AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX.
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ABSTRACT:
SUBMITTER: Shieh KR
PROVIDER: S-EPMC8524629 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Aptamers are high-affinity ligands selected from DNA or RNA libraries via SELEX, a repetitive in vitro process of sequential selection and amplification steps. RNA SELEX is more complicated than DNA SELEX because of the additional transcription and reverse transcription steps. Here, we report a new selection scheme, RAPID-SELEX (RNA Aptamer Isolation via Dual-cycles SELEX), that simplifies this process by systematically skipping unnecessary amplification steps. Using affinity microcolumns, we were able to complete a multiplex selection for protein targets, CHK2 and UBLCP1, in a third of the time required for analogous selections using a conventional SELEX approach. High-throughput sequencing of the enriched pools from both RAPID and SELEX revealed many identical candidate aptamers from the starting pool of 5 × 10(15) sequences. For CHK2, the same sequence was preferentially enriched in both selections as the top candidate and was found to bind to its respective target. These results demonstrate the efficiency and, most importantly, the robustness of our selection scheme. RAPID provides a generalized approach that can be used with any selection technology to accelerate the rate of aptamer discovery, without compromising selection performance.
| S-EPMC3869713 | biostudies-literature
Project description:BackgroundBacterial surfaces are complex systems, constructed from membranes, peptidoglycan and, importantly, proteins. The proteins play crucial roles as critical regulators of how the bacterium interacts with and survive in its environment. A full catalog of the motifs in protein families and their relative conservation grade is a prerequisite to target the protein-protein interaction that bacterial surface protein makes to host proteins.ResultsIn this paper, we propose a greedy approach to identify conserved motifs in large sequence families iteratively. Each iteration discovers a motif de novo and masks all occurrences of that motif. Remaining unmasked sequences are subjected to the next round of motif detection until no more significant motifs can be found. We demonstrate the utility of the method through the construction of a proteome-wide motif repository for Group A Streptococcus (GAS), a significant human pathogen. GAS produce numerous surface proteins that interact with over 100 human plasma proteins, helping the bacteria to evade the host immune response. We used the repository to find that proteins part of the bacterial surface has motif architectures that differ from intracellular proteins.ConclusionsWe elucidate that the M protein, a coiled-coil homodimer that extends over 500 A from the cell wall, has a motif architecture that differs between various GAS strains. As the M protein is known to bind a variety of different plasma proteins, the results indicate that the different motif architectures are responsible for the quantitative differences of plasma proteins that various strains bind. The speed and applicability of the method enable its application to all major human pathogens.
| S-EPMC6471678 | biostudies-literature
Project description:Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.
| S-EPMC6030986 | biostudies-literature
Project description:De novo motif discovery has been an important challenge of bioinformatics for the past two decades. Since the emergence of high-throughput techniques like ChIP-seq, ChIP-exo and protein-binding microarrays (PBMs), the focus of de novo motif discovery has shifted to runtime and accuracy on large data sets. For this purpose, specialized algorithms have been designed for discovering motifs in ChIP-seq or PBM data. However, none of the existing approaches work perfectly for all three high-throughput techniques. In this article, we propose Dimont, a general approach for fast and accurate de novo motif discovery from high-throughput data. We demonstrate that Dimont yields a higher number of correct motifs from ChIP-seq data than any of the specialized approaches and achieves a higher accuracy for predicting PBM intensities from probe sequence than any of the approaches specifically designed for that purpose. Dimont also reports the expected motifs for several ChIP-exo data sets. Investigating differences between in vitro and in vivo binding, we find that for most transcription factors, the motifs discovered by Dimont are in good accordance between techniques, but we also find notable exceptions. We also observe that modeling intra-motif dependencies may increase accuracy, which indicates that more complex motif models are a worthwhile field of research.
| S-EPMC3834837 | biostudies-other
Project description:We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. On the task of retrieving the motifs of expressed TFs in a given cell type, BindVAE is competitive with existing motif discovery approaches.
| S-EPMC9380350 | biostudies-literature
Project description:LAG3 receptor belongs to a family of immune-checkpoints expressed in T lymphocytes and other cells of the immune system. It plays an important role as a rheostat of the immune response. Focus on this receptor as a potential therapeutic target in cancer immunotherapy has been underscored after the success of other immune-checkpoint blockade strategies in clinical trials. LAG3 showcases the interest in the field of autoimmunity as several studies show that LAG3-targeting antibodies can also be used for the treatment of autoimmune diseases. In this work we describe the identification of a high-affinity LAG3 aptamer by High Throughput Sequencing SELEX in combination with a study of potential conserved binding modes according to sequence conservation by using 2D-structure prediction and 3D-RNA modeling using Rosetta. The aptamer with the highest accumulation of these conserved sequence motifs displays the highest affinity to LAG3 recombinant soluble proteins and binds to LAG3-expressing lymphocytes. The aptamer described herein has the potential to be used as a therapeutic agent, as it enhances the threshold of T-cell activation. Nonetheless, in future applications, it could also be engineered for treatment of autoimmune diseases by target depletion of LAG3-effector T lymphocytes.
| S-EPMC5608357 | biostudies-literature
Project description:BACKGROUND:A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57-74, 2012; Nat 507:462-70, 2014; Nat 507:455-61, 2014; Nat 518:317-30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users. RESULTS:We present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563-5, 2007; Nat Protoc 5:323-34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy. CONCLUSIONS:TrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au .
| S-EPMC5887194 | biostudies-literature
Project description:The BaMM web server offers four tools: (i) de-novo discovery of enriched motifs in a set of nucleotide sequences, (ii) scanning a set of nucleotide sequences with motifs to find motif occurrences, (iii) searching with an input motif for similar motifs in our BaMM database with motifs for >1000 transcription factors, trained from the GTRD ChIP-seq database and (iv) browsing and keyword searching the motif database. In contrast to most other servers, we represent sequence motifs not by position weight matrices (PWMs) but by Bayesian Markov Models (BaMMs) of order 4, which we showed previously to perform substantially better in ROC analyses than PWMs or first order models. To address the inadequacy of P- and E-values as measures of motif quality, we introduce the AvRec score, the average recall over the TP-to-FP ratio between 1 and 100. The BaMM server is freely accessible without registration at https://bammmotif.mpibpc.mpg.de.
| S-EPMC6030882 | biostudies-literature
Project description:MotivationSystematic 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.ResultsTo 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.
| S-EPMC3371842 | biostudies-literature
Project description:Type 1 Diabetes is still an incurable disease characterized by autoimmune destruction of insulin-producing beta cells within the islet of Langerhans in the pancreas. Currently, there are no methods to monitor beta-cell mass in humans or deliver therapeutics specifically to beta cells. Here we performed Cluster Systematic Evolution of Ligands by Exponential Enrichment (SELEX) experiments and toggle SELEX experiments to identify RNA aptamers specific for human islets. In the cluster SELEX, we started from a random library of RNA nucleotides composed of a 40 nucleotide long variable region flanked by two constant regions. We performed eight selection cycles using hand-picked islets and islet-depleted acinar tissue from 4 cadaveric human donors as positive and negative selectors. In the toggle SELEX, we conducted eight cycles of selection using islets and acinar tissue from mice, followed by two cycles of selection using human tissues. The polyclonal libraries from the two selection strategies showed a convergent evolution of ligands and increased specificity for human islets.
2022-02-23 | GSE197262 | GEO