\ The orchestrated binding of transcriptional activators and repressors\ to specific DNA sequences in the context of chromatin defines the\ regulatory program of eukaryotic genomes. We developed a digital\ approach to assay regulatory protein occupancy on genomic DNA in vivo\ by dense mapping of individual DNase I cleavages from intact nuclei\ using massively parallel DNA sequencing. Analysis of >23 million\ cleavages across the Saccharomyces cerevisiae genome revealed\ thousands of protected regulatory protein footprints, enabling de\ novo derivation of factor binding motifs as well as the\ identification of hundreds of novel binding sites for major\ regulators. We observed striking correspondence between\ nucleotide-level DNase I cleavage patterns and protein-DNA\ interactions determined by crystallography. The data also yielded a\ detailed view of larger chromatin features including positioned\ nucleosomes flanking factor binding regions. Digital genomic\ footprinting provides a powerful approach to delineate the\ cis-regulatory framework of any organism with an available genome\ sequence.
\ \ \ \\ DNaseI-seq cleavage counts are displayed at nucleotide resolution,\ along with a 'mappability' track that indicates whether tag sequences\ starting at that location on both the forward and the reverse strands can be\ uniquely mapped to the yeast genome. Finally, the set of footprints\ with q values <0.1 are included, where the q value is\ defined as the minimal false discovery rate threshold at which the\ given footprint is deemed significant. The name associated with each\ footprint is its q value.
\ \\ To visualize regulatory protein occupancy across the genome of\ Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was\ coupled with massively parallel DNA sequencing to create a dense\ whole-genome map of DNA template accessibility at the \ nucleotide-level.
\ \\ Yeast nuclei were isolated and treated with a DNase I concentration\ sufficient to release short (<300 bp) DNA fragments. Small\ fragments were derived from two DNase I "hits" in close proximity.\ Each end of those fragments represents an in vivo DNase I cleavage\ site. The sequence and hence genomic location of these sites were then\ determined by DNA sequencing.
\ \\ Footprints were identified using a computational algorithm that\ evaluates short regions (between 8 and 30 bp) over which the DNase I\ cleavage density was significantly reduced compared with the\ immediately flanking regions. FDR thresholds were assigned to each\ footprint by comparing p-values obtained from real and shuffled\ cleavage data.
\ \\ Detailed methods are given in Hesselberth et al. (2009), and\ supplementary data and source code are available\ here.
\ \\ This track was produced at the University of Washington by Jay\ R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard\ Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael\ S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley\ Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos\ (jstam@stamlab.org).
\ \\
Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS,\
Noble WS et al.\
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting.\
Nat Methods. 2009 Apr;6(4):283-9.\
PMID: 19305407; PMC:
\ The orchestrated binding of transcriptional activators and repressors\ to specific DNA sequences in the context of chromatin defines the\ regulatory program of eukaryotic genomes. We developed a digital\ approach to assay regulatory protein occupancy on genomic DNA in vivo\ by dense mapping of individual DNase I cleavages from intact nuclei\ using massively parallel DNA sequencing. Analysis of >23 million\ cleavages across the Saccharomyces cerevisiae genome revealed\ thousands of protected regulatory protein footprints, enabling de\ novo derivation of factor binding motifs as well as the\ identification of hundreds of novel binding sites for major\ regulators. We observed striking correspondence between\ nucleotide-level DNase I cleavage patterns and protein-DNA\ interactions determined by crystallography. The data also yielded a\ detailed view of larger chromatin features including positioned\ nucleosomes flanking factor binding regions. Digital genomic\ footprinting provides a powerful approach to delineate the\ cis-regulatory framework of any organism with an available genome\ sequence.
\ \ \ \\ DNaseI-seq cleavage counts are displayed at nucleotide resolution,\ along with a 'mappability' track that indicates whether tag sequences\ starting at that location on both the forward and the reverse strands can be\ uniquely mapped to the yeast genome. Finally, the set of footprints\ with q values <0.1 are included, where the q value is\ defined as the minimal false discovery rate threshold at which the\ given footprint is deemed significant. The name associated with each\ footprint is its q value.
\ \\ To visualize regulatory protein occupancy across the genome of\ Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was\ coupled with massively parallel DNA sequencing to create a dense\ whole-genome map of DNA template accessibility at the \ nucleotide-level.
\ \\ Yeast nuclei were isolated and treated with a DNase I concentration\ sufficient to release short (<300 bp) DNA fragments. Small\ fragments were derived from two DNase I "hits" in close proximity.\ Each end of those fragments represents an in vivo DNase I cleavage\ site. The sequence and hence genomic location of these sites were then\ determined by DNA sequencing.
\ \\ Footprints were identified using a computational algorithm that\ evaluates short regions (between 8 and 30 bp) over which the DNase I\ cleavage density was significantly reduced compared with the\ immediately flanking regions. FDR thresholds were assigned to each\ footprint by comparing p-values obtained from real and shuffled\ cleavage data.
