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193
JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles
- Nucleic Acids Res
, 2010
"... JASPAR ..."
Evaluation of algorithm performance in ChIP-seq peak detection
- PLoS One
, 2010
"... Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for th ..."
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Cited by 41 (2 self)
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Next-generation DNA sequencing coupled with chromatin immunoprecipitation (ChIP-seq) is revolutionizing our ability to interrogate whole genome protein-DNA interactions. Identification of protein binding sites from ChIP-seq data has required novel computational tools, distinct from those used for the analysis of ChIP-Chip experiments. The growing popularity of ChIP-seq spurred the development of many different analytical programs (at last count, we noted 31 open source methods), each with some purported advantage. Given that the literature is dense and empirical benchmarking challenging, selecting an appropriate method for ChIP-seq analysis has become a daunting task. Herein we compare the performance of eleven different peak calling programs on common empirical, transcription factor datasets and measure their sensitivity, accuracy and usability. Our analysis provides an unbiased critical assessment of available technologies, and should assist researchers in choosing a suitable tool for handling ChIP-seq data.
Extracting transcription factor targets from ChIP-Seq data. Nucleic acids research 2009;37:e113
"... ChIP-Seq technology, which combines chromatin immunoprecipitation (ChIP) with massively parallel sequencing, is rapidly replacing ChIP-on-chip for the genome-wide identification of transcription factor binding events. Identifying bound regions from the large number of sequence tags produced by ChIP- ..."
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Cited by 37 (4 self)
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ChIP-Seq technology, which combines chromatin immunoprecipitation (ChIP) with massively parallel sequencing, is rapidly replacing ChIP-on-chip for the genome-wide identification of transcription factor binding events. Identifying bound regions from the large number of sequence tags produced by ChIP-Seq is a challenging task. Here, we present GLITR (GLobal Identifier of Target Regions), which accurately identifies enriched regions in target data by calculating a fold-change based on random samples of control (input chromatin) data. GLITR uses a classification method to identify regions in ChIP data that have a peak height and fold-change which do not resemble regions in an input sample. We compare GLITR to several recent methods and show that GLITR has improved sensitivity for identifying bound regions closely matching the consensus sequence of a given transcription factor, and can detect bona fide transcription factor targets missed by other programs. We also use GLITR to address the issue of sequencing depth, and show that sequencing biological replicates identifies far more binding regions than re-sequencing the same sample.
On the immortality of television sets: ‘function’ in the human genome according to the evolution-free gospel of ENCODE.
- Genome Biology and Evolution
, 2013
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Determinants of nucleosome organization in primary human cells. Nature 474
, 2011
"... Nucleosomes are the basic packaging units of chromatin, modulating accessibility of regulatory proteins to DNA and thus influencing eukaryotic gene regulation. Elaborate chromatin remodeling mechanisms have evolved that govern nucleosome organization at promoters, regulatory elements, and other func ..."
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Cited by 30 (0 self)
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Nucleosomes are the basic packaging units of chromatin, modulating accessibility of regulatory proteins to DNA and thus influencing eukaryotic gene regulation. Elaborate chromatin remodeling mechanisms have evolved that govern nucleosome organization at promoters, regulatory elements, and other functional regions in the genome1. Analyses of chromatin landscape have uncovered a variety of mechanisms, including DNA sequence preferences, that can influence nucleosome positions2–4. To identify major determinants of nucleosome organization in the human genome, we utilized deep sequencing to map nucleosome positions in three primary human cell types and in vitro. A majority of the genome exhibited substantial flexibility of nucleosome positions while a small fraction showed reproducibly positioned nucleosomes. Certain sites that position in vitro can anchor the formation of nucleosomal arrays that have cell type-specific spacing in vivo. Our results unveil an interplay of sequence-based nucleosome preferences and non-nucleosomal factors in determining nucleosome organization within mammalian cells. Previous studies in model organisms3–7 as well as initial analyses in human cells8 have identified fundamental aspects of nucleosome organization. We here focus on the dynamic relationships between sequence-based nucleosome preferences and chromatin regulatory function in primary human cells. We mapped tissue-specific and DNA-encoded nucleosome organization across granulocytes and two types of T-cells (CD4+ and CD8+) isolated from the blood of a single human donor, by isolating cellular chromatin and treating it with micrococcal nuclease (MNase) followed by deep sequencing of the resulting nucleosome-protected fragments (Methods, Supplementary Fig. 1). To provide sufficient depth for both
On the detection and refinement of transcription factor binding sites using ChIP-Seq data,”
- Nucleic Acids Research,
, 2010
"... ABSTRACT Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth ..."
