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A Bayesian network approach to operon prediction

J. Bockhorst, M. Craven, D. Page, J. Shavlik, J. Glasner
2003 Bioinformatics  
Results: We present a probabilistic approach to predicting operons using Bayesian networks. Our approach exploits diverse evidence sources such as sequence and expression data.  ...  We evaluate our approach on the Escherichia coli K-12 genome where our results indicate we are able to identify over 78% of its operons at a 10% false positive rate.  ...  We present a Bayesian network approach to predicting operons in prokaryotic genomes.  ... 
doi:10.1093/bioinformatics/btg147 pmid:12835266 fatcat:i4waqd2hgrhqnjgrufl24pkuou

Probabilistic functional gene societies

Insuk Lee
2011 Progress in Biophysics and Molecular Biology  
The initial goal of systems biology was to learn more about the whole organismal system.  ...  The next genome-context approach, the gene neighboring method, uses bacterial operon structures.  ... 
doi:10.1016/j.pbiomolbio.2011.01.003 pmid:21281658 fatcat:5p7pg6fzdrfmhhzmcnkg4msjzu

Reverse-Engineering Transcriptional Modules from Gene Expression Data

Tom Michoel, Riet De Smet, Anagha Joshi, Kathleen Marchal, Yves Van de Peer
2009 Annals of the New York Academy of Sciences  
"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions.  ...  We show that the inferred probabilistic models extend beyond the data set used to learn the models.  ...  Therefore, in our approach we exploit the "fuzzy" property of a module to increase the reliability of the predicted interactions.  ... 
doi:10.1111/j.1749-6632.2008.03943.x pmid:19348630 fatcat:5aruwpt7tfbnxkkq2334bnmc44

Genome-wide operon prediction in Staphylococcus aureus

L. Wang
2004 Nucleic Acids Research  
This consensus approach has enabled us to predict operons with high accuracy from a genome where limited experimental evidence for operon structure is available.  ...  We have integrated several operon prediction methods and developed a consensus approach to score the likelihood of each adjacent gene pair to be co-transcribed.  ...  The second method is to use a probabilistic machine-learning approach to induce operon prediction models using a variety of data types including sequence data, gene expression data and functional annotation  ... 
doi:10.1093/nar/gkh694 pmid:15252153 pmcid:PMC484181 fatcat:kacut3wzsbhrddipp53y6tqcgu

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

X. Chen
2004 Nucleic Acids Research  
We present a computational method for operon prediction based on a comparative genomics approach.  ...  Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp.  ...  ACKNOWLEDGEMENTS The authors would like to thank the anonymous referees for many valuable suggestions in the revision. This work was  ... 
doi:10.1093/nar/gkh510 pmid:15096577 pmcid:PMC407844 fatcat:n2zxubhbxva7zn3lhisijcxb64

Predicting bacterial transcription units using sequence and expression data

J. Bockhorst, Y. Qiu, J. Glasner, M. Liu, F. Blattner, M. Craven
2003 Bioinformatics  
We present a method, based on probabilistic language models, that we apply to predict operons, promoters and terminators in the genome of Escherichia coli K-12.  ...  Results: Our experimental results show that we are able to predict operons and localize promoters and terminators with high accuracy.  ...  We present an approach, based on probabilistic language models, that uses sequence and expression data to predict a variety of regulatory elements in prokaryotic genomes.  ... 
doi:10.1093/bioinformatics/btg1003 pmid:12855435 fatcat:7nhdadxl6zezjjqqe6k2zd7pcy

Biological applications of multi-relational data mining

David Page, Mark Craven
2003 SIGKDD Explorations  
This paper presents several applications of multi-relational data mining to biological data, taking care to cover a broad range of multi-relational data mining techniques.  ...  Biological databases contain a wide variety of data types, often with rich relational structure. Consequently multirelational data mining techniques frequently are applied to biological data.  ...  In our most recent work [4] , we have developed a probabilistic language model to simultaneously predict promoters, terminators and operons.  ... 
doi:10.1145/959242.959250 fatcat:kqnuj6o55zhq3fr2jfyyy6u6tm

Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum

Wen-Chi Chou, Qin Ma, Shihui Yang, Sha Cao, Dawn M. Klingeman, Steven D. Brown, Ying Xu
2015 Nucleic Acids Research  
TU organization of Clostridium thermocellum using a machine-learning approach.  ...  Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism.  ...  -C.C. and Q.M. contributed equally to this paper.  ... 
doi:10.1093/nar/gkv177 pmid:25765651 pmcid:PMC4446414 fatcat:brqjarbcwfezrmgrnvuswogphy