\ \\ Detailed methods are given in Hesselberth et al. (2009), and\ supplementary data and source code are available\ here.
\ \\ This track was produced at the University of Washington by Jay\ R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard\ Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael\ S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley\ Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos\ (jstam@stamlab.org).
\ \\
Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS,\
Noble WS et al.\
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting.\
Nat Methods. 2009 Apr;6(4):283-9.\
PMID: 19305407; PMC:
\ The orchestrated binding of transcriptional activators and repressors\ to specific DNA sequences in the context of chromatin defines the\ regulatory program of eukaryotic genomes. We developed a digital\ approach to assay regulatory protein occupancy on genomic DNA in vivo\ by dense mapping of individual DNase I cleavages from intact nuclei\ using massively parallel DNA sequencing. Analysis of >23 million\ cleavages across the Saccharomyces cerevisiae genome revealed\ thousands of protected regulatory protein footprints, enabling de\ novo derivation of factor binding motifs as well as the\ identification of hundreds of novel binding sites for major\ regulators. We observed striking correspondence between\ nucleotide-level DNase I cleavage patterns and protein-DNA\ interactions determined by crystallography. The data also yielded a\ detailed view of larger chromatin features including positioned\ nucleosomes flanking factor binding regions. Digital genomic\ footprinting provides a powerful approach to delineate the\ cis-regulatory framework of any organism with an available genome\ sequence.
\ \ \ \\ DNaseI-seq cleavage counts are displayed at nucleotide resolution,\ along with a 'mappability' track that indicates whether tag sequences\ starting at that location on both the forward and the reverse strands can be\ uniquely mapped to the yeast genome. Finally, the set of footprints\ with q values <0.1 are included, where the q value is\ defined as the minimal false discovery rate threshold at which the\ given footprint is deemed significant. The name associated with each\ footprint is its q value.
\ \\ To visualize regulatory protein occupancy across the genome of\ Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was\ coupled with massively parallel DNA sequencing to create a dense\ whole-genome map of DNA template accessibility at the \ nucleotide-level.
\ \\ Yeast nuclei were isolated and treated with a DNase I concentration\ sufficient to release short (<300 bp) DNA fragments. Small\ fragments were derived from two DNase I "hits" in close proximity.\ Each end of those fragments represents an in vivo DNase I cleavage\ site. The sequence and hence genomic location of these sites were then\ determined by DNA sequencing.
\ \\ Footprints were identified using a computational algorithm that\ evaluates short regions (between 8 and 30 bp) over which the DNase I\ cleavage density was significantly reduced compared with the\ immediately flanking regions. FDR thresholds were assigned to each\ footprint by comparing p-values obtained from real and shuffled\ cleavage data.
\ \\ Detailed methods are given in Hesselberth et al. (2009), and\ supplementary data and source code are available\ here.
\ \\ This track was produced at the University of Washington by Jay\ R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard\ Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael\ S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley\ Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos\ (jstam@stamlab.org).
\ \\
Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS,\
Noble WS et al.\
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting.\
Nat Methods. 2009 Apr;6(4):283-9.\
PMID: 19305407; PMC:
\
This track displays the location of clones (mostly lambda and cosmid clones) \
from Washington University in\
St. Louis using the names assigned by that group. This information was \
downloaded from the Saccharomyces Genome Database (SGD) from the file
\
https://downloads.yeastgenome.org/curation/chromosomal_feature/clone.tab.\
\
\ Thanks to Washington University \ in St. Louis and the SGD\ for the data used in this track.\ \ map 1 altColor 180,180,180\ group map\ longLabel Washington University Clones\ priority 14\ shortLabel WashU Clones\ track sgdClone\ type bed 4 +\ visibility hide\ sgdGene SGD Genes genePred sgdPep Protein-Coding Genes from Saccharomyces Genome Database 3 39 0 100 180 127 177 217 0 0 0
\
This track shows a variety of features in the \
Saccharomyces cerevisiae genome, including\
tRNAs, transposons, centromeres, and open reading frames (ORFs) classified as \
dubious.\
The data were downloaded from the\
Saccharomyces Genome Database (SGD) from the file
\
ftp://genome-ftp.stanford.edu/pub/yeast/data_download/chromosomal_feature/s_cerevisiae.gff3 on 27 Nov. 2003. \
Click on an item in this track to display details about it.\
\
This track shows putative regulatory elements in Saccharomyces\ cerevisiae that are supported by cross-species evidence (Harbison,\ Gordon, et al., 2004). Harbison, Gordon, et al. performed a genome-wide\ location analysis with 203 known DNA-binding transcriptional regulators\ (some under multiple environmental conditions) and identified 11,000\ high-confidence interactions between regulators and promoter regions. They\ then compiled a compendium of motifs for 102 transcriptional regulators\ based on a combination of their experimental results, cross-species\ conservation data for four species of yeast and motifs from the\ literature. Finally, they mapped these motifs to the\ S. cerevisiae genome. This track shows positions at which these\ motifs matched the genome with high confidence and at which the\ matching sequence was well conserved across yeast species.
