Assessment of Algorithms for Inferring Positional Weight Matrix Motifs of Transcription Factor Binding Sites Using Protein Binding Microarray Data
Identifieur interne : 002274 ( Main/Exploration ); précédent : 002273; suivant : 002275Assessment of Algorithms for Inferring Positional Weight Matrix Motifs of Transcription Factor Binding Sites Using Protein Binding Microarray Data
Auteurs : Yaron Orenstein ; Chaim Linhart ; Ron ShamirSource :
- PLoS ONE [ 1932-6203 ] ; 2012.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : DNA Probes.
- chemical , metabolism : DNA Probes, Transcription Factors.
- methods : Protein Array Analysis.
- Algorithms, Animals, Base Sequence, Binding Sites, Mice, Protein Binding.
Abstract
The new technology of protein binding microarrays (PBMs) allows simultaneous measurement of the binding intensities of a transcription factor to tens of thousands of synthetic double-stranded DNA probes, covering all possible 10-mers. A key computational challenge is inferring the binding motif from these data. We present a systematic comparison of four methods developed specifically for reconstructing a binding site motif represented as a positional weight matrix from PBM data. The reconstructed motifs were evaluated in terms of three criteria: concordance with reference motifs from the literature and ability to predict
Url:
DOI: 10.1371/journal.pone.0046145
PubMed: 23029415
PubMed Central: 3460961
Affiliations:
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last="Linhart">Chaim Linhart</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author><author><name sortKey="Shamir, Ron" sort="Shamir, Ron" uniqKey="Shamir R" first="Ron" last="Shamir">Ron Shamir</name><affiliation><nlm:aff id="aff1"></nlm:aff></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Base Sequence</term><term>Binding Sites</term><term>DNA Probes (chemistry)</term><term>DNA Probes (metabolism)</term><term>Mice</term><term>Protein Array Analysis (methods)</term><term>Protein Binding</term><term>Transcription Factors (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes</term><term>Analyse par réseau de protéines ()</term><term>Animaux</term><term>Facteurs de transcription (métabolisme)</term><term>Liaison aux protéines</term><term>Sites de fixation</term><term>Sondes d'ADN ()</term><term>Sondes d'ADN (métabolisme)</term><term>Souris</term><term>Séquence nucléotidique</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>DNA Probes</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>DNA Probes</term><term>Transcription Factors</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Protein Array Analysis</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Facteurs de transcription</term><term>Sondes d'ADN</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Animals</term><term>Base Sequence</term><term>Binding Sites</term><term>Mice</term><term>Protein Binding</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse par réseau de protéines</term><term>Animaux</term><term>Liaison aux protéines</term><term>Sites de fixation</term><term>Sondes d'ADN</term><term>Souris</term><term>Séquence nucléotidique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The new technology of protein binding microarrays (PBMs) allows simultaneous measurement of the binding intensities of a transcription factor to tens of thousands of synthetic double-stranded DNA probes, covering all possible 10-mers. A key computational challenge is inferring the binding motif from these data. We present a systematic comparison of four methods developed specifically for reconstructing a binding site motif represented as a positional weight matrix from PBM data. The reconstructed motifs were evaluated in terms of three criteria: concordance with reference motifs from the literature and ability to predict <italic>in vivo</italic> and <italic>in vitro</italic> bindings. The evaluation encompassed over 200 transcription factors and some 300 assays. The results show a tradeoff between how the methods perform according to the different criteria, and a dichotomy of method types. Algorithms that construct motifs with low information content predict PBM probe ranking more faithfully, while methods that produce highly informative motifs match reference motifs better. Interestingly, in predicting high-affinity binding, all methods give far poorer results for <italic>in vivo</italic> assays compared to <italic>in vitro</italic> assays.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Bailey, Tl" uniqKey="Bailey T">TL Bailey</name></author><author><name sortKey="Elkan, C" uniqKey="Elkan C">C Elkan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Aparicio, O" uniqKey="Aparicio O">O Aparicio</name></author><author><name sortKey="Geisberg, Jv" uniqKey="Geisberg J">JV Geisberg</name></author><author><name sortKey="Struhl, K" uniqKey="Struhl K">K Struhl</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Johnson, Ds" uniqKey="Johnson D">DS Johnson</name></author><author><name sortKey="Mortazavi, A" uniqKey="Mortazavi A">A Mortazavi</name></author><author><name sortKey="Myers, Rm" uniqKey="Myers R">RM Myers</name></author><author><name 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