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Statements

Subject Item
n2:RIV%2F60461373%3A22310%2F13%3A43895100%21RIV14-MSM-22310___
rdf:type
skos:Concept n11:Vysledek
rdfs:seeAlso
http://www.biomedcentral.com/content/pdf/1471-2105-14-205.pdf
dcterms:description
A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a nonhierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations. A growing number of crystal and NMR structures reveals a considerable structural polymorphism of DNA architecture going well beyond the usual image of a double helical molecule. DNA is highly variable with dinucleotide steps exhibiting a substantial flexibility in a sequence-dependent manner. An analysis of the conformational space of the DNA backbone and the enhancement of our understanding of the conformational dependencies in DNA are therefore important for full comprehension of DNA structural polymorphism. A detailed classification of local DNA conformations based on the technique of Fourier averaging was published in our previous work. However, this procedure requires a considerable amount of manual work. To overcome this limitation we developed an automatic classification method consisting of the combination of supervised and unsupervised approaches. A proposed workflow is composed of k-NN method followed by a nonhierarchical single-pass clustering algorithm. We applied this workflow to analyze 816 X-ray and 664 NMR DNA structures released till February 2013. We identified and annotated six new conformers, and we assigned four of these conformers to two structurally important DNA families: guanine quadruplexes and Holliday (four-way) junctions. We also compared populations of the assigned conformers in the dataset of X-ray and NMR structures. In the present work we developed a machine learning workflow for the automatic classification of dinucleotide conformations. Dinucleotides with unassigned conformations can be either classified into one of already known 24 classes or they can be flagged as unclassifiable. The proposed machine learning workflow permits identification of new classes among so far unclassifiable data, and we identified and annotated six new conformations in the X-ray structures released since our previous analysis. The results illustrate the utility of machine learning approaches in the classification of local DNA conformations.
dcterms:title
Automatic workflow for the classification of local DNA conformations Automatic workflow for the classification of local DNA conformations
skos:prefLabel
Automatic workflow for the classification of local DNA conformations Automatic workflow for the classification of local DNA conformations
skos:notation
RIV/60461373:22310/13:43895100!RIV14-MSM-22310___
n11:predkladatel
n20:orjk%3A22310
n3:aktivita
n12:Z n12:P
n3:aktivity
P(GAP305/12/1801), Z(AV0Z50520701), Z(MSM6046137302), Z(MSM6046137306)
n3:cisloPeriodika
205
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n16:5520991
n3:druhVysledku
n19:J
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
62627
n3:idVysledku
RIV/60461373:22310/13:43895100
n3:jazykVysledku
n17:eng
n3:klicovaSlova
cluster analysis; regularized regression; k-NN; MLP; RBF; neural network; machine learning; classification; dinucleotide conformation; DNA
n3:klicoveSlovo
n4:k-NN n4:DNA n4:dinucleotide%20conformation n4:classification n4:RBF n4:cluster%20analysis n4:machine%20learning n4:MLP n4:neural%20network n4:regularized%20regression
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[5337D37F7952]
n3:nazevZdroje
BMC Bioinformatics
n3:obor
n14:JD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
5
n3:projekt
n18:GAP305%2F12%2F1801
n3:rokUplatneniVysledku
n10:2013
n3:svazekPeriodika
14
n3:tvurceVysledku
Schneider, Bohdan Kukal, Jaromír Čech, Petr Černý, Jiří Svozil, Daniel
n3:wos
000321006500001
n3:zamer
n15:AV0Z50520701 n15:MSM6046137306 n15:MSM6046137302
s:issn
1471-2105
s:numberOfPages
14
n22:doi
10.1186/1471-2105-14-205
n21:organizacniJednotka
22310