SELEX Data Research Articles

Prediction of cooperative homeodomain DNA binding sites from high-throughput-SELEX data.

Homeodomain proteins constitute one of the largest families of metazoan transcription factors. Genetic studies have demonstrated that homeodomain proteins regulate many developmental processes. Yet, biochemical data reveal that most bind highly similar DNA sequences. Defining how homeodomain proteins achieve DNA binding specificity has therefore been a long-standing goal. Here, we developed a novel computational approach to predict cooperative dimeric binding of homeodomain proteins using High-Throughput (HT) SELEX data. Importantly, we found that 15 of 88 homeodomain factors form cooperative homodimer complexes on DNA sites with precise spacing requirements. Approximately one third of the paired-like homeodomain proteins cooperatively bind palindromic sequences spaced 3 bp apart, whereas other homeodomain proteins cooperatively bind sites with distinct orientation and spacing requirements. Combining structural models of a paired-like factor with our cooperativity predictions identified key amino acid differences that help differentiate between cooperative and non-cooperative factors. Finally, we confirmed predicted cooperative dimer sites in vivo using available genomic data for a subset of factors. These findings demonstrate how HT-SELEX data can be computationally mined to predict cooperativity. In addition, the binding site spacing requirements of select homeodomain proteins provide a mechanism by which seemingly similar AT-rich DNA sequences can preferentially recruit specific homeodomain factors.

Phylogeny and cross-regulation of the YjjM and LeuO transcription factors translated as multiple protein forms from one gene in Escherichia coli

Until recently, no examples of the in-frame translation of several proteins from one gene in bacteria were known. The first one was the VirF transcription factor controlling pathogenicity development in Shigella flexneri and CobB sirtuin in Salmonella enterica. Recently, we observed synthesis of shortened protein products for YjjM (LgoR) and LeuO functioning as transcription factors in Escherichia coli. To determine the evolutionary factors that could lead to the appearance of alternative start codons, we performed phylogenetic analysis and showed that each protein had a unique phylogenetic history, and additional starting methionines appeared only in Enterobacteria. Using the Western-blot analysis of proteins synthesized from the Escherichia coli K-12 MG1655 chromosome with the his-tagged leuO gene two shortened variants of LeuO, corresponding to translation starting from Met34 and Met48 were detected. Synthesis of all three LeuO forms was inhibited in the absence of the yjjM gene, suggesting interplay of these transcription factors. The YjjM recognition motif revealed from the ChIP-seq data appeared to be very similar to that of LeuO, known previously. Taking this into account, we compared ChIP and SELEX data for LeuO and YjjM and found six common targets. At least five of them were confirmed to be under control of these regulators by qRT-PCR. Interestingly, the effects were more prominent during anaerobic growth at 37°C simulating conditions inside a host organism. Two genes, coding for the enterobactin transporter FepA, and a repressor of genes responsible for flagellar biosynthesis and virulence YjjQ, were repressed, mainly by YjjM, only in these conditions, while tsr coding for the chemotaxis receptor protein was more repressed under lower temperature and higher aeration.

In Vitro Transcription Factor Binding Site Predictions Using Support Vector Machine Classification

Transcription factors (TFs) are sequence‐specific DNA‐binding proteins essential in regulating gene expression. Determining TF DNA‐binding specificity can help to study gene regulatory networks within cells and how genetic variation can disrupt normal gene expression. One method for characterizing TF specificity is through Support Vector Machines (SVMs) by analyzing chromatin immunoprecipitation followed by DNA‐sequencing (ChIP‐seq) data. However, this can also be achieved using Systematic Evolution of Ligands by Exponential Enrichment (SELEX) data, a method that also aids in determining TF‐DNA preferences. During this project, I implemented a gapped kmer SVM to study TF‐DNA binding preferences by using data from SELEX‐seq. I used a large scale‐gapped kmer, a sequence‐based SVM for analyzing TF specificity. It works by creating a predictive model that is trained with bound and unbound sequences from SELEX data. For purposes of this project, we used the T‐box transcription factor 5 (TBX5). After training the model for TBX5 and testing its performance, it had an AUROC value of 0.8248, indicating a significant degree of reliability. Likewise, the sequences with highest scores contained motifs for the TBX5. Given these results, we concluded that SVM was successfully implemented. In addition, SELEX data had not been previously used to train SVM based predictive models, meaning SELEX data is compatible and useful for developing predictive models.

DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs.

