Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data
Identifieur interne : 001F97 ( Main/Exploration ); précédent : 001F96; suivant : 001F98Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data
Auteurs : Juhani K H R [Finlande] ; Harri L Hdesm Ki [Finlande]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2013.
Descripteurs français
- KwdFr :
- ADN (génétique), ADN (métabolisme), Algorithmes, Cartographie d'interactions entre protéines (), Facteur de transcription GATA-1 (génétique), Facteur de transcription GATA-1 (métabolisme), Facteurs de transcription (génétique), Facteurs de transcription (métabolisme), Facteurs de transcription NFATC (génétique), Facteurs de transcription NFATC (métabolisme), Facteurs de transcription des facteurs régulateurs X, Humains, Liaison aux protéines (génétique), Modèles linéaires, Protéines (génétique), Protéines (métabolisme), Protéines de liaison à l'ADN (génétique), Protéines de liaison à l'ADN (métabolisme), Séquençage nucléotidique à haut débit, Séquençage par oligonucléotides en batterie.
- MESH :
- génétique : ADN, Facteur de transcription GATA-1, Facteurs de transcription, Facteurs de transcription NFATC, Liaison aux protéines, Protéines, Protéines de liaison à l'ADN.
- métabolisme : ADN, Facteur de transcription GATA-1, Facteurs de transcription, Facteurs de transcription NFATC, Protéines, Protéines de liaison à l'ADN.
- Algorithmes, Cartographie d'interactions entre protéines, Facteurs de transcription des facteurs régulateurs X, Humains, Modèles linéaires, Séquençage nucléotidique à haut débit, Séquençage par oligonucléotides en batterie.
English descriptors
- KwdEn :
- Algorithms, DNA (genetics), DNA (metabolism), DNA-Binding Proteins (genetics), DNA-Binding Proteins (metabolism), GATA1 Transcription Factor (genetics), GATA1 Transcription Factor (metabolism), High-Throughput Nucleotide Sequencing, Humans, Linear Models, NFATC Transcription Factors (genetics), NFATC Transcription Factors (metabolism), Oligonucleotide Array Sequence Analysis, Protein Binding (genetics), Protein Interaction Mapping (methods), Proteins (genetics), Proteins (metabolism), Regulatory Factor X Transcription Factors, Transcription Factors (genetics), Transcription Factors (metabolism).
- MESH :
- chemical , genetics : DNA, DNA-Binding Proteins, GATA1 Transcription Factor, NFATC Transcription Factors, Proteins, Transcription Factors.
- chemical , metabolism : DNA, DNA-Binding Proteins, GATA1 Transcription Factor, NFATC Transcription Factors, Proteins, Transcription Factors.
- genetics : Protein Binding.
- methods : Protein Interaction Mapping.
- Algorithms, High-Throughput Nucleotide Sequencing, Humans, Linear Models, Oligonucleotide Array Sequence Analysis, Regulatory Factor X Transcription Factors.
Abstract
Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear
Url:
DOI: 10.1186/1471-2105-14-S10-S2
PubMed: 24267147
PubMed Central: 3750486
Affiliations:
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Le document en format XML
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-mer model for protein-DNA interactions using high-throughput SELEX data</title>
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-mer model for protein-DNA interactions using high-throughput SELEX data</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>DNA (genetics)</term>
<term>DNA (metabolism)</term>
<term>DNA-Binding Proteins (genetics)</term>
<term>DNA-Binding Proteins (metabolism)</term>
<term>GATA1 Transcription Factor (genetics)</term>
<term>GATA1 Transcription Factor (metabolism)</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Humans</term>
<term>Linear Models</term>
<term>NFATC Transcription Factors (genetics)</term>
<term>NFATC Transcription Factors (metabolism)</term>
<term>Oligonucleotide Array Sequence Analysis</term>
<term>Protein Binding (genetics)</term>
<term>Protein Interaction Mapping (methods)</term>
<term>Proteins (genetics)</term>
<term>Proteins (metabolism)</term>
<term>Regulatory Factor X Transcription Factors</term>
<term>Transcription Factors (genetics)</term>
<term>Transcription Factors (metabolism)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>ADN (génétique)</term>
<term>ADN (métabolisme)</term>
<term>Algorithmes</term>
<term>Cartographie d'interactions entre protéines ()</term>
<term>Facteur de transcription GATA-1 (génétique)</term>
<term>Facteur de transcription GATA-1 (métabolisme)</term>
<term>Facteurs de transcription (génétique)</term>
<term>Facteurs de transcription (métabolisme)</term>
<term>Facteurs de transcription NFATC (génétique)</term>
<term>Facteurs de transcription NFATC (métabolisme)</term>
<term>Facteurs de transcription des facteurs régulateurs X</term>
<term>Humains</term>
<term>Liaison aux protéines (génétique)</term>
<term>Modèles linéaires</term>
<term>Protéines (génétique)</term>
<term>Protéines (métabolisme)</term>
<term>Protéines de liaison à l'ADN (génétique)</term>
<term>Protéines de liaison à l'ADN (métabolisme)</term>
<term>Séquençage nucléotidique à haut débit</term>
<term>Séquençage par oligonucléotides en batterie</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="genetics" xml:lang="en"><term>DNA</term>
<term>DNA-Binding Proteins</term>
<term>GATA1 Transcription Factor</term>
<term>NFATC Transcription Factors</term>
<term>Proteins</term>
<term>Transcription Factors</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA</term>
<term>DNA-Binding Proteins</term>
<term>GATA1 Transcription Factor</term>
<term>NFATC Transcription Factors</term>
<term>Proteins</term>
<term>Transcription Factors</term>
</keywords>
<keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Protein Binding</term>
</keywords>
<keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>ADN</term>
<term>Facteur de transcription GATA-1</term>
<term>Facteurs de transcription</term>
<term>Facteurs de transcription NFATC</term>
<term>Liaison aux protéines</term>
<term>Protéines</term>
<term>Protéines de liaison à l'ADN</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Protein Interaction Mapping</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>ADN</term>
<term>Facteur de transcription GATA-1</term>
<term>Facteurs de transcription</term>
<term>Facteurs de transcription NFATC</term>
<term>Protéines</term>
<term>Protéines de liaison à l'ADN</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Algorithms</term>
<term>High-Throughput Nucleotide Sequencing</term>
<term>Humans</term>
<term>Linear Models</term>
<term>Oligonucleotide Array Sequence Analysis</term>
<term>Regulatory Factor X Transcription Factors</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term>
<term>Cartographie d'interactions entre protéines</term>
<term>Facteurs de transcription des facteurs régulateurs X</term>
<term>Humains</term>
<term>Modèles linéaires</term>
<term>Séquençage nucléotidique à haut débit</term>
<term>Séquençage par oligonucléotides en batterie</term>
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<front><div type="abstract" xml:lang="en"><p>Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear <italic>k</italic>
-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative <italic>k</italic>
-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of <italic>k</italic>
-mers in the model and observed that the number of <italic>k</italic>
-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of <italic>k</italic>
-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the <italic>k</italic>
-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for <italic>k</italic>
-mer and PWMs, respectively. Finally, the <italic>k</italic>
-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.</p>
</div>
</front>
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