An algorithm for finding protein–DNA binding sites with applications to chromatin- immunoprecipitation microarray experiments

Liu, X. Shirley; Brutlag, Douglas L.; Liu, Jun S.

doi:10.1038/nbt717

Cited by 622 publications

(495 citation statements)

References 15 publications

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“…Sequences in the highest Z score bin (Ն25) were subjected to several motif-searching algorithms (31)(32)(33), which identified 5Ј- Table 1). The intensities of all CSI probe sequences that contain a particular footprinted sequence are averaged together.…”

Section: Resultsmentioning

confidence: 99%

Defining the sequence-recognition profile of DNA-binding molecules

Warren

Kratochvil

Hauschild

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

222

238

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Determining the sequence-recognition properties of DNA-binding proteins and small molecules remains a major challenge. To address this need, we have developed a high-throughput approach that provides a comprehensive profile of the binding properties of DNA-binding molecules. The approach is based on displaying every permutation of a duplex DNA sequence (up to 10 positional variants) on a microfabricated array. The entire sequence space is interrogated simultaneously, and the affinity of a DNA-binding molecule for every sequence is obtained in a rapid, unbiased, and unsupervised manner. Using this platform, we have determined the full molecular recognition profile of an engineered small molecule and a eukaryotic transcription factor. The approach also yielded unique insights into the altered sequence-recognition landscapes as a result of cooperative assembly of DNA-binding molecules in a ternary complex. Solution studies strongly corroborated the sequence preferences identified by the array analysis.chemical genomics ͉ ligand-DNA recognition

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Section: Resultsmentioning

confidence: 99%

Defining the sequence-recognition profile of DNA-binding molecules

Warren

Kratochvil

Hauschild

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

222

238

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“…Overrepresented sequence motifs were identified for low-glucose and static Rap1 targets using MDscan and BioProspector 37,38 . For these and previously known motifs, each Rap1 target was scanned using the MatrixScan module from BioProspector with a third-order Markov background model at a threshold of 0.0001.…”

Section: Motif Determinationmentioning

confidence: 99%

“…A motif that had no predictive value would have an AUC of about 0.5; higher values are better (maximum = 1). B) Overrepresented DNA sequence motifs for static and lowglucose Rap1 targets were determined using MDscan and BioProspector 37,38 . The motif found for the static targets is the archetypical Rap1 binding motif.…”

Section: Motif Determinationmentioning

confidence: 99%

A chromatin-mediated mechanism for specification of conditional transcription factor targets

Buck

Lieb

2006

Nat Genet

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Organisms respond to changes in their environment, and many such responses are initiated at the level of gene transcription. Here, we provide evidence for a previously undiscovered mechanism for directing transcriptional regulators to new binding targets in response to an environmental change. We show that Rap1, a master regulator of yeast metabolism, binds to an expanded target set upon nutrient depletion despite decreasing protein levels and no evidence of posttranslational modification. Computational analysis predicted that proteins capable of recruiting the chromatin regulator Tup1 acted to restrict the binding distribution of Rap1 in the presence of nutrients. Deletions of TUP1, genes that encode recruiters of Tup1, or chromatin regulators recruited by Tup1, cause Rap1 to bind specifically and inappropriately to low-nutrient targets. These data, combined with whole-genome measurements of nucleosome occupancy and Tup1 distribution, provide evidence for a mechanism of dynamic target specification that coordinates the genome-wide distribution of intermediate-affinity DNA sequence motifs with chromatin-mediated regulation of accessibility to those sites. Main TextTo survive in fluctuating environments, organisms must respond to changes in their surroundings. One of the primary means of response at the cellular level is the adjustment of levels of gene transcription 1 . In some cases, new transcriptional programs are established by the binding of regulatory proteins to new gene targets under a given environmental condition 2 . These regulatory factors then act to either activate or repress transcription. Four straightforward mechanisms for the regulation of transcription-factor targeting have been demonstrated. These include altering the regulatory factor's concentration in the nucleus 3 , altering binding affinity by post-translational modification 4 or through an allosteric cofactor 5 , and altering binding properties by expression of a protein cofactor 6 . Here, we present experiments that provide evidence for a new mechanism of dynamic transcription factor target specification. In the proposed mechanism, the genome-wide distribution of DNA sequence motifs for which a factor has intermediate affinity is carefully coordinated with chromatinmediated regulation of accessibility to those sites. In this way, rather than through use of a specific cofactor or post-translational modification, the genome itself is remodeled to change the targeting and biological outcome of an unaltered transcription factor. Rap1 (Repressor-Activator Protein 1) directs a transcriptional program that is central to yeast metabolism, activating the transcription of genes encoding the ribosomal protein subunits and glycolytic enzymes [7][8][9] . Nearly 40% of the mRNA initiation events in mitotically growing yeast are activated by Rap1, yet Rap1 is also required for the repression of these same genes in

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“…In other words, for every pair of promoter sequences corresponding to the 8,741 orthologous genes, we defined a k-mer in C. reinhardtii as conserved if there exists at least one k-mer in its V. carteri orthologous sequence that has at most a mismatches with it. We chose the top x motifs based on the statistical significance, where x is equal to the number of predicted motifs using MERCED (Liu et al, 2002). We then compared the predicted motifs with known motifs in the PLACE database using the STAMP motif comparison tool (Mahony and Benos, 2007) with various E-value cutoffs.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Systematic Prediction of cis-Regulatory Elements in the Chlamydomonas reinhardtii Genome Using Comparative Genomics

Ding

2012

Plant Physiology

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Chlamydomonas reinhardtii is one of the most important microalgae model organisms and has been widely studied toward the understanding of chloroplast functions and various cellular processes. Further exploitation of C. reinhardtii as a model system to elucidate various molecular mechanisms and pathways requires systematic study of gene regulation. However, there is a general lack of genome-scale gene regulation study, such as global cis-regulatory element (CRE) identification, in C. reinhardtii. Recently, large-scale genomic data in microalgae species have become available, which enable the development of efficient computational methods to systematically identify CREs and characterize their roles in microalgae gene regulation. Here, we performed in silico CRE identification at the whole genome level in C. reinhardtii using a comparative genomics-based method. We predicted a large number of CREs in C. reinhardtii that are consistent with experimentally verified CREs. We also discovered that a large percentage of these CREs form combinations and have the potential to work together for coordinated gene regulation in C. reinhardtii. Multiple lines of evidence from literature, gene transcriptional profiles, and gene annotation resources support our prediction. The predicted CREs will serve, to our knowledge, as the first large-scale collection of CREs in C. reinhardtii to facilitate further experimental study of microalgae gene regulation. The accompanying software tool and the predictions in C. reinhardtii are also made available through a Web-accessible database (http://hulab.ucf.edu/research/projects/Microalgae/sdcre/motifcomb.html).

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An algorithm for finding protein–DNA binding sites with applications to chromatin- immunoprecipitation microarray experiments

Cited by 622 publications

References 15 publications

Defining the sequence-recognition profile of DNA-binding molecules

Defining the sequence-recognition profile of DNA-binding molecules

A chromatin-mediated mechanism for specification of conditional transcription factor targets

Systematic Prediction of cis-Regulatory Elements in the Chlamydomonas reinhardtii Genome Using Comparative Genomics

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