High-Throughput SELEX data analysis software tools
High-Throughput (HT) SELEX combines SELEX (Systematic Evolution of Ligands by EXponential Enrichment), a method for aptamer discovery, with massively parallel sequencing technologies. This emerging technology provides data for a global analysis of the selection process and for simultaneous discovery of a large number of candidates but currently lacks dedicated computational approaches for their analysis.
Provides a complete and comprehensive analysis pipeline for HT-SELEX data and comprises four fully automated core steps: data preprocessing, sequence analysis, cluster extraction, and data visualization. At the same time, AptaTools is modular enough to be extended with additional features. This pipeline is capable of handling most of the file formats generated by modern high throughput sequencing devices including paired-end data but also supports already pre-partitioned pools as input.
Determines the binding free energy coefficients. SelexGLM employs a generalized model based on the Poisson distribution. It allows to uncover and characterize intrinsic differences in DNA binding specificity between androgen receptor (AR) and glucocorticoid receptor (GR). This tool is able to take into account offsets within the probe that partially cover the fixed sequences upstream and downstream of the variable region.
An approach for the identification of sequence-structure binding motifs in HT-SELEX derived aptamers. AptaTRACE leverages the experimental design of the SELEX protocol and identifies sequence-structure motifs that show a signature of selection. Because of its unique approach, AptaTRACE can uncover motifs even when these are present in only a minuscule fraction of the pool. Due to these features, our method can help to reduce the number of selection cycles required to produce aptamers with the desired properties, thus reducing cost and time of this rather expensive procedure. The performance of the method on simulated and real data indicates that AptaTRACE can detect sequence-structure motifs even in highly challenging data.
A meta-motif based statistical framework and pipeline to predict SELEX derived binding aptamers. Briefly, MPBind calculates four kinds of p-values (1-sided) for each motif, representing different features. Using human embryonic stem cell SELEX-Seq data, MPBind achieved high prediction accuracy for binding potential. Further analysis showed that MPBind is robust to both polymerase chain reaction amplification bias and incomplete sequencing of aptamer pools. These two biases usually confound aptamer analysis.
A computational tool to identify target-specific aptamers from HT-SELEX data and secondary structure information. APTANI builds on AptaMotif algorithm (Hoinka et al., 2012), 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.
Selects the optimal motif length and calculates the confidence intervals of estimated parameters. BEESEL uses the expectation maximization (EM) algorithm to iteratively find both the optimal position weight matrice (PWM) and the most likely binding position on each sequence read. The tool allows the sequences to be much longer than the binding sites, which requires the simultaneous estimation of the binding site locations and the specificity model.
Infers the specificity of transcription factors (TFs) based on HT-SELEX data. BEESEM allows the sequences to be much longer than the binding sites, which requires simultaneous estimation of the binding site locations and specificity model. This model achieves significantly better fits to the quantitative HT-SELEX data. It also allows to calculate confidence intervals on the estimated parameters.
A motif discovery algorithm that is built on a recent machine learning technique referred to as Method of Moments. Based on spectral decompositions, this method is robust to model misspecifications and is not prone to locally optimal solutions.
Analyzes in vitro selection data for RNA-protein interactions that may primarily rely on specific local features in the context of a larger secondary structure. hts-exploration is a Clojure library containing methods for analyzing high-throughput sequencing (HTS)-SELEX data. This analysis approach simultaneously considers the sequence and structure to identify a discontinuous double-stranded binding motif.
An easy-to-use and universally compatible toolkit designed for bench scientists to address the primary sequence analysis needs from high-throughput sequencing of combinatorial selection populations. FASTAptamer performs the simple tasks of counting, normalizing, ranking and sorting the abundance of each unique sequence in a population, comparing sequence distributions for two populations, clustering sequences into sequence families based on Levenshtein edit distance, calculating fold-enrichment for all of the sequences present across populations, and searching degenerately for nucleotide sequence motifs. While FASTAptamer was originally developed for analysis of high-throughput sequencing data from aptamer selections, it offers broad utility for those working on ribozyme or DNAzyme selections, surface display (phage display, mRNA display, etc.) selections, in vivo SELEX, protein mutagenesis selection, or any biocombinatorial selection that results in a DNA-encoded library for sequencing.
A program for the aptamer truncation process. ValFold can predict the secondary structures with improved accuracy based on unique aptamer characteristics. ValFold predicts not only the canonical Watson-Crick pairs but also G-G pairs derived from G-quadruplex (known structure for many aptamers) using the stem candidate selection algorithm.
Corrects for technological biases, selects the cycle and k, and builds a motif starting from a consensus k-mer in that cycle. In large scale tests, HTS-IBIS outperformed the extant automatic algorithm for the motif finding task on both in vitro and in vivo binding prediction.
Detects shapemers enriched in both oligos that contain binding motifs and the background oligos that survived until the final round. Co-SELECT is a computational approach to analyze the results of in vitro HT-SELEX experiments for transcription factors (TFs)-DNA binding. It was developed with a method of deconvoluting the contributions of DNA sequence and DNA shape on the binding.
Assists users for aptamer selection analysis. AptCompare combines different approaches to perform de novo motif discovery in aptamer selections. This software automates the preprocessing and analysis of high-throughput sequencing (HTS)-SELEX sequencing data and utilizes a simple scheme to rank the weighted scores of the combined approaches. It uses an initial hierarchical clustering step to detect cluster seeds that corresponds to representative sequences of each cluster.
Functions to assist in discovering transcription factor DNA binding specificities from SELEX-seq experimental data. SELEX is an R package that offers functions used to calculate and return the affinities and affinity standard errors of K-mers of length k, to count and return the number of instances K-mers of length k appear within the sample’s variable regions.
Aligns pseudorandom DNA X-aptamers from next-generation sequencing data. Aptaligner uses the inherent design scheme of bead-based X-aptamers to create a hypothetical reference library and Markov modeling techniques to provide improved alignments. Aptaligner also provides length error and noise level cutoff features, is parallelized to run on multiple central processing units (cores), and sorts sequences from a single chip into projects and subprojects.
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