As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.

As with motif discovery, TF-binding sites search benefits from a comparative genomics approach. Searching a single genome for TFBS will yield very noisy results. If a number of related genomes are searched, then the search results can be compared and strengthened by requiring that a site be located, for instance, in the promoter region of the same gene for at least two or three species. As in the case of motif discovery, these methods are not often applied to verify experimental results, but can be used to guide experimental research. For instance, comparative genomics searches can be implemented to detect good candidate sites, which are then verified using an experimental technique.

In motif discovery, we are given a set of sequences that we suspect harbor binding sites for a given transcription factor. A typical scenario is data coming from expression experiments, in which we wish to analyze the promoter region of a bunch of genes that are up- or down-regulated under some condition. The goal of motif discovery is to detect the transcription factor binding motif (i.e. the sequence “pattern” bound by the TF), by assuming that it will be overrepresented in our sample of sequences. There are different strategies to accomplish this, but the standard approach uses expectation maximization (EM) and in particular Gibbs sampling or greedy search. Popular algorithms for motif discovery are MEME, Gibbs Motif Sampler or CONSENSUS. More recently, motif discovery algorithms that make use of phylogenetic foot-printing (the idea that TF-binding site will be conserved in the promoter sequences for the same gene in different species) have become available. These are not usually applied to complement experimental work, but can be used to provide a starting point for it. Popular algorithms include FootPrinter and PhyloGibbs.

Once the binding motif for a TF is known, this motif (which essentially defines a pattern) can be used to scan sequences in order to search for putative TF-binding site. This is useful, for instance, when trying to identify TF-binding site in ChIP-chip data. Searching for TF-binding site can be done in numerous ways. The most basic method is consensus search, in sequences are scored according to how many mismatches they have with the consensus sequence for the motif. A more elaborate way of searching involves using regular expressions, which allow to search for more loosely defined motifs [e.g. C(C/G)AT]. Common algorithms for this type of search include Pattern Locator and the DNA Pattern Find method of the SMS2 suite, but also some word processors. Finally, the mainstream way of conducting TF-binding site search is through the use of position-specific scoring matrices, which basically count the occurrences of each base at each position of the motif and use the inferred frequencies to score candidate sites. Algorithms in this last category include TFSEARCH, FITOM, CONSITE, TESS and MatInspector.

Surface plasmon resonance (SPR) is a technique that exploits the generation of plasmons (waves) on the interface between a planar surface and vacuum/insulator. Plasmons are generated by an incident light beam. With proteins/DNA attached to the surface, ligands can be detected as changes in the SPR reflectivity, providing the means for accurate measurement of binding dynamics.