\ \The details page for each putative binding site shows the sequence at\ that site compared to the position-specific probability matrix for the\ associated transcriptional regulator (shown as both a table and a graphical\ logo). It also indicates whether the binding site is supported by\ experimental (ChIP-chip) results and the number of other yeast species in\ which it is conserved.
\ \See also the "Reg. ChIP-chip" track for additional related information.
\ \The scoring ranges from 200 to 1000 and is based on the number of lines of \ evidence that support the motif being active. Each of the two sensu \ stricto species in which the motif was conserved counts as a line of \ evidence. If the ChIP-chip data showed good (P ≤ 0.001) evidence of binding \ to the transcription factor associated with the motif, that counts as two \ lines of evidence. If the ChIP-chip data showed weaker (P ≤ 0.005) evidence \ of binding, that counts as just one line of evidence. The following table \ shows the relationship between lines of evidence and score:
\ \
\\ \\
\ \
\\\\ Evidence Score \ 4 1000 \ 3 500 \ 2 333 \ \ 1 250 \ 0 200
This track shows the location of the probes spotted on a slide in\ the chromatin immunoprecipitation/microarray hybridization (ChIP-chip)\ experiments described in Harbison, Gordon et al. below.\ Click on an item in this track to display a page showing which\ transcription factors pulled down DNA that is enriched for this probe\ sequence, which transcription factor binding site motifs are present in\ the probe and whether these motifs are conserved in related yeast species.\ See also the "Regulatory Code" track for the position of the individual\ motifs.\ \
\ This track shows ab initio predictions from the program\ AUGUSTUS (version 3.1).\ The predictions are based on the genome sequence alone.\
\ \\ For more information on the different gene tracks, see our Genes FAQ.
\ \\ Statistical signal models were built for splice sites, branch-point\ patterns, translation start sites, and the poly-A signal.\ Furthermore, models were built for the sequence content of\ protein-coding and non-coding regions as well as for the length distributions\ of different exon and intron types. Detailed descriptions of most of these different models\ can be found in Mario Stanke's\ dissertation.\ This track shows the most likely gene structure according to a\ Semi-Markov Conditional Random Field model.\ Alternative splicing transcripts were obtained with\ a sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2\ --minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2).\
\ \\ The different models used by Augustus were trained on a number of different species-specific\ gene sets, which included 1000-2000 training gene structures. The --species option allows\ one to choose the species used for training the models. Different training species were used\ for the --species option when generating these predictions for different groups of\ assemblies.\
| Assembly Group | \ \ \Training Species | \ \
| Fish | \ \ \zebrafish\ \ |
| Birds | \ \ \chicken\ \ |
| Human and all other vertebrates | \ \ \human\ \ |
| Nematodes | \ \ \caenorhabditis | \ \
| Drosophila | \ \ \fly | \ \
| A. mellifera | \ \ \honeybee1 | \ \
| A. gambiae | \ \ \culex | \ \
| S. cerevisiae | \ \ \saccharomyces | \ \
\ This table describes which training species was used for a particular group of assemblies.\ When available, the closest related training species was used.\
\ \\ Stanke M, Diekhans M, Baertsch R, Haussler D.\ \ Using native and syntenically mapped cDNA alignments to improve de novo gene finding.\ Bioinformatics. 2008 Mar 1;24(5):637-44.\ PMID: 18218656\
\ \\ Stanke M, Waack S.\ \ Gene prediction with a hidden Markov model and a new intron submodel.\ Bioinformatics. 2003 Oct;19 Suppl 2:ii215-25.\ PMID: 14534192\
\ genes 1 baseColorDefault genomicCodons\ baseColorUseCds given\ color 12,105,0\ group genes\ longLabel AUGUSTUS ab initio gene predictions v3.1\ shortLabel AUGUSTUS\ track augustusGene\ type genePred\ visibility hide\ multizYeast Conservation wigMaf 0.0 1.0 Seven Species of Saccharomyces, Alignments & Conservation 3 100 0 0 0 127 127 127 0 0 0\ This track shows a measure of evolutionary conservation in seven species of\ the genus Saccharomyces based on a phylogenetic hidden Markov model\ (phastCons). The graphic display shows the alignment projected onto\ S. cerevisiae. \
\
The genomes were downloaded from:
\
\ In full display mode, this track shows the overall conservation score across all\ species as well as pairwise alignments \ of each species with S. cerevisiae. The pairwise alignments are\ shown in dense display mode using a grayscale \ density gradient. The checkboxes in the track configuration section allow\ the exclusion of species from the pairwise display; however, this does not\ remove them from the conservation score display.
