Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements

Creanza, Teresa Maria; Horner, David S.; D’Addabbo, A.; Maglietta, Rosalia; Mignone, Flavio; Ancona, Nicola; Pesole, Graziano

doi:10.1186/1471-2105-10-s6-s2

Cited by 4 publications

(7 citation statements)

References 38 publications

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“…We give an example. Many single sequence techniques for measuring the coding potential of DNA sequences are based upon frequencies of n-mers in known intronic and exonic regions [2,10,11,23]. We can use this principle to write down parameters v exon which are based simply on the frequency of codons in the exon sequences.…”

Section: Cds Density Estimation On Musmentioning

confidence: 99%

“…The development of robust techniques that detect the coding potential of short sequences is an important area of research [11,14,16,21,30,31,40,46] with applications to sequence annotation as well as gene prediction. We show that our equilibrium measure is reasonably effective in distinguishing between randomly selected introns and exons of length 750bp in the human genome.…”

Section: Introductionmentioning

confidence: 99%

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Coding sequence density estimation via topological pressure

Koslicki

Thompson

2014

J. Math. Biol.

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We give a new approach to coding sequence (CDS) density estimation in genomic analysis based on the topological pressure, which we develop from a well known concept in ergodic theory. Topological pressure measures the 'weighted information content' of a finite word, and incorporates 64 parameters which can be interpreted as a choice of weight for each nucleotide triplet. We train the parameters so that the topological pressure fits the observed coding sequence density on the human genome, and use this to give ab initio predictions of CDS density over windows of size around 66,000 bp on the genomes of Mus Musculus, Rhesus Macaque and Drososphilia Melanogaster. While the differences between these genomes are too great to expect that training on the human genome could predict, for example, the exact locations of genes, we demonstrate that our method gives reasonable estimates for the 'coarse scale' problem of predicting CDS density. Inspired again by ergodic theory, the weightings of the nucleotide triplets obtained from our training procedure are used to define a probability distribution on finite sequences, which can be used to distinguish between intron and exon sequences from the human genome of lengths between 750 and 5,000 bp. At the end of the paper, we explain the theoretical underpinning for our approach, which is the theory of Thermodynamic Formalism from the dynamical systems literature. Mathematica and MATLAB implementations of our method are available at http://sourceforge.net/projects/topologicalpres/ .

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Section: Cds Density Estimation On Musmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coding sequence density estimation via topological pressure

Koslicki

Thompson

2014

J. Math. Biol.

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“…Information flow in the biological network has been used in previous studies for predicting protein functions, prioritizing candidate disease genes, and finding network centralities (Nabieva et al, 2005; Newman, 2005; Tu et al, 2006; Stojmirovic and Yu, 2007; Köhler et al, 2008; Suthram et al, 2008; Zotenko et al, 2008; Lee et al, 2009b; Missiuro et al, 2009; Yeger-Lotem et al, 2009; Vanunu and Sharan, 2010). In particular, a flow based approach can be used to augment network information to eQTL analysis, helping identify causal genes in genomic regions and understand the propagation of information signals from causal genes to their target genes.…”

Section: From Genotype To Phenotypementioning

confidence: 99%

Bridging the Gap between Genotype and Phenotype via Network Approaches

Kim¹,

Przytycka²

2013

Front. Genet.

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In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap.

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“…Comparative genomics is a central step in many sequence analysis studies and the annotation of whole genomes through the identification of coding and regulatory regions is one of the major challenges in the current research in molecular biology. Creanza et al [ 63 ] present a statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements, comparing distributions of a set of comparative and non comparative features and evaluating the prediction accuracy of classifiers trained for discriminating sequence elements conserved among human, mouse and rat species.…”

Section: An Overview Of Proceeding Contentsmentioning

confidence: 99%

The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community

D’Elia¹,

Gisel²,

Eriksson

et al. 2009

BMC Bioinformatics

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The EMBnet Conference 2008, focusing on 'Leading Applications and Technologies in Bioinformatics', was organized by the European Molecular Biology network (EMBnet) to celebrate its 20 th anniversary. Since its foundation in 1988, EMBnet has been working to promote collaborative development of bioinformatics services and tools to serve the European community of molecular biology laboratories. This conference was the first meeting organized by the network that was open to the international scientific community outside EMBnet. The conference covered a broad range of research topics in bioinformatics with a main focus on new achievements and trends in emerging technologies supporting genomics, transcriptomics and proteomics analyses such as high-throughput sequencing and data managing, text and data-mining, ontologies and Grid technologies. Papers selected for publication, in this supplement to BMC Bioinformatics, cover a broad range of the topics treated, providing also an overview of the main bioinformatics research fields that the EMBnet community is involved in. EMBnet history, mission and activitiesEMBnet was established in 1988 as an initiative of the European Molecular Biology Laboratory (EMBL) [1]. At that time, high-speed data communication across Europe as well as bioinformatics were in their infancy. The EMBL was accumulating an ever-growing quantity of sequence data and both the molecular biology and the biotechnology research communities needed to have an easy and fast access to those data. Accessing a remote computer using an ordinary command-line oriented terminal was not enough. The solution for eliminating communication delays could only be found in a distribution of data and

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Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements

Cited by 4 publications

References 38 publications

Coding sequence density estimation via topological pressure

Coding sequence density estimation via topological pressure

Bridging the Gap between Genotype and Phenotype via Network Approaches

The 20th anniversary of EMBnet: 20 years of bioinformatics for the Life Sciences community

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