Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

Levitsky, Victor G.; Ignatieva, E. V.; Ananko, E. A.; Turnaev, I. I.; Меркулова, Т. И.; Колчанов, Н. А.; Hodgman, Charlie

doi:10.1186/1471-2105-8-481

Cited by 38 publications

(42 citation statements)

References 70 publications

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“…Numerous techniques have been developed to predict TFBSs (Elnitski et al 2006;Levitsky et al 2007;von Rohr et al 2007) and TF binding motifs (TFBMs) (Mü ller-Molina et al 2012). However, few studies have attempted to predict DNA methylation patterns.…”

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confidence: 99%

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

2013

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Gene expression regulation is gated by promoter methylation states modulating transcription factor binding. The known DNA methylation/unmethylation mechanisms are sequence unspecific, but different cells with the same genome have different methylomes. Thus, additional processes bringing specificity to the methylation/unmethylation mechanisms are required. Searching for such processes, we demonstrated that CpG methylation states are influenced by the sequence context surrounding the CpGs. We used such a property to develop a CpG methylation motif discovery algorithm. The newly discovered motifs reveal ''methylation/unmethylation factors'' that could recruit the ''methylation/unmethylation machinery'' to the loci specified by the motifs. Our methylation motif discovery algorithm provides a synergistic approach to the differently methylated region algorithms. Since our algorithm searches for commonly methylated regions inside the same sample, it requires only a single sample to operate. The motifs that were found discriminate between hypomethylated and hypermethylated regions. The hypomethylation-associated motifs have a high CG content, their targets appear in conserved regions near transcription start sites, they tend to co-occur within transcription factor binding sites, they are involved in breaking the H3K4me3/H3K27me3 bivalent balance, and they transit the enhancers from repressive H3K27me3 to active H3K27ac during ES cell differentiation. The new methylation motifs characterize the pluripotent state shared between ES and iPS cells. Additionally, we found a collection of motifs associated with the somatic memory inherited by the iPS from the initial fibroblast cells, thus revealing the existence of epigenetic somatic memory on a fine methylation scale.

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confidence: 99%

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

2013

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“…Genetic Algorithms have been previously applied to the prediction of transcription factor binding sites [8], [9], [10]. The SiteGA web-based tool was used by Levitsky et al [8] to discover dinucleotide interactions that are most significant to distinguish actual sites from false positives.…”

Section: Introductionmentioning

confidence: 99%

Evolving Spiking Neural Networks for predicting transcription factor binding sites

Sichtig

Schaffer

Riva

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Interdisciplinary problem solving in computational biology requires a fundamental understanding of complex biological adaptive systems, from cellular to molecular level in order to tackle challenging problems such as neurodegenerative diseases. In this work we present a description and an initial evaluation of a Spiking Neural Network-Genetic Algorithm (SNN-GA) system we are developing for the computational prediction of transcription factor binding sites (TFBS). The SNN-GA approach is based on modeling information processing of biological neurons through evolutionary processes. The goal of our work is to reduce the number of false positives in the predicted TFBSs, through a more precise modeling of information contained in the alignments in the training data. We show an evaluation of four proposed network topologies that represent TFBS data. We use real TFBS data from the TRANSFAC R database and appropriately generated negative samples. We evaluated the network topologies one three wellknown models for transcription factors: RSRFC4, ZID and p53. Benchmark performances for these models are given using MAPPER and MATCH TM . The results show that our method has the potential to attain very high classi cation accuracy.

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“…The SiteGA web-based tool was used to discover dinucleotide interactions that are most significant to distinguish real sites from false positives [13]. According to its authors, SiteGA performs close to, and sometimes better than, probability weight matrices (PWMs); on the other hand, the web-based tool supports only a limited number of TFs.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic Algorithms have been previously applied to the prediction of transcription factor binding sites [13], [14], [15]. The SiteGA web-based tool was used to discover dinucleotide interactions that are most significant to distinguish real sites from false positives [13].…”

Section: Introductionmentioning

confidence: 99%

An innovative Positional Pattern Detection tool applied to GAL4 Binding Sites in yeast

Sichtig

Riva

2011

The 2011 International Joint Conference on Neural Networks

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The computational identification of regulatory elements in genomic DNA is key to understanding the regulatory infrastructure of a cell. We present an innovative tool to identify Transcription Factor Binding Sites (TFBSs) in genomic sequences. We show that our Positional Pattern Detection tool is able to attain high sensitivity and specificity of TFBS detection by capturing dependencies between nucleotide positions within the TFBS, thereby elucidating complex interactions that may be critical for the TFBS activity.Further, we unveil a combination of two biologically realistic information processing methods that underlie our tool: thirdgeneration neural networks (spiking neural networks) are used to represent the structure of TFBSs, and a genetic algorithm is used for optimization of network parameters. Initially, the networks are trained to distinguish known TFBS binding sites from negative examples in the learning phase. Then, the evolved network is used as a classifier to detect novel TFBSs in genomic sequences.Moreover, we show an application of our method to GAL4 binding sites in yeast. A two-neuron network topology is trained with real data from TRANSFAC and SCPD and evaluated through simulation. We show how neuron and synapse parameters can be evolved to improve classification results. Furthermore, the networks' predictions were compared against MAPPER, TFBIND and TFSEARCH. Our results reveal that our innovative tool has the potential to attain very high classification accuracy, with a very small number of false positives. These results show that information processing methods are able to capture important positional information in TFBSs and should be explored further to look at complex relationships underlying transcriptional and epigenetic regulation.

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Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions

Cited by 38 publications

References 70 publications

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

Evolving Spiking Neural Networks for predicting transcription factor binding sites

An innovative Positional Pattern Detection tool applied to GAL4 Binding Sites in yeast

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