\\ When zoomed-in to the base-display level, the track shows the base \ composition of each alignment. The numbers and symbols on the Gaps\ line indicate the lengths of gaps in the S. cerevisiae sequence at those \ alignment positions relative to the longest non-S. cerevisiae sequence. \ If there is sufficient space in the display, the size of the gap is shown; \ if not, and if the gap size is a multiple of 3, a "*" is displayed, \ otherwise "+" is shown. \ To view detailed information about the alignments at a specific position,\ zoom in the display to 30,000 or fewer bases, then click on the alignment.
\\ This track may be configured in a variety of ways to highlight different aspects \ of the displayed information. Click the \ Graph \ configuration help link for an explanation of the configuration options.
\ \\ Best-in-genome pairwise alignments were generated for each species \ using blastz, followed by chaining and netting. The pairwise alignments\ were then multiply aligned using multiz, and\ the resulting multiple alignments were assigned \ conservation scores by phastCons.
\\ The phastCons program computes conservation scores based on a phylo-HMM, a\ type of probabilistic model that describes both the process of DNA\ substitution at each site in a genome and the way this process changes from\ one site to the next (Felsenstein and Churchill 1996, Yang 1995, Siepel and\ Haussler 2005). PhastCons uses a two-state phylo-HMM, with a state for\ conserved regions and a state for non-conserved regions. The value plotted\ at each site is the posterior probability that the corresponding alignment\ column was "generated" by the conserved state of the phylo-HMM. These\ scores reflect the phylogeny (including branch lengths) of the species in\ question, a continuous-time Markov model of the nucleotide substitution\ process, and a tendency for conservation levels to be autocorrelated along\ the genome (i.e., to be similar at adjacent sites). The general reversible\ (REV) substitution model was used. Note that, unlike many\ conservation-scoring programs, phastCons does not rely on a sliding window\ of fixed size, so short highly-conserved regions and long moderately\ conserved regions can both obtain high scores. More information about\ phastCons can be found in Siepel et al. (2005).
\\ PhastCons currently treats alignment gaps as missing data, which\ sometimes has the effect of producing undesirably high conservation scores\ in gappy regions of the alignment. We are looking at several possible ways\ of improving the handling of alignment gaps.
\ \\ This track was created at UCSC using the following programs:\
\ Felsenstein J, Churchill GA.\ A Hidden Markov Model approach to\ variation among sites in rate of evolution.\ Mol Biol Evol. 1996 Jan;13(1):93-104.\ PMID: 8583911\
\ \\ Siepel A, Haussler D.\ Phylogenetic Hidden Markov Models.\ In: Nielsen R, editor. Statistical Methods in Molecular Evolution.\ New York: Springer; 2005. pp. 325-351.\
\ \\
Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K,\
Clawson H, Spieth J, Hillier LW, Richards S, et al.\
Evolutionarily conserved elements in vertebrate, insect, worm,\
and yeast genomes.\
Genome Res. 2005 Aug;15(8):1034-50.\
PMID: 16024819; PMC:
\
Yang Z.\
A space-time process model for the evolution of DNA\
sequences.\
Genetics. 1995 Feb;139(2):993-1005.\
PMID: 7713447; PMC:
\
Kent WJ, Baertsch R, Hinrichs A, Miller W, Haussler D.\
Evolution's cauldron:\
duplication, deletion, and rearrangement in the mouse and human genomes.\
Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9.\
PMID: 14500911; PMC:
\
Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM,\
Baertsch R, Rosenbloom K, Clawson H, Green ED, et al.\
Aligning multiple genomic sequences with the threaded blockset aligner.\
Genome Res. 2004 Apr;14(4):708-15.\
PMID: 15060014; PMC:
\ Harris RS.\ Improved pairwise alignment of genomic DNA.\ Ph.D. Thesis. Pennsylvania State University, USA. 2007.\
\ \\ Chiaromonte F, Yap VB, Miller W.\ Scoring pairwise genomic sequence alignments.\ Pac Symp Biocomput. 2002:115-26.\ PMID: 11928468\
\ compGeno 1 autoScale Off\ group compGeno\ longLabel Seven Species of Saccharomyces, Alignments & Conservation\ maxHeightPixels 100:40:11\ pairwise pwMaf\ priority 100\ shortLabel Conservation\ speciesOrder sacPar sacMik sacKud sacBay sacCas sacKlu\ track multizYeast\ type wigMaf 0.0 1.0\ visibility pack\ wiggle phastCons\ yLineOnOff Off\ ensGene Ensembl Genes genePred ensPep Ensembl Genes 0 100 150 0 0 202 127 127 0 0 0\ These gene predictions were generated by Ensembl.\
\ \\ For more information on the different gene tracks, see our Genes FAQ.
\ \\ For a description of the methods used in Ensembl gene predictions, please refer to\ Hubbard et al. (2002), also listed in the References section below. \
\ \\
Ensembl Gene data can be explored interactively using the\
Table Browser or the\
Data Integrator. \
For local downloads, the genePred format files for sacCer1 are available in our\
\
downloads directory as ensGene.txt.gz or in our\
\
genes download directory in GTF format.
