Set cover-based methods for motif selection

Abstract 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.

Download Full-text

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

Bioinformatics ◽

10.1093/bioinformatics/btaa054 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2905-2906 ◽

Cited By ~ 1

Author(s):

Kevin R Shieh ◽

Christina Kratschmer ◽

Keith E Maier ◽

John M Greally ◽

Matthew Levy ◽

...

Keyword(s):

Motif Discovery ◽

High Throughput Sequencing ◽

De Novo ◽

Rna Aptamers ◽

Supplementary Information ◽

Good Correspondence ◽

Detection Algorithms ◽

De Novo Motif Discovery ◽

Exponential Enrichment ◽

Analytical Approaches

Abstract 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.

Download Full-text

Detection and Employment of Biological Sequence Motifs

Big Data Analytics in Bioinformatics and Healthcare - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-4666-6611-5.ch005 ◽

2015 ◽

pp. 86-116

Author(s):

Marjan Trutschl ◽

Phillip C. S. R. Kilgore ◽

Rona S. Scott ◽

Christine E. Birdwell ◽

Urška Cvek

Keyword(s):

Motif Discovery ◽

De Novo ◽

Amino Acid Sequences ◽

Discriminative Learning ◽

Large Set ◽

Sequence Motifs ◽

Biological Sequence ◽

Practical Applications ◽

De Novo Motif Discovery ◽

Covariance Models

Biological sequence motifs are short nucleotide or amino acid sequences that are biologically significant and are attractive to scientists because they are usually highly conserved and result in structural and regulatory implications. In this chapter, the authors show practical applications of these data, followed by a review of the algorithms, techniques, and tools. They address the nature of motifs and elucidate on several methods for de novo motif discovery, covering the algorithms based on Gibbs sampling, expectation maximization, Bayesian inference, covariance models, and discriminative learning. The authors present the tools and their requirements to weigh their individual benefits and challenges. Since interpretation of a large set of results can pose significant challenges, they discuss several methods for handling data that span from visualization to integration into pipelines and curated databases. Additionally, the authors show practical applications of these data with examples.

Download Full-text

DISPOM: A DISCRIMINATIVE DE-NOVO MOTIF DISCOVERY TOOL BASED ON THE JSTACS LIBRARY

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720013400064 ◽

2013 ◽

Vol 11 (01) ◽

pp. 1340006 ◽

Cited By ~ 1

Author(s):

JAN GRAU ◽

JENS KEILWAGEN ◽

ANDRÉ GOHR ◽

IVAN A. PAPONOV ◽

STEFAN POSCH ◽

...

Keyword(s):

Gene Expression ◽

Dna Binding ◽

Binding Sites ◽

Motif Discovery ◽

Binding Proteins ◽

Target Genes ◽

De Novo ◽

Specific Binding ◽

Dna Binding Proteins ◽

De Novo Motif Discovery

DNA-binding proteins are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in target regions of genomic DNA. However, de-novo discovery of these binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not yet been solved satisfactorily. Here, we present a detailed description and analysis of the de-novo motif discovery tool Dispom, which has been developed for finding binding sites of DNA-binding proteins that are differentially abundant in a set of target regions compared to a set of control regions. Two additional features of Dispom are its capability of modeling positional preferences of binding sites and adjusting the length of the motif in the learning process. Dispom yields an increased prediction accuracy compared to existing tools for de-novo motif discovery, suggesting that the combination of searching for differentially abundant motifs, inferring their positional distributions, and adjusting the motif lengths is beneficial for de-novo motif discovery. When applying Dispom to promoters of auxin-responsive genes and those of ABI3 target genes from Arabidopsis thaliana, we identify relevant binding motifs with pronounced positional distributions. These results suggest that learning motifs, their positional distributions, and their lengths by a discriminative learning principle may aid motif discovery from ChIP-chip and gene expression data. We make Dispom freely available as part of Jstacs, an open-source Java library that is tailored to statistical sequence analysis. To facilitate extensions of Dispom, we describe its implementation using Jstacs in this manuscript. In addition, we provide a stand-alone application of Dispom at http://www.jstacs.de/index.php/Dispom for instant use.

Download Full-text

Finding de novo methylated DNA motifs

Bioinformatics ◽

10.1093/bioinformatics/btz079 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3287-3293 ◽

Cited By ~ 4

Author(s):

Vu Ngo ◽

Mengchi Wang ◽

Wei Wang

Keyword(s):

Motif Discovery ◽

De Novo ◽

Affinity Purification ◽

Simulated Data ◽

Motif Finding ◽

Supplementary Information ◽

Dna Motifs ◽

Methylated Dna ◽

De Novo Motif Discovery ◽

And Performance

Abstract 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.

Download Full-text

Nucleosome Occupancy Information Improves de novo Motif Discovery

Lecture Notes in Computer Science - Research in Computational Molecular Biology ◽

10.1007/978-3-540-71681-5_8 ◽

2007 ◽

pp. 107-121 ◽

Cited By ~ 16

Author(s):

Leelavati Narlikar ◽

Raluca Gordân ◽

Alexander J. Hartemink

Keyword(s):

Motif Discovery ◽

De Novo ◽

Nucleosome Occupancy ◽

De Novo Motif Discovery

Download Full-text

TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets

BMC Genomics ◽

10.1186/s12864-018-4630-0 ◽

2018 ◽

Vol 19 (1) ◽

Cited By ~ 3

Author(s):

Louis T. Dang ◽

Markus Tondl ◽

Man Ho H. Chiu ◽

Jerico Revote ◽

Benedict Paten ◽

...

Keyword(s):

Next Generation Sequencing ◽

Motif Discovery ◽

De Novo ◽

Next Generation ◽

De Novo Motif Discovery ◽

Generation Sequencing

Download Full-text

De Novo Motif Prediction Using the Fireworks Algorithm

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch041 ◽

2019 ◽

pp. 1069-1085

Author(s):

Andrei Lihu ◽

Ștefan Holban

Keyword(s):

Gene Expression ◽

Motif Discovery ◽

De Novo ◽

Fireworks Algorithm ◽

Regulatory Processes ◽

Motif Prediction ◽

Leibler Divergence ◽

De Novo Motif Discovery ◽

Low Performance ◽

Motif Finding 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.

Download Full-text