Connecting quantitative regulatory-network models to the genome

Yue Pan, Tim Durfee, Joseph Bockhorst, Mark Craven
2007 Computer applications in the biosciences : CABIOS  
We have developed an extension to this approach that involves representing and learning the key kinetic parameters as functions of features in the genomic sequence.  ...  Results: We evaluate our approach using two E. coli geneexpression data sets, with a particular focus on modeling the networks that are involved in controlling how E. coli regulates its response to the  ...  ACKNOWLEDGEMENTS The authors thank Keith Noto for helpful comments on a draft of this article.  ... 
doi:10.1093/bioinformatics/btm228 pmid:17646319 fatcat:ffjm6dwizrevze5qdtcvws6wte

Towards the automated engineering of a synthetic genome

Javier Carrera, Guillermo Rodrigo, Alfonso Jaramillo
2009 Molecular Biosystems  
Here it is of outmost importance to harness the ability of using computational design to predict and optimize a synthetic genome before attempting its synthesis.  ...  The methodology to computationally design a genome is based on an optimization that computationally mimics genome evolution. The biggest bottleneck lies on the use of an appropriate fitness function.  ...  The step (iii) is the most complicated of all, as it requires a quantitative model of the whole cell able to predict cell growth for a given genome.  ... 
doi:10.1039/b904400k pmid:19562112 fatcat:2us2zczupjcy3g6niyb4pweeje

A powerful non-homology method for the prediction of operons in prokaryotes

G. Moreno-Hagelsieb, J. Collado-Vides
2002 Bioinformatics  
Though several methods have been devised to predict operons, most need a high characterization of the genome analysed.  ...  Loglikelihoods derived from inter-genic distance distributions work surprisingly well to predict operons in Escherichia coli and are available for any genome as soon as the gene sets are predicted.  ...  ACKNOWLEDGEMENTS This work was supported by grant number NC028 from: Consejo Nacional de Ciencia y Tecnología to J.C.-V. We appreciate fruitful discussions with Temple F.  ... 
doi:10.1093/bioinformatics/18.suppl_1.s329 pmid:12169563 fatcat:vlzeot5vtrhzraljfkfmy7tsiu

Genome sequencing of bacteria: sequencing, de novo assembly and rapid analysis using open source tools

Veljo Kisand, Teresa Lettieri
2013 BMC Genomics  
Regardless, using draft whole genomes that are not finished and remain fragmented into tens of contigs allows one to characterize unknown bacteria with modest effort.  ...  Despite a high coverage (~30 fold), it did not allow the reference genome to be fully mapped. Reads from regions with errors had low quality, low coverage, or were missing.  ...  RAST failed to annotate any rRNA operons and predicted one fewer tRNA.  ... 
doi:10.1186/1471-2164-14-211 pmid:23547799 pmcid:PMC3618134 fatcat:y4ll6qcskfcjjcz4izbunzgfnm

A Comparative Genomics Approach to Prediction of New Members of Regulons

K. Tan
2001 Genome Research  
a comparison with the Haemophilus influenzae genome.  ...  We combine the prediction of transcription units having orthologous genes with the prediction of transcription factor binding sites based on probabilistic models.  ...  This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.  ... 
doi:10.1101/gr.149301 pmid:11282972 pmcid:PMC311042 fatcat:mqicqtvtiraexj47y4kkrjz72y

Towards a semi-automatic functional annotation tool based on decision-tree techniques

Jérôme Azé, Lucie Gentils, Claire Toffano-Nioche, Valentin Loux, Jean-François Gibrat, Philippe Bessières, Céline Rouveirol, Anne Poupon, Christine Froidevaux
2008 BMC Proceedings  
Results obtained for the two approaches on both genomes are comparable and show a good precision together with a high prediction rate.  ...  Using combined approaches increases the recall and the prediction rate.  ...  Two approaches In this section, we present the two machine learning techniques we used to learn decision-trees: ILP framework and Multilabel probabilistic decision-tree.  ... 
doi:10.1186/1753-6561-2-s4-s3 pmid:19091050 pmcid:PMC2654970 fatcat:kvy6yuxiczcedcn3muiefge34y

Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

David B Bernstein, Snorre Sulheim, Eivind Almaas, Daniel Segrè
2021 Genome Biology  
The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving  ...  A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration  ...  Also in this case, machine learning approaches can be used to predict the specific subcellular localization of proteins [59, 60] .  ... 
doi:10.1186/s13059-021-02289-z pmid:33602294 pmcid:PMC7890832 fatcat:de5czxlhpzdtfepx7sjgoblypi
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