\
For programmatic access, the data can be queried from the \
REST API or\
directly from our public MySQL\
servers. Instructions on this method are available on our\
MySQL help page and on\
our blog.
\ Previous versions of this track can be found on our archive download server.\
\ \\ We would like to thank Ensembl for providing these gene annotations. For more information, please see\ Ensembl's genome annotation page.\
\ \\
Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, Cox T, Cuff J,\
Curwen V, Down T et al.\
The Ensembl genome database project.\
Nucleic Acids Res. 2002 Jan 1;30(1):38-41.\
PMID: 11752248; PMC:
\ The GC percent track shows the percentage of G (guanine) and C (cytosine) bases\ in a 20,000 base window. Windows with high GC content are drawn more darkly \ than windows with low GC content. High GC content is typically associated with \ gene-rich areas.\
\\ This track was generated at UCSC.\ map 1 group map\ longLabel Percentage GC in 20,000-Base Windows\ shortLabel GC Percent\ spectrum on\ track gcPercent\ type bed 4 +\ visibility hide\ blastHg16KG Human Proteins psl protein Human Proteins (hg16) Mapped by Chained tBLASTn 0 100 0 0 0 127 127 127 0 0 0
\ This track contains tBLASTn alignments of the peptides from the predicted \ and known genes identified in the hg16 Known Genes track.\
\ \\ First, the predicted proteins from the human Known Genes track were aligned \ with the human genome using the Blat program to discover exon boundaries. \ Next, the amino acid sequences that make up each exon were aligned with the \ S. cerevisiae sequence using the tBLASTn program.\ Finally, the putative S. cerevisiae exons were chained together using an \ organism-specific maximum gap size but no gap penalty. The single best exon \ chains extending over more than 60% of the query protein were included. Exon \ chains that extended over 60% of the query and matched at least 60% of the \ protein's amino acids were also included.
\ \\ tBLASTn is part of the NCBI BLAST tool set. For more information on BLAST, see\ Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ.\ Basic local alignment search tool.\ J Mol Biol. 1990 Oct 5;215(3):403-410.
\\ Blat was written by Jim Kent. The remaining utilities \ used to produce this track were written by Jim Kent or Brian Raney.
\ genes 1 blastRef hg16.blastKGRef00\ colorChromDefault off\ group genes\ longLabel Human Proteins (hg16) Mapped by Chained tBLASTn\ pred hg16.blastKGPep00\ shortLabel Human Proteins\ track blastHg16KG\ type psl protein\ visibility hide\ phastConsElements Most Conserved bed 5 . PhastCons Conserved Elements (Seven Species of Saccharomyces) 0 100 0 0 0 127 127 127 0 0 0\ This track shows predictions of conserved elements produced by the phastCons\ program. PhastCons is part of the \ PHAST (PHylogenetic Analysis with \ Space/Time models) package. The predictions are based on a phylogenetic hidden \ Markov model (phylo-HMM), a type of probabilistic model that describes both \ the process of DNA substitution at each site in a genome and the way this \ process changes from one site to the next.
\ \\ Best-in-genome pairwise alignments were generated for\ each species using blastz, followed by chaining and netting. A multiple\ alignment was then constructed from these pairwise alignments using multiz.\ Predictions of conserved elements were then obtained by running phastCons\ on the multiple alignments with the --most-conserved option.
\\ PhastCons constructs a two-state phylo-HMM with a state for conserved\ regions and a state for non-conserved regions. The two states share a\ single phylogenetic model, except that the branch lengths of the tree\ associated with the conserved state are multiplied by a constant scaling\ factor rho (0 <= rho <= 1). The free parameters of the\ phylo-HMM, including the scaling factor rho, are estimated from\ the data by maximum likelihood using an EM algorithm. This procedure is\ subject to certain constraints on the "coverage" of the genome by conserved\ elements and the "smoothness" of the conservation scores. Details can be\ found in Siepel et al. (2005).
\\ The predicted conserved elements are segments of the alignment that are\ likely to have been "generated" by the conserved state of the phylo-HMM.\ Each element is assigned a log-odds score equal to its log probability\ under the conserved model minus its log probability under the non-conserved\ model. The "score" field associated with this track contains transformed\ log-odds scores, taking values between 0 and 1000. (The scores are\ transformed using a monotonic function of the form a * log(x) + b.) The\ raw log odds scores are retained in the "name" field and can be seen on the\ details page or in the browser when the track's display mode is set to\ "pack" or "full".
