard Cite

Transcriptional regulation of protein-coding genes is increasingly well-understood on a global scale, yet no comparable information exists for long non-coding RNA (lncRNA) genes, which were recently recognized to be as numerous as protein-coding genes in mammalian genomes. We performed a genome-wide comparative analysis of the promoters of human lncRNA and protein-coding genes, finding global differences in specific genetic and epigenetic features relevant to transcriptional regulation. These two groups of genes are hence subject to separate transcriptional regulatory programs, including distinct transcription factor (TF) proteins that significantly favor lncRNA, rather than coding-gene, promoters. We report a specific signature of promoter-proximal transcriptional regulation of lncRNA genes, including several distinct transcription factor binding sites (TFBS). Experimental DNase I hypersensitive site profiles are consistent with active configurations of these lncRNA TFBS sets in diverse human cell types. TFBS ChIP-seq datasets confirm the binding events that we predicted using computational approaches for a subset of factors. For several TFs known to be directly regulated by lncRNAs, we find that their putative TFBSs are enriched at lncRNA promoters, suggesting that the TFs and the lncRNAs may participate in a bidirectional feedback loop regulatory network. Accordingly, cells may be able to modulate lncRNA expression levels independently of mRNA levels via distinct regulatory pathways. Our results also raise the possibility that, given the historical reliance on protein-coding gene catalogs to define the chromatin states of active promoters, a revision of these chromatin signature profiles to incorporate expressed lncRNA genes is warranted in the future.

show abstract

Section: Resultsmentioning

confidence: 99%

Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many authors propose that variants found in coding regions (intragenic variants) have the greatest potential to be functionally important and more likely to contribute to trait variation [ 27 ]. Out of the variants annotated in intragenic regions, the splice site and synonymous classes are some of the most significant in our study, capturing a large proportion of the variance out of the total variance explained (Fig.…”

Section: Discussionmentioning

confidence: 99%

Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

et al. 2018

View full text Add to dashboard Cite

BackgroundThere are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously.ResultsOur data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small.ConclusionThe results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4617-x) contains supplementary material, which is available to authorized users.

show abstract

“…Information on the precise locations of DNA sites binding transcription factors (TFs) is necessary to reconstruct regulatory networks ( 1 ), as well as to evaluate functional consequences of mutations in DNA regulatory regions ( 2 , 3 ) or interactions between DNA-bound TFs ( 4 ). Conventional methods that experimentally reveal nucleotides contacting regulatory proteins are low throughput, laborious and expensive ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

et al. 2015

Self Cite

View full text Add to dashboard Cite

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

show abstract

Mutations and Binding Sites of Human Transcription Factors

Cited by 13 publications

References 50 publications

Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

Promoter Analysis Reveals Globally Differential Regulation of Human Long Non-Coding RNA and Protein-Coding Genes

Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

Contact Info

Product

Resources

About