De Novo Motif Prediction Using the Fireworks Algorithm

Biotechnology ◽  
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.

De novo Motif Prediction using the Fireworks Algorithm

2015 ◽  
Vol 6 (3) ◽  
pp. 24-40 ◽  
Author(s):  
Andrei Lihu ◽  
Ștefan Holban
Keyword(s):  
Motif Discovery ◽  
De Novo ◽  
Proof Of Concept ◽  
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.

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

2013 ◽  
Vol 11 (01) ◽  
pp. 1340006 ◽  
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.

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

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
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

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

2020 ◽  
Vol 36 (9) ◽  
pp. 2905-2906 ◽  
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.

scholarly journals Factorbook Motif Pipeline: A de novo motif discovery and filtering web server for ChIP-seq peaks

2015 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Jiali Zhuang ◽  
Jie Wang ◽  
Zhiping Weng
Keyword(s):  
Motif Discovery ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Statistical Tests ◽  
Web Server ◽  
Biological Processes ◽  
Web Based ◽  
Sequencing Technologies ◽  
De Novo Motif Discovery

Summary: High-throughput sequencing technologies such as ChIP-seq have deepened our understanding in many biological processes. De novo motif search is one of the key downstream computational analysis following the ChIP-seq experiments and several algorithms have been proposed for this purpose. However, most web-based systems do not perform independent filtering or enrichment analyses to ensure the quality of the discovered motifs. Here, we developed a web server Factorbook Motif Pipeline based on an algorithm used in analyzing ENCODE consortium ChIP-seq datasets. It performs comprehensive analysis on the set of peaks detected from a ChIP-seq experiments: (i) de novo motif discovery; (ii) independent composition and bias analyses and (iii) matching to the annotated motifs. The statistical tests employed in our pipeline provide a reliable measure of confidence as to how significant are the motifs reported in the discovery step. Availability: Factorbook Motif Pipeline source code is accessible through the following URL. https://github.com/joshuabhk/factorbook-motif-pipeline

Detection and Employment of Biological Sequence Motifs

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.

scholarly journals PairMotif+: A Fast and Effective Algorithm for De Novo Motif Discovery in DNA sequences

2013 ◽  
Vol 9 (4) ◽  
pp. 412-424 ◽  
Author(s):  
Qiang Yu ◽  
Hongwei Huo ◽  
Yipu Zhang ◽  
Hongzhi Guo ◽  
Haitao Guo
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
De Novo ◽  
Effective Algorithm ◽  
De Novo Motif Discovery
Sign in / Sign up

Export Citation Format

Share Document