AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX

Summary High-throughput sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment. Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. While several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI-enabled environment. We demonstrate AptCompare’s performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences’ rankings and their binding affinities. Availability and implementation The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare. Supplementary information Supplementary data are available at Bioinformatics online.

Set cover-based methods for motif selection

Motivation De novo motif discovery algorithms find statistically over-represented sequence motifs that may function as transcription factor binding sites. Current methods often report large numbers of motifs, making it difficult to perform further analyses and experimental validation. The motif selection problem seeks to identify a minimal set of putative regulatory motifs that characterize sequences of interest (e.g. ChIP-Seq binding regions). Results In this study, the motif selection problem is mapped to variants of the set cover problem that are solved via tabu search and by relaxed integer linear programing (RILP). The algorithms are employed to analyze 349 ChIP-Seq experiments from the ENCODE project, yielding a small number of high-quality motifs that represent putative binding sites of primary factors and cofactors. Specifically, when compared with the motifs reported by Kheradpour and Kellis, the set cover-based algorithms produced motif sets covering 35% more peaks for 11 TFs and identified 4 more putative cofactors for 6 TFs. Moreover, a systematic evaluation using nested cross-validation revealed that the RILP algorithm selected fewer motifs and was able to cover 6% more peaks and 3% fewer background regions, which reduced the error rate by 7%. Availability and implementation The source code of the algorithms and all the datasets are available at https://github.com/YichaoOU/Set_cover_tools. Supplementary information Supplementary data are available at Bioinformatics online.

Finding de novo methylated DNA motifs

Motivation Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. Results mEpigram outperforms both MEME and DREME on finding modified motifs in simulated data that mimics various motif enrichment scenarios. Furthermore we were able to identify methylated motifs in Arabidopsis DNA affinity purification sequencing (DAP-seq) data that were previously demonstrated to contain such motifs. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis-elements by collaborative proteins. Availability and implementation The mEpigram program is available at http://wanglab.ucsd.edu/star/mEpigram. Supplementary information Supplementary data are available at Bioinformatics online.

De Novo Motif Prediction Using the Fireworks Algorithm

De novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Evolutionary methods, including swarm intelligence algorithms, have been applied with limited success for motif prediction. However, recently developed methods, such as the Fireworks Algorithm (FWA) which simulates the explosion process of fireworks, may show better prospects. This paper describes a motif finding algorithm based on FWA that maximizes the Kullback-Leibler divergence between candidate solutions and the background noise. Following the terminology of FWA's framework, the candidate motifs are fireworks that generate additional sparks (i.e. derived motifs) in their neighborhood. During the iterations, better sparks can replace the fireworks, as the Fireworks Motif Finder (FW-MF) assumes a one occurrence per sequence mode. The results obtained on a standard benchmark for promoter analysis show that our proof of concept is promising.

AptCompare: optimized de novo motif discovery of RNA aptamers via HTS-SELEX

ABSTRACTSummary:High-Throughput Sequencing can enhance the analysis of aptamer libraries generated by the Systematic Evolution of Ligands by EXponential enrichment (HTS-SELEX). Robust analysis of the resulting sequenced rounds is best implemented by determining a ranked consensus of reads following the processing by multiple aptamer detection algorithms. Whilst several such approaches have been developed to this end, their installation and implementation is problematic. We developed AptCompare, a cross-platform program that combines six of the most widely used analytical approaches for the identification of RNA aptamer motifs and uses a simple weighted ranking to order the candidate aptamers, all driven within the same GUI- enabled environment. We demonstrate AptCompare’s performance by identifying the top-ranked candidate aptamers from a previously published selection experiment in our laboratory, with follow-up bench assays demonstrating good correspondence between the sequences’ rankings and their binding affinities.Availability and Implementation:The source code and pre-built virtual machine images are freely available at https://bitbucket.org/shiehk/aptcompare.

Argo_CUDA: Exhaustive GPU based approach for motif discovery in large DNA datasets

The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top “peak” ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.