Transcription factor (TF) DNA-binding is a central mechanism in gene regulation. Biologists would like to know where and when these factors bind DNA. Hence, they require accurate DNA-binding models to enable binding prediction to any DNA sequence. Recent technological advancements measure the binding of a single TF to thousands of DNA sequences. One of the prevailing techniques, high-throughput SELEX, measures protein-DNA binding by high-throughput sequencing over several cycles of enrichment. Unfortunately, current computational methods to infer the binding preferences from high-throughput SELEX data do not exploit the richness of these data, and are under-using the most advanced computational technique, deep neural networks. To better characterize the binding preferences of TFs from these experimental data, we developed DeepSELEX, a new algorithm to infer intrinsic DNA-binding preferences using deep neural networks. DeepSELEX takes advantage of the richness of high-throughput sequencing data and learns the DNA-binding preferences by observing the changes in DNA sequences through the experimental cycles. DeepSELEX outperforms extant methods for the task of DNA-binding inference from high-throughput SELEX data in binding prediction in vitro and is on par with the state of the art in in vivo binding prediction. Analysis of model parameters reveals it learns biologically relevant features that shed light on TFs' binding mechanism. DeepSELEX is available through github.com/OrensteinLab/DeepSELEX/. Supplementary data are available at Bioinformatics online.

Crumple: An Efficient Tool to Explore Thoroughly the RNA Folding Landscape.

The folding landscape for an RNA sequence contains many diverse structures and motifs, which are often sampled rather than completely explored. Today's supercomputers make the complete enumeration of all possible folds for an RNA and a detailed description of the RNA folding landscape a more feasible task. This chapter provides protocols for using the Crumple folding algorithm, an efficient tool to generate all possible non-pseudoknotted folds for an RNA sequence. Crumple in conjunction with Sliding Windows and Assembly can incorporate experimental constraints on the global features of an RNA, such as the minimum number and lengths of helices, which may be determined by crystallography or cryo-electron microscopy. This complete enumeration method is independent of free-energy minimization and allows the user to incorporate experimental data such as chemical probing, SELEX data on RNA-protein binding motifs, and phylogenetic covariation.

APTANI: a computational tool to select aptamers through sequence-structure motif analysis of HT-SELEX data.

Aptamers are synthetic nucleic acid molecules that can bind biological targets in virtue of both their sequence and three-dimensional structure. Aptamers are selected using SELEX, Systematic Evolution of Ligands by EXponential enrichment, a technique that exploits aptamer-target binding affinity. The SELEX procedure, coupled with high-throughput sequencing (HT-SELEX), creates billions of random sequences capable of binding different epitopes on specific targets. Since this technique produces enormous amounts of data, computational analysis represents a critical step to screen and select the most biologically relevant sequences. Here, we present APTANI, a computational tool to identify target-specific aptamers from HT-SELEX data and secondary structure information. APTANI builds on AptaMotif algorithm, originally implemented to analyze SELEX data; extends the applicability of AptaMotif to HT-SELEX data and introduces new functionalities, as the possibility to identify binding motifs, to cluster aptamer families or to compare output results from different HT-SELEX cycles. Tabular and graphical representations facilitate the downstream biological interpretation of results. APTANI is available at http://aptani.unimore.it. silvio.bicciato@unimore.it Supplementary data are available at Bioinformatics online.

Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data

Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear k-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative k-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of k-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for k-mer and PWMs, respectively. Finally, the k-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.

A model for sequential evolution of ligands by exponential enrichment (SELEX) data

A Systematic Evolution of Ligands by EXponential enrichment (SELEX) experiment begins in round one with a random pool of oligonucleotides in equilibrium solution with a target. Over a few rounds, oligonucleotides having a high affinity for the target are selected. Data from a high throughput SELEX experiment consists of lists of thousands of oligonucleotides sampled after each round. Thus far, SELEX experiments have been very good at suggesting the highest affinity oligonucleotide, but modeling lower affinity recognition site variants has been difficult. Furthermore, an alignment step has always been used prior to analyzing SELEX data. We present a novel model, based on a biochemical parametrization of SELEX, which allows us to use data from all rounds to estimate the affinities of the oligonucleotides. Most notably, our model also aligns the oligonucleotides. We use our model to analyze a SELEX experiment containing double stranded DNA oligonucleotides and the transcription factor Bicoid as the target. Our SELEX model outperformed other published methods for predicting putative binding sites for Bicoid as indicated by the results of an in-vivo ChIP-chip experiment.

