DNA Motif Databases and Their Uses

Stormo, Gary D.

doi:10.1002/0471250953.bi0215s51

Cited by 20 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Though ChIP can experimentally identify TF-bound regions, existing data sets in the Drosophila embryo are low resolution, with ~100 base pair peaks (Li et al, 2008; MacArthur et al, 2009; Roy et al, 2010), which are longer than the ~10 bp TF binding sites (Zhu et al, 2011). Therefore, we predict TF binding sites using experimentally measured binding motifs (Stormo, 2015). To select a threshold above which a sequence is deemed a “true” binding site, we develop a principled approach, scoring the aligned sequences used to create the motifs and setting a threshold such that 75% of these aligned sequences are predicted as “true” (see Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

An Enhancer's Length and Composition Are Shaped by Its Regulatory Task

Wunderlich

2017

Front. Genet.

View full text Add to dashboard Cite

Enhancers drive the gene expression patterns required for virtually every process in metazoans. We propose that enhancer length and transcription factor (TF) binding site composition—the number and identity of TF binding sites—reflect the complexity of the enhancer's regulatory task. In development, we define regulatory task complexity as the number of fates specified in a set of cells at once. We hypothesize that enhancers with more complex regulatory tasks will be longer, with more, but less specific, TF binding sites. Larger numbers of binding sites can be arranged in more ways, allowing enhancers to drive many distinct expression patterns, and therefore cell fates, using a finite number of TF inputs. We compare ~100 enhancers patterning the more complex anterior-posterior (AP) axis and the simpler dorsal-ventral (DV) axis in Drosophila and find that the AP enhancers are longer with more, but less specific binding sites than the (DV) enhancers. Using a set of ~3,500 enhancers, we find enhancer length and TF binding site number again increase with increasing regulatory task complexity. Therefore, to be broadly applicable, computational tools to study enhancers must account for differences in regulatory task.

show abstract

Section: Resultsmentioning

confidence: 99%

An Enhancer's Length and Composition Are Shaped by Its Regulatory Task

Wunderlich

2017

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…To identify the local sequence and structure properties that determine specificity, we fit the entire distribution of K A,rel values to two analytical models of RNA sequence specificity. First, a position weight matrix (PWM) model was used that only considers the identity of a nucleobase at each position in the loop (28). The affinity distribution was also fit to a PWM model that includes interaction coefficients (IC values) between nucleobases that account for positive and negative effects of sequence variation between positions in the binding site (29,30).…”

Section: Ess3mentioning

confidence: 99%

Rules of RNA specificity of hnRNP A1 revealed by global and quantitative analysis of its affinity distribution

Jain

Lin

Morgan

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a multipurpose RNA-binding protein (RBP) involved in normal and pathological RNA metabolism. Transcriptome-wide mapping and in vitro evolution identify consensus hnRNP A1 binding motifs; however, such data do not reveal how surrounding RNA sequence and structural context modulate affinity. We determined the affinity of hnRNP A1 for all possible sequence variants (n = 16,384) of the HIV exon splicing silencer 3 (ESS3) 7-nt apical loop. Analysis of the affinity distribution identifies the optimal motif 5′-YAG-3′ and shows how its copy number, position in the loop, and loop structure modulate affinity. For a subset of ESS3 variants, we show that specificity is determined by association rate constants and that variants lacking the minimal sequence motif bind competitively with consensus RNA. Thus, the results reveal general rules of specificity of hnRNP A1 and provide a quantitative framework for understanding how it discriminates between alternative competing RNA ligands in vivo.hnRNP A1 | protein-RNA specificity | RNA structure | thermodynamics | binding kinetics G ene expression is regulated by an ensemble of protein-RNA complexes that assemble and disassemble throughout the lifetime of a transcript (1-5). Knowledge of the determinants of RNA-binding protein (RBP) specificity is therefore essential to understanding the relative affinity for sites of association within the transcriptome. A characteristic feature of many RBPs is their ability to elicit biological function by binding at sites in RNAs that vary significantly in sequence and local structure. This broad specificity of RBPs challenges the simplistic description of RNA binding as either specific or nonspecific. Global profiling methods provide consensus sequence motifs that represent preferential binding sites by RBPs in the transcriptome (6-11). Although powerful, such approaches are usually nonquantitative. Moreover, they do not readily provide information on how surrounding sequence identity or positioning of preferred sequences within higher order structure alters affinity. Methods such as high-throughput sequencing kinetics (HTS-KIN), RNA Bind-n-Seq, and RNA MaP have recently been developed that allow the affinities and reaction kinetics of thousands of RNAs to be measured simultaneously (6,(12)(13)(14)(15). These methods can potentially provide global information on the surrounding context of a given sequence motif; however, they have yet to see wide application for structure/function studies of RNA specificity.Heterogeneous nuclear ribonucleoproteins (hnRNPs) represent a diverse family of RBPs that are implicated at most stages of posttranscriptional gene regulation (16,17). The prototypical member of this family, hnRNP A1, regulates alternative splicing, nuclear export, translation, and other RNA processing events (17). HnRNP A1 has a modular domain organization consisting of tandem RNA recognition motifs (RRMs), collectively referred to as unwinding protein 1 (UP1), and an intrinsically di...

show abstract

“…The precise location of binding sites could be predicted by computational methods based on binding motif models such as positional weight matrices (PWMs). Direct information on binding specificities is known for about 30–50% of ∼1400 human TFs ( 8 ), and is stored in many proprietary and open access collections such as JASPAR ( 9 ), TRANSFAC ( 10 ), SwissRegulon ( 11 ), HOCOMOCO ( 12 ) and others ( 13 ). From the beginning, the idea of HOCOMOCO was to provide a single binding model for each transcription factor, except for TFs exhibiting two distinctly different and well-confirmed binding specificities.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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

DNA Motif Databases and Their Uses

Cited by 20 publications

References 18 publications

An Enhancer's Length and Composition Are Shaped by Its Regulatory Task

An Enhancer's Length and Composition Are Shaped by Its Regulatory Task

Rules of RNA specificity of hnRNP A1 revealed by global and quantitative analysis of its affinity distribution

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

Contact Info

Product

Resources

About