Selection of oligonucleotides for whole-genome microarrays with semi-automatic update

Golfier, Geoffroy; Lemoine, Sophie; Miltenberg, A. van; Bendjoudi, A.; Rossier, Jean; Crom, Stéphane Le; Potier, Marie‐Claude

doi:10.1093/bioinformatics/btn573

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…Models of cross-hybridization on the other hand need to address two tasks: identifying potential non-targets unintentionally matching the probe, and modelling their influence on the hybridization signal. Established probe design tools already filter out non-specific probes through cross-hybridization prediction after efficient sequence-similarity based detection screens [ 46 , 47 ]; similar to the filtering employed in this study. Latest advances now promise sufficiently fast and more sensitive detection tools based on thermodynamic models [ 48 , 49 ].…”

Section: Discussionmentioning

confidence: 99%

Hybridization thermodynamics of NimbleGen Microarrays

Mueckstein

Leparc²,

Posekany³

et al. 2010

BMC Bioinformatics

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BackgroundWhile microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets.ResultsWe demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex.ConclusionsThis analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals.

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Section: Discussionmentioning

confidence: 99%

Hybridization thermodynamics of NimbleGen Microarrays

Mueckstein

Leparc²,

Posekany³

et al. 2010

BMC Bioinformatics

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“…Case studies 3 and 4: for full database clustering, we analysed 24 taxon-specific collections from 18 databases ( Supplementary Table S1 ): vertebrates (JASPAR ( 7 ), HOCOMOCO mouse and human ( 11 ), Cis-BP ( 9 ), Jolma 2013 ‘HumanTF’ ( 4 ), Jolma 2015 ‘HumanTF_dimers’ ( 13 ), Uniprobe ( 43 ), Fantom5 ‘novel’ motifs ( 44 ), hPDI ( 45 ), epigram ( 46 ), Homer ( 42 ), Encode ( 47 )), plants (JASPAR, Athamap ( 48 ), Cis-BP, ArabidopsisPBM ( 49 ) and Cistrome ( 14 )) and insects (OntheFly ( 50 ), JASPAR, dmmpmm and idmmpmm ( 51 ), Cis-BP ( 9 ), FlyFactorSurvey ( 52 ), DrosphilaTF ( 53 )).…”

Section: Methodsmentioning

confidence: 99%

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

Castro-Mondragón

Jaeger

Thieffry

et al. 2017

Nucleic Acids Research

104

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Transcription factor (TF) databases contain multitudes of binding motifs (TFBMs) from various sources, from which non-redundant collections are derived by manual curation. The advent of high-throughput methods stimulated the production of novel collections with increasing numbers of motifs. Meta-databases, built by merging these collections, contain redundant versions, because available tools are not suited to automatically identify and explore biologically relevant clusters among thousands of motifs. Motif discovery from genome-scale data sets (e.g. ChIP-seq) also produces redundant motifs, hampering the interpretation of results. We present matrix-clustering, a versatile tool that clusters similar TFBMs into multiple trees, and automatically creates non-redundant TFBM collections. A feature unique to matrix-clustering is its dynamic visualisation of aligned TFBMs, and its capability to simultaneously treat multiple collections from various sources. We demonstrate that matrix-clustering considerably simplifies the interpretation of combined results from multiple motif discovery tools, and highlights biologically relevant variations of similar motifs. We also ran a large-scale application to cluster ∼11 000 motifs from 24 entire databases, showing that matrix-clustering correctly groups motifs belonging to the same TF families, and drastically reduced motif redundancy. matrix-clustering is integrated within the RSAT suite (http://rsat.eu/), accessible through a user-friendly web interface or command-line for its integration in pipelines.

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“…At each time point, by comparing each experimental sample to a reference pooled mRNA sample, the relative abundance of each transcript can be measured, which can further be used as a gene’s expression level [ 34 ]. To represent this gene expression measurement data using a time-evolving network, the following steps are followed [ 35 ]. At each developmental point, the 588 genes that are known to play an important role in the development of the Drosophila are selected.…”

Section: Experiments and Evaluationsmentioning

confidence: 99%

Thermodynamic Analysis of Time Evolving Networks

Cheng

Wilson

Rossi

et al. 2018

Entropy

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The problem of how to represent networks, and from this representation, derive succinct characterizations of network structure and in particular how this structure evolves with time, is of central importance in complex network analysis. This paper tackles the problem by proposing a thermodynamic framework to represent the structure of time-varying complex networks. More importantly, such a framework provides a powerful tool for better understanding the network time evolution. Specifically, the method uses a recently-developed approximation of the network von Neumann entropy and interprets it as the thermodynamic entropy for networks. With an appropriately-defined internal energy in hand, the temperature between networks at consecutive time points can be readily derived, which is computed as the ratio of change of entropy and change in energy. It is critical to emphasize that one of the main advantages of the proposed method is that all these thermodynamic variables can be computed in terms of simple network statistics, such as network size and degree statistics. To demonstrate the usefulness of the thermodynamic framework, the paper uses real-world network data, which are extracted from time-evolving complex systems in the financial and biological domains. The experimental results successfully illustrate that critical events, including abrupt changes and distinct periods in the evolution of complex networks, can be effectively characterized.

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Selection of oligonucleotides for whole-genome microarrays with semi-automatic update

Cited by 11 publications

References 9 publications

Hybridization thermodynamics of NimbleGen Microarrays

Hybridization thermodynamics of NimbleGen Microarrays

RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections

Thermodynamic Analysis of Time Evolving Networks

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