CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments

Kuttippurathu, Lakshmi; Hsing, Michael; Liu, Yongchao; Schmidt, Bertil; Maskell, Douglas L.; Lee, Kyungjoon; He, Aibin; Pu, William T.; Kong, Sek Won

doi:10.1093/bioinformatics/btq707

Cited by 44 publications

(40 citation statements)

References 17 publications

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“…31 A phastcons score of ≥0.57 corresponding to conservation across mammals was used as a conservation threshold. 32 To generate the background conservation estimate, we randomly generated 100 sets of 8637 15 nt sequences.…”

Section: Sequence Conservation and Selection Analysismentioning

confidence: 99%

Functional Genomics Analysis of Big Data Identifies Novel Peroxisome Proliferator–Activated Receptor γ Target Single Nucleotide Polymorphisms Showing Association With Cardiometabolic Outcomes

Richardson

Schnitzler

Lai

et al. 2015

Circ Cardiovasc Genet

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Background-Cardiovascular disease and type 2 diabetes mellitus represent overlapping diseases where a large portion of the variation attributable to genetics remains unexplained. An important player in their pathogenesis is peroxisome proliferator-activated receptor γ (PPARγ) that is involved in lipid and glucose metabolism and maintenance of metabolic homeostasis. We used a functional genomics methodology to interrogate human chromatin immunoprecipitationsequencing, genome-wide association studies, and expression quantitative trait locus data to inform selection of candidate functional single nucleotide polymorphisms (SNPs) falling in PPARγ motifs. Methods and Results-We derived 27 328 chromatin immunoprecipitation-sequencing peaks for PPARγ in human adipocytes through meta-analysis of 3 data sets. The PPARγ consensus motif showed greatest enrichment and mapped to 8637 peaks. We identified 146 SNPs in these motifs. This number was significantly less than would be expected by chance, and Inference of Natural Selection from Interspersed Genomically coHerent elemenTs analysis indicated that these motifs are under weak negative selection. A screen of these SNPs against genome-wide association studies for cardiometabolic traits revealed significant enrichment with 16 SNPs. A screen against the MuTHER expression quantitative trait locus data revealed 8 of these were significantly associated with altered gene expression in human adipose, more than would be expected by chance.

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Section: Sequence Conservation and Selection Analysismentioning

confidence: 99%

Functional Genomics Analysis of Big Data Identifies Novel Peroxisome Proliferator–Activated Receptor γ Target Single Nucleotide Polymorphisms Showing Association With Cardiometabolic Outcomes

Richardson

Schnitzler

Lai

et al. 2015

Circ Cardiovasc Genet

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show abstract

“…Tools for de novo motif discovery, that is from the second class of algorithms mentioned earlier, are often used in analysis of ChIP-seq experiments: WEEDER (Pavesi et al 2004, Tompa et al 2005, MEME (Bailey & Elkan 1994), MDScan (Liu et al 2002), and W-ChIPMotifs ( Jin et al 2009) are equipped with web interfaces. Convenient integrative frames for launching multiple DNA discovery algorithms (two or more of the above-mentioned) are TAMO (Gordon et al 2005), by shell scripting, and the web-based CompleteMOTIFs (Kuttippurathu et al 2011). Motif discovery algorithms eventually need to be coupled to other tools, either to link the discovered motifs to known TFBSs or to classify them using, for example, clustering algorithms (Bickel et al 2009, Hackenberg et al 2011.…”

Section: Bioinformatics Toolkitsmentioning

confidence: 99%

“…1. Enhancers often lie several kilo bases from the core, as observed in regulation by nuclear receptors (Lin et al 2007, Kuttippurathu et al 2011, Navlakha & Bar-Joseph 2011, and may lie in intragenic regions (mainly introns) as well as in the upstream sequence of a gene. The 'linear' architecture of the promoter (Fig.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics applied to gene transcription regulation

Altobelli

2012

Journal of Molecular Endocrinology

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Understanding regulation of gene transcription is central to molecular biology as well as being of great interest in medicine. The molecular syntax of the concerted transcriptional activation/repression of gene networks in mammal cells, which shape the physiological response to the molecular signals, is often unknown or not completely understood. Combining genome-wide experiments within silicoapproaches opens the way to a more systematic comprehension of the molecular mechanisms of transcription regulation. Diverse bioinformatics tools have been developed to help unravel these mechanisms, by handling and processing data at different stages: from data collection and storage to the identification of molecular targets and from the detection of DNA motif signatures in the regulatory sequences of functionally related genes to the identification of relevant regulatory networks. Moreover, the large amount of genome-wide scale data recently produced has attracted professionals from diverse backgrounds to this cutting-edge realm of molecular biology. This mini-review is intended as an orientation for multidisciplinary professionals, introducing a streamlined workflow in gene transcription regulation with emphasis on sequence analysis. It provides an outlook on tools and methods, selected from a host of bioinformatics resources available today. It has been designed for the benefit of students, investigators, and professionals who seek a coherent yet quick introduction toin silicoapproaches to analyzing regulation of gene transcription in the post-genomic era.

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“…In fact, many newly proposed computational motif prediction tools [34] are designed to tackle large numbers of DNA sequences. Examples of such tools are AMD [31], RSAT peakmotifs [34] and DREME [30], which are based on consensus pattern enumeration; genetic algorithm based GADEM [35], and CompleteMotifs [36], which employs multiple motif discovery tools. Some authors have employed an ad-hoc motif discovery pipeline that works per the recommendation by Hu et al [21].…”

Section: Introductionmentioning

confidence: 99%

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

Lee

Azizan

Wong

et al. 2018

Biotechnology & Biotechnological Equipment

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We propose an improved solution to the three-stage DNA motif prediction approach. The threestage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis.

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CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments

Abstract: http://cmotifs.tchlab.org.

Cited by 44 publications

References 17 publications

Functional Genomics Analysis of Big Data Identifies Novel Peroxisome Proliferator–Activated Receptor γ Target Single Nucleotide Polymorphisms Showing Association With Cardiometabolic Outcomes

Functional Genomics Analysis of Big Data Identifies Novel Peroxisome Proliferator–Activated Receptor γ Target Single Nucleotide Polymorphisms Showing Association With Cardiometabolic Outcomes

Bioinformatics applied to gene transcription regulation

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

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