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Cited by 29 (2 self)
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ABSTRACT Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.
High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints
"... An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal ke ..."
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Cited by 19 (1 self)
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An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM’s adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding,
19 Infectious Disease Ontology
"... Abstract In the last decade, technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to ..."
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Cited by 19 (1 self)
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Abstract In the last decade, technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. More recently, ontologies have been shown to have significant benefits both for the analysis of data resulting from high-throughput technologies and for automated reasoning applications, and this has led to organized attempts to improve the structure and formal rigor of ontologies in ways that will better support computational analysis and reasoning. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology suite of interoperable ontology modules that together cover the entire infectious disease domain. Acknowledgments: LGC’s contributions were supported by a Career Award from the Burroughs-Wellcome
Use of in vivo biotinylation to study protein-protein and proteinDNA interactions in mouse embryonic stem cells
- Nat Protoc
"... In gene regulation, proteins function as members of protein complexes to recognize chromosomal target DNA loci. In dissecting the pluripotent state in mouse embryonic stem (mES) cells, we have used in vivo biotinylation of critical transcription factors for affinity purification of protein complexe ..."
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Cited by 18 (2 self)
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In gene regulation, proteins function as members of protein complexes to recognize chromosomal target DNA loci. In dissecting the pluripotent state in mouse embryonic stem (mES) cells, we have used in vivo biotinylation of critical transcription factors for affinity purification of protein complexes and chromatin immunoprecipitation (ChIP)-on-chip for target identification, respectively. Here, we describe detailed procedures for such studies to dissect protein-protein and protein-DNA interactions in mES cells. Specifically, the following three procedures will be described: (i) in vivo biotinylation system setup in mES cells; (ii) affinity purification of multiprotein complexes by one-step streptavidin capture and tandem anti-FLAG/streptavidin affinity purification; (iii) biotinmediated ChIP (bioChIP). The system setup takes B50 d to complete, and it takes another B15 d and B3 d to perform affinity purification of protein complexes and bioChIP, respectively. INTRODUCTION Embryonic stem (ES) cells are pluripotent and have great potential in cell replacement therapies. An understanding of pluripotency at the molecular level should provide clues into ways to direct differentiation along specific lineages or reprogram somatic cells to an ES cell phenotype. Vital cellular functions require the coordinated action of a large number of proteins that assemble into an array of multiprotein complexes of distinct composition and structure to execute transcriptional regulation of target genes. The analyses of protein complexes and intricate protein-protein interaction and protein-DNA transcriptional regulatory networks are key to understanding these complex biological systems. ES cells are an excellent system for such biochemical studies because they are relatively homogenous and easily expandable. Purification of pluripotency protein complexes and delineation of the protein interaction network, and mapping chromosomal binding loci of critical ES cell factors, will provide information on how pluripotency factors regulate target gene expression, uncover new factors in self-renewal signaling pathways, and ultimately lead to a better understanding of the molecular mechanisms by which ES cells selfrenew and maintain pluripotency.
Efficient double fragmentation ChIP-seq provides nucleotide resolution protein-DNA binding profiles
- PLoS One
, 2010
"... Immunoprecipitated crosslinked protein-DNA fragments typically range in size from several hundred to several thousand base pairs, with a significant part of chromatin being much longer than the optimal length for next-generation sequencing (NGS) procedures. Because these larger fragments may be non- ..."
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Cited by 16 (3 self)
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Immunoprecipitated crosslinked protein-DNA fragments typically range in size from several hundred to several thousand base pairs, with a significant part of chromatin being much longer than the optimal length for next-generation sequencing (NGS) procedures. Because these larger fragments may be non-random and represent relevant biology that may otherwise be missed, but also because they represent a significant fraction of the immunoprecipitated material, we designed a double-fragmentation ChIP-seq procedure. After conventional crosslinking and immunoprecipitation, chromatin is decrosslinked and sheared a second time to concentrate fragments in the optimal size range for NGS. Besides the benefits of increased chromatin yields, the procedure also eliminates a laborious size-selection step. We show that the doublefragmentation ChIP-seq approach allows for the generation of biologically relevant genome-wide protein-DNA binding profiles from sub-nanogram amounts of TCF7L2/TCF4, TBP and H3K4me3 immunoprecipitated material. Although