Identification of sequence–structure RNA binding motifs for SELEX-derived aptamers

Systematic Evolution of Ligands by EXponential Enrichment (SELEX) represents a state-of-the-art technology to isolate single-stranded (ribo)nucleic acid fragments, named aptamers, which bind to a molecule (or molecules) of interest via specific structural regions induced by their sequence-dependent fold. This powerful method has applications in designing protein inhibitors, molecular detection systems, therapeutic drugs and antibody replacement among others. However, full understanding and consequently optimal utilization of the process has lagged behind its wide application due to the lack of dedicated computational approaches. At the same time, the combination of SELEX with novel sequencing technologies is beginning to provide the data that will allow the examination of a variety of properties of the selection process. To close this gap we developed, Aptamotif, a computational method for the identification of sequence-structure motifs in SELEX-derived aptamers. To increase the chances of identifying functional motifs, Aptamotif uses an ensemble-based approach. We validated the method using two published aptamer datasets containing experimentally determined motifs of increasing complexity. We were able to recreate the author's findings to a high degree, thus proving the capability of our approach to identify binding motifs in SELEX data. Additionally, using our new experimental dataset, we illustrate the application of Aptamotif to elucidate several properties of the selection process.

Better estimation of protein-DNA interaction parameters improve prediction of functional sites

BackgroundCharacterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method designed to produce many such sequences.ResultsWe analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP), and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones) in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay) and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation) trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites.ConclusionWe think that training binding site detection algorithms on datasets from binding assays lead to better prediction. The improvements in accuracy came from the unbiased nature of the SELEX dataset rather than from the number of sites available. We believe that with progress in short-read sequencing technology, one could use SELEX methods to characterize binding affinities of many low specificity transcription factors.

Is [formula omitted] the first radial excitation of [formula omitted

We present a quantitative analysis of the DsJ+(2632) observed by SELEX mainly focusing on the assumption that DsJ+(2632) is the first radial excitation of the 1− ground state Ds∗(2112). By solving the instantaneous Bethe–Salpeter equation, we obtain the mass 2658±15 MeV for the first excited state, which is about 26 MeV heavier than the experimental value 2632±1.7 MeV. By means of PCAC and low-energy theorem we calculate the transition matrix elements and obtain the decay widths: Γ(DsJ+→Ds+η)=4.07±0.34 MeV, Γ(DsJ+→D0K+)≃Γ(Γ(DsJ+→D+K0)=8.9±1.2 MeV, and the ratio Γ(DsJ+→D0K+)/Γ(DsJ+→Ds+η)=2.2±0.2 as well. This ratio is quite different from the SELEX data 0.14±0.06. The summed decay width of those three channels is approximately 21.7 MeV, already larger than the observed bound for the full width (⩽17 MeV). Furthermore, assuming DsJ+(2632) is 1− state, we also explore the possibility of S–D wave mixing to explain the SELEX observation. Based on our analysis, we suspect that it is too early to conclude that DsJ+(2632) is the first radial excitation of the 1− ground state Ds∗(2112). More precise measurements of the relative ratios and the total decay width are urgently required, especially for S–D wave mixing.

Options for the SELEX state [formula omitted

We consider possible assignments for the D s J + ( 2632 ) , which was recently reported in D s + η and D 0 K + final states by the SELEX Collaboration at Fermilab. The most plausible quark model assignment for this state is the first radial excitation ( 2 3 S 1 ) of the c s ¯ D s * ( 2112 ) , although the predicted mass and strong decay branching fractions for this assignment are not in agreement with the SELEX data. The reported dominance of D s η over DK appears especially problematic. An intriguing similarity to the K * ( 1414 ) is noted. 2 3 S 1 – D 1 3 configuration mixing is also considered, and we find that this effect is unlikely to resolve the branching fraction discrepancy. Other interpretations as a c s ¯ -hybrid or a two-meson molecule are also considered, but appear unlikely. Thus, if this state is confirmed, it will require reconsideration of the systematics of charmed meson spectroscopy and strong decays.

Two RICH detectors as velocity spectrometers in the CKM experiment

We present the design of two velocity spectrometers, to be used in the recently approved CKM experiment. CKM's main goal is the measurement of the branching ratio of K+ -> pi+ nu nu with a precision of 10%, via decays in flight of the K+. The design of both RICH detectors is based on the SELEX Phototube RICH. We will discuss the design and the expected performance, based on studies with SELEX data and Monte Carlo Simulations.