Correlated evolution of transcription factors and their binding sites

Yang, Shu; Yalamanchili, Hari Krishna; Li, Xinran; Yao, KM; Sham, Pak C.; Zhang, Michael Q.; Wang, Junwen

doi:10.1093/bioinformatics/btr503

Cited by 25 publications

(26 citation statements)

References 34 publications

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“…While there are many layers of gene regulation that exist between DNA sequence data and expressed proteins, emphasis is often placed on the mechanisms that regulate transcription. There has been much work characterizing the coevolution of transcription factors and their DNA binding elements [8]. However, less is known concerning the evolution of post-transcriptional regulatory elements.…”

Section: Introductionmentioning

confidence: 99%

Evolution of microRNA in primates

et al. 2017

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MicroRNA play an important role in post-transcriptional regulation of most transcripts in the human genome, but their evolution across the primate lineage is largely uncharacterized. A particular miRNA can have one to thousands of messenger RNA targets, establishing the potential for a small change in sequence or overall miRNA structure to have profound phenotypic effects. However, the majority of non-human primate miRNA is predicted solely by homology to the human genome and lacks experimental validation. In the present study, we sequenced thirteen species representing a wide range of the primate phylogeny. Hundreds of miRNA were validated, and the number of species with experimentally validated miRNA was tripled. These species include a sister taxon to humans (bonobo) and basal primates (aye-aye, mouse lemur, galago). Consistent with previous studies, we found the seed region and mature miRNA to be highly conserved across primates, with overall structural conservation of the pre-miRNA hairpin. However, there were a number of interesting exceptions, including a seed shift due to structural changes in miR-501. We also identified an increase in the number of miR-320 paralogs throughout primate evolution. Many of these non-conserved miRNA appear to regulate neuronal processes, illustrating the importance of investigating miRNA to learn more about human evolution.

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Section: Introductionmentioning

confidence: 99%

Evolution of microRNA in primates

et al. 2017

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“…Proteins binding to such regions are called Transcription Factors (TFs) and the binding sites are called Transcription Factor Binding Sites (TFBS). Interactions amongst TFs and TFBSs regulate the transcription of genes differentially on various developmental stages and tissue types [54]. Chromatin Immunoprecipitation followed by high-throughput DNA Sequencing (ChIP-Seq) has been widely used to detect TF-DNA interactions [55].…”

Section: Interactomics and Epigenomicsmentioning

confidence: 99%

The Current Status and Challenges in Computational Analysis of Genomic Big Data

et al. 2015

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“…Our TF-TFBS binding subtype discovery distinguishes itself from the studies from several aspects:

The previous studies focus on a small number (3–4) of specific TF families. Our study is general and large scale on the whole TRANSFAC database (across all possible families) and produces more biologically interesting case studies and novel results.

More importantly, although the previous studies rely on known TF domain information, which may be limited [mainly around 10–20 samples per dataset in (22)], we work on computationally discovered approximate associated TF-TFBS patterns without requiring such annotations. The associated patterns are more conserved, facilitating more convenient analysis than degenerate aligned patterns.…”

Section: Introductionmentioning

confidence: 98%

“…More importantly, although the previous studies rely on known TF domain information, which may be limited [mainly around 10–20 samples per dataset in (22)], we work on computationally discovered approximate associated TF-TFBS patterns without requiring such annotations. The associated patterns are more conserved, facilitating more convenient analysis than degenerate aligned patterns.…”

Section: Introductionmentioning

confidence: 99%

Subtypes of associated protein–DNA (Transcription Factor-Transcription Factor Binding Site) patterns

Chan

Leung

Lee

et al. 2012

Nucleic Acids Research

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In protein–DNA interactions, particularly transcription factor (TF) and transcription factor binding site (TFBS) bindings, associated residue variations form patterns denoted as subtypes. Subtypes may lead to changed binding preferences, distinguish conserved from flexible binding residues and reveal novel binding mechanisms. However, subtypes must be studied in the context of core bindings. While solving 3D structures would require huge experimental efforts, recent sequence-based associated TF-TFBS pattern discovery has shown to be promising, upon which a large-scale subtype study is possible and desirable. In this article, we investigate residue-varying subtypes based on associated TF-TFBS patterns. By re-categorizing the patterns with respect to varying TF amino acids, statistically significant (P values ≤ 0.005) subtypes leading to varying TFBS patterns are discovered without using TF family or domain annotations. Resultant subtypes have various biological meanings. The subtypes reflect familial and functional properties and exhibit changed binding preferences supported by 3D structures. Conserved residues critical for maintaining TF-TFBS bindings are revealed by analyzing the subtypes. In-depth analysis on the subtype pair PKVVIL-CACGTG versus PKVEIL-CAGCTG shows the V/E variation is indicative for distinguishing Myc from MRF families. Discovered from sequences only, the TF-TFBS subtypes are informative and promising for more biological findings, complementing and extending recent one-sided subtype and familial studies with comprehensive evidence.

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Correlated evolution of transcription factors and their binding sites

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 25 publications

References 34 publications

Evolution of microRNA in primates

Evolution of microRNA in primates

The Current Status and Challenges in Computational Analysis of Genomic Big Data

Subtypes of associated protein–DNA (Transcription Factor-Transcription Factor Binding Site) patterns

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