\ \\ This track was created at UCSC using the following programs:\
\
Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, Clawson H, Spieth J, Hillier\
LW, Richards S et al.\
Evolutionarily conserved elements in vertebrate, insect, worm, \
and yeast genomes.\
Genome Res. 2005 Aug;15(8):1034-50.\
PMID: 16024819; PMC:
\
Kent WJ, Baertsch R, Hinrichs A, Miller W, and Haussler D.\
Evolution's cauldron: Duplication, deletion, and rearrangement\
in the mouse and human genomes.\
Proc Natl Acad Sci U S A. 2003 Sep 30;100(20):11484-9.\
PMID: 14500911; PMC:
\
Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AF, Roskin KM, Baertsch R, Rosenbloom K, Clawson\
H, Green ED et al.\
Aligning multiple genomic sequences with the threaded blockset aligner.\
Genome Res. 2004 Apr;14(4):708-15.\
PMID: 15060014; PMC:
\ Chiaromonte F, Yap VB, Miller W. \ Scoring pairwise genomic sequence alignments. \ Pac Symp Biocomput. 2002:115-26.\ PMID: 11928468\
\ \\
Schwartz S, Kent WJ, Smit A, Zhang Z, Baertsch R, Hardison R, \
Haussler D, and Miller W.\
Human-Mouse Alignments with BLASTZ. \
Genome Res. 2003 Jan;13(1):103-7.\
PMID: 12529312; PMC:
\ This track displays literature-curated regulatory regions, transcription\ factor binding sites, and regulatory polymorphisms from\ ORegAnno (Open Regulatory Annotation). For more detailed\ information on a particular regulatory element, follow the link to ORegAnno\ from the details page. \ \
\ \The display may be filtered to show only selected region types, such as:
\ \To exclude a region type, uncheck the appropriate box in the list at the top of \ the Track Settings page.
\ \\ An ORegAnno record describes an experimentally proven and published regulatory\ region (promoter, enhancer, etc.), transcription factor binding site, or\ regulatory polymorphism. Each annotation must have the following attributes:\
\ ORegAnno core team and principal contacts: Stephen Montgomery, Obi Griffith, \ and Steven Jones from Canada's Michael Smith Genome Sciences Centre, Vancouver, \ British Columbia, Canada.
\\ The ORegAnno community (please see individual citations for various\ features): ORegAnno Citation.\ \
\
Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJ, Montgomery SB, Griffith OL, Open\
Regulatory Annotation Consortium..\
\
ORegAnno 3.0: a community-driven resource for curated regulatory annotation.\
Nucleic Acids Res. 2016 Jan 4;44(D1):D126-32.\
PMID: 26578589; PMC:
\
Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, Mahony S, Sleumer MC, Bilenky M,\
Haeussler M et al.\
\
ORegAnno: an open-access community-driven resource for regulatory annotation.\
Nucleic Acids Res. 2008 Jan;36(Database issue):D107-13.\
PMID: 18006570; PMC:
\ Montgomery SB, Griffith OL, Sleumer MC, Bergman CM, Bilenky M, Pleasance ED, \ Prychyna Y, Zhang X, Jones SJ. \ ORegAnno: an open access database and curation system for \ literature-derived promoters, transcription factor binding sites and regulatory variation.\ Bioinformatics. 2006 Mar 1;22(5):637-40.\ PMID: 16397004\
\ \ regulation 1 color 102,102,0\ group regulation\ longLabel Regulatory elements from ORegAnno\ shortLabel ORegAnno\ track oreganno\ type bed 4 +\ visibility hide\ xenoRefGene Other RefSeq genePred xenoRefPep xenoRefMrna Non-S. cerevisiae RefSeq Genes 1 100 12 12 120 133 133 187 0 0 0\ This track shows known protein-coding and non-protein-coding genes \ for organisms other than S. cerevisiae, taken from the NCBI RNA reference \ sequences collection (RefSeq). The data underlying this track are \ updated weekly.
\ \\ This track follows the display conventions for \ gene prediction \ tracks.\ The color shading indicates the level of review the RefSeq record has \ undergone: predicted (light), provisional (medium), reviewed (dark).
\\ The item labels and display colors of features within this track can be\ configured through the controls at the top of the track description page. \
\ The RNAs were aligned against the S. cerevisiae genome using blat; those\ with an alignment of less than 15% were discarded. When a single RNA aligned \ in multiple places, the alignment having the highest base identity was \ identified. Only alignments having a base identity level within 0.5% of \ the best and at least 25% base identity with the genomic sequence were kept.\
\ \\ This track was produced at UCSC from RNA sequence data\ generated by scientists worldwide and curated by the \ NCBI RefSeq project.
