HTPSELEX--a database of high-throughput SELEX libraries for transcription factor binding sites

Jagannathan, Vidhya; Roulet, E.; Delorenzi, Mauro; Bücher, Philipp

doi:10.1093/nar/gkj049

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…In SELEX-SAGE, the SELEX-screened dsDNA fragments were first digested with a restriction endoenzyme BglII, then the digested dsDNA fragments with stick ends were concatemerized and cloned, finally the cloned DNA was sequenced by cloning DNA sequencing technology. SELEX-SAGE can generate large numbers (O1000) of ligands in a single assay; therefore, this method was called high-throughput SELEX (HTPSELEX), which can originate large volumes of data (Jagannathan et al 2006). However, SELEX-SAGE was still a method dependent on cloning DNA sequencing technology.…”

Section: Selex-seqmentioning

confidence: 99%

In vitro DNA-binding profile of transcription factors: methods and new insights

Wang¹,

Lü²,

Gu³

et al. 2011

Journal of Endocrinology

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The DNA-binding specificity of transcription factors (TFs) has broad impacts on cell physiology, cell development and in evolution. However, the DNA-binding specificity of most known TFs still remains unknown. The specificity of a TF protein is determined by its relative affinity to all possible binding sites. In recent years, the development of several in vitro techniques permits high-throughput determination of relative binding affinity of a TF to all possible k bp-long DNA sequences, thus greatly promoting the characterization of DNA-binding specificity of many known TFs. All DNA sequences that can be bound by a TF with various binding affinities form their DNA-binding profile (DBP). The DBP is important to generate an accurate DNA-binding model, identify all DNA-binding sites and target genes of TFs in the whole genome, and build transcription regulatory network. This study reviewed these techniques, especially two master techniques: double-stranded DNA microarray and systematic evolution of ligands by exponential enrichment in combination with parallel DNA sequencing techniques (SELEX-seq).

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Section: Selex-seqmentioning

confidence: 99%

In vitro DNA-binding profile of transcription factors: methods and new insights

Wang¹,

Lü²,

Gu³

et al. 2011

Journal of Endocrinology

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“…The experimental data includes three dimensional (3D) structures of TFs bound to DNA, immunoprecipitated DNA sequences followed by hybridization to microarray chips (ChIP-chip) [4] or massively parallel sequencing (ChIP-Seq) [5], Systematic Evolution of Ligands by EXponential enrichment (SELEX) [6], [7], or protein-binding microarrays (PBMs) [8]. While each of these methods have proven to be useful, they each have their own drawbacks.…”

Section: Introductionmentioning

confidence: 99%

A Structural-Based Strategy for Recognition of Transcription Factor Binding Sites

Schones

Wang

et al. 2013

PLoS ONE

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Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.

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“…In the development of our dataset, we searched primary sources and online repositories such as TRANSFAC31 for experimentally‐verified DNA binding sites for each of the proteins in our data set. More than 45 repositories and primary sources were used,7, 31–74 providing experimental evidence in the form of footprints, SELEX experiments, and dsDNA microarray assays. The experimentally‐verified DNA binding sites for each protein were then aligned and combined into a single PWM, by setting the probability of a given nucleotide in the PWM to its frequency as observed in the set of DNA binding sites.…”

Section: Methodsmentioning

confidence: 99%

Three enhancements to the inference of statistical protein‐DNA potentials

AlQuraishi

McAdams

2012

Proteins

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The energetics of protein-DNA interactions are often modeled using so-called statistical potentials, that is, energy models derived from the atomic structures of protein-DNA complexes. Many statistical protein-DNA potentials based on differing theoretical assumptions have been investigated, but little attention has been paid to the types of data and the parameter estimation process used in deriving the statistical potentials. We describe three enhancements to statistical potential inference that significantly improve the accuracy of predicted protein-DNA interactions: (i) incorporation of binding energy data of protein-DNA complexes, in conjunction with their X-ray crystal structures, (ii) use of spatially-aware parameter fitting, and (iii) use of ensemble-based parameter fitting. We apply these enhancements to three widely-used statistical potentials and use the resulting enhanced potentials in a structure-based prediction of the DNA binding sites of proteins. These enhancements are directly applicable to all statistical potentials used in protein-DNA modeling, and we show that they can improve the accuracy of predicted DNA binding sites by up to 21%.

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HTPSELEX--a database of high-throughput SELEX libraries for transcription factor binding sites

Cited by 30 publications

References 14 publications

In vitro DNA-binding profile of transcription factors: methods and new insights

In vitro DNA-binding profile of transcription factors: methods and new insights

A Structural-Based Strategy for Recognition of Transcription Factor Binding Sites

Three enhancements to the inference of statistical protein‐DNA potentials

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