\ \\
Kent WJ.\
\
BLAT--the BLAST-like alignment tool.\
Genome Res. 2002 Apr;12(4):656-64.\
PMID: 11932250; PMC:
\
Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, Ermolaeva O, Farrell CM, Hart J,\
Landrum MJ, McGarvey KM et al.\
\
RefSeq: an update on mammalian reference sequences.\
Nucleic Acids Res. 2014 Jan;42(Database issue):D756-63.\
PMID: 24259432; PMC:
\
Pruitt KD, Tatusova T, Maglott DR.\
\
NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins.\
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D501-4.\
PMID: 15608248; PMC:
\ This track shows predicted transcription factor binding sites \ based on sequence similarities upstream of coordinately expressed genes.\
\ In dense display mode the gold areas indicate the extent of the area\ searched for binding sites; black boxes indicate the actual\ binding sites. In other modes the gold areas disappear and only\ the binding sites are displayed. Clicking on a particular predicted binding \ site displays a page that shows the sequence motif associated with the \ predicted transcription factor and the sequence at the predicted binding site.\ Where known motifs have been identified by this method, they are named;\ otherwise, they are assigned a motif number.\ \
\ This analysis was performed according to \ Genome-wide discovery of transcriptional modules from DNA \ sequence and gene expression on various pre-existing microarray datasets.\ A regulatory module is comprised of a set of genes predicted to be regulated \ by the same combination of DNA sequence motifs. The predictions are based on \ the co-expression of the set of genes in the module and on the appearance of\ common combinations of motifs in the upstream regions of genes assigned to\ the same module. \ \
\ Thanks to Eran Segal for providing the data analysis that forms the \ basis for this track. The display was programmed by \ Jim Kent.\ \
\ Segal E, Yelensky R, Koller D.\ \ Genome-wide discovery of transcriptional modules from DNA sequence and gene expression.\ Bioinformatics. 2003;19 Suppl 1:i273-82.\ PMID: 12855470\
\ regulation 1 exonArrows off\ group regulation\ longLabel Eran Segal Regulatory Module\ noScoreFilter .\ shortLabel Reg. Module\ spectrum on\ track esRegGeneToMotif\ type bed 6 +\ visibility dense\ est S. cer. ESTs psl est S. cerevisiae ESTs Including Unspliced 0 100 0 0 0 127 127 127 1 0 0\ This track shows alignments between S. cerevisiae expressed sequence tags\ (ESTs) in \ GenBank and the genome. ESTs are single-read sequences,\ typically about 500 bases in length, that usually represent fragments of\ transcribed genes.\
\ \\ This track follows the display conventions for\ \ PSL alignment tracks. In dense display mode, the items that\ are more darkly shaded indicate matches of better quality.\
\ \\ The strand information (+/-) indicates the\ direction of the match between the EST and the matching\ genomic sequence. It bears no relationship to the direction\ of transcription of the RNA with which it might be associated.\
\ \\ The description page for this track has a filter that can be used to change\ the display mode, alter the color, and include/exclude a subset of items\ within the track. This may be helpful when many items are shown in the track\ display, especially when only some are relevant to the current task.\
\ \\ To use the filter:\
\ This track may also be configured to display base labeling, a feature that\ allows the user to display all bases in the aligning sequence or only those\ that differ from the genomic sequence. For more information about this option,\ go to the\ \ Base Coloring for Alignment Tracks page.\ Several types of alignment gap may also be colored;\ for more information, go to the\ \ Alignment Insertion/Deletion Display Options page.\
\ \\ To make an EST, RNA is isolated from cells and reverse\ transcribed into cDNA. Typically, the cDNA is cloned\ into a plasmid vector and a read is taken from the 5'\ and/or 3' primer. For most — but not all — ESTs, the\ reverse transcription is primed by an oligo-dT, which\ hybridizes with the poly-A tail of mature mRNA. The\ reverse transcriptase may or may not make it to the 5'\ end of the mRNA, which may or may not be degraded.\
\ \\ In general, the 3' ESTs mark the end of transcription\ reasonably well, but the 5' ESTs may end at any point\ within the transcript. Some of the newer cap-selected\ libraries cover transcription start reasonably well. Before the\ cap-selection techniques\ emerged, some projects used random rather than poly-A\ priming in an attempt to retrieve sequence distant from the\ 3' end. These projects were successful at this, but as\ a side effect also deposited sequences from unprocessed\ mRNA and perhaps even genomic sequences into the EST databases.\ Even outside of the random-primed projects, there is a\ degree of non-mRNA contamination. Because of this, a\ single unspliced EST should be viewed with considerable\ skepticism.\
\ \\ To generate this track, S. cerevisiae ESTs from GenBank were aligned\ against the genome using blat. Note that the maximum intron length\ allowed by blat is 750,000 bases, which may eliminate some ESTs with very\ long introns that might otherwise align. When a single\ EST aligned in multiple places, the alignment having the\ highest base identity was identified. Only alignments having\ a base identity level within 0.5% of the best and at least 96% base identity\ with the genomic sequence were kept.\
\ \\ This track was produced at UCSC from EST sequence data\ submitted to the international public sequence databases by\ scientists worldwide.\
\ \\
Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW.\
\
GenBank.\
Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42.\
PMID: 23193287; PMC:
\
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL.\
GenBank: update.\
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6.\
PMID: 14681350; PMC:
\
Kent WJ.\
BLAT - the BLAST-like alignment tool.\
Genome Res. 2002 Apr;12(4):656-64.\
PMID: 11932250; PMC:
\ The mRNA track shows alignments between S. cerevisiae mRNAs\ in GenBank and the genome.
\ \\ This track follows the display conventions for \ PSL alignment tracks. In dense display mode, the items that\ are more darkly shaded indicate matches of better quality.
\\ The description page for this track has a filter that can be used to change \ the display mode, alter the color, and include/exclude a subset of items \ within the track. This may be helpful when many items are shown in the track \ display, especially when only some are relevant to the current task.
\\ To use the filter:\
\ This track may also be configured to display codon coloring, a feature that\ allows the user to quickly compare mRNAs against the genomic sequence. For more \ information about this option, click \ here.\
\ \\ GenBank S. cerevisiae mRNAs were aligned against the genome using the \ blat program. When a single mRNA aligned in multiple places, \ the alignment having the highest base identity was found. \ Only alignments having a base identity level within 0.5% of\ the best and at least 96% base identity with the genomic sequence were kept.\
\ \\ The mRNA track was produced at UCSC from mRNA sequence data\ submitted to the international public sequence databases by \ scientists worldwide.
\ \\
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. \
GenBank: update.\
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6.\
PMID: 14681350; PMC:
\
Kent WJ.\
BLAT - the BLAST-like alignment tool.\
Genome Res. 2002 Apr;12(4):656-64.\
PMID: 11932250; PMC:
\ This track displays simple tandem repeats (possibly imperfect repeats) located\ by Tandem Repeats\ Finder (TRF) which is specialized for this purpose. These repeats can\ occur within coding regions of genes and may be quite\ polymorphic. Repeat expansions are sometimes associated with specific\ diseases.
\ \\ For more information about the TRF program, see Benson (1999).\
\ \\ TRF was written by \ Gary Benson.
\ \\
Benson G.\
\
Tandem repeats finder: a program to analyze DNA sequences.\
Nucleic Acids Res. 1999 Jan 15;27(2):573-80.\
PMID: 9862982; PMC:
\ This track shows alignments between S. cerevisiae expressed sequence tags\ (ESTs) in \ GenBank and the genome that show signs of splicing when\ aligned against the genome. ESTs are single-read sequences, typically about\ 500 bases in length, that usually represent fragments of transcribed genes.\
\ \\ To be considered spliced, an EST must show\ evidence of at least one canonical intron (i.e., the genomic\ sequence between EST alignment blocks must be at least 32 bases in\ length and have GT/AG ends). By requiring splicing, the level\ of contamination in the EST databases is drastically reduced\ at the expense of eliminating many genuine 3' ESTs.\ For a display of all ESTs (including unspliced), see the\ S. cerevisiae EST track.\
\ \\ This track follows the display conventions for\ \ PSL alignment tracks. In dense display mode, darker shading\ indicates a larger number of aligned ESTs.\
\ \\ The strand information (+/-) indicates the\ direction of the match between the EST and the matching\ genomic sequence. It bears no relationship to the direction\ of transcription of the RNA with which it might be associated.\
\ \\ The description page for this track has a filter that can be used to change\ the display mode, alter the color, and include/exclude a subset of items\ within the track. This may be helpful when many items are shown in the track\ display, especially when only some are relevant to the current task.\
\ \\ To use the filter:\
\ This track may also be configured to display base labeling, a feature that\ allows the user to display all bases in the aligning sequence or only those\ that differ from the genomic sequence. For more information about this option,\ go to the\ \ Base Coloring for Alignment Tracks page.\ Several types of alignment gap may also be colored;\ for more information, go to the\ \ Alignment Insertion/Deletion Display Options page.\
\ \\ To make an EST, RNA is isolated from cells and reverse\ transcribed into cDNA. Typically, the cDNA is cloned\ into a plasmid vector and a read is taken from the 5'\ and/or 3' primer. For most — but not all — ESTs, the\ reverse transcription is primed by an oligo-dT, which\ hybridizes with the poly-A tail of mature mRNA. The\ reverse transcriptase may or may not make it to the 5'\ end of the mRNA, which may or may not be degraded.\
\ \\ In general, the 3' ESTs mark the end of transcription\ reasonably well, but the 5' ESTs may end at any point\ within the transcript. Some of the newer cap-selected\ libraries cover transcription start reasonably well. Before the\ cap-selection techniques\ emerged, some projects used random rather than poly-A\ priming in an attempt to retrieve sequence distant from the\ 3' end. These projects were successful at this, but as\ a side effect also deposited sequences from unprocessed\ mRNA and perhaps even genomic sequences into the EST databases.\ Even outside of the random-primed projects, there is a\ degree of non-mRNA contamination. Because of this, a\ single unspliced EST should be viewed with considerable\ skepticism.\
\ \\ To generate this track, S. cerevisiae ESTs from GenBank were aligned\ against the genome using blat. Note that the maximum intron length\ allowed by blat is 750,000 bases, which may eliminate some ESTs with very\ long introns that might otherwise align. When a single\ EST aligned in multiple places, the alignment having the\ highest base identity was identified. Only alignments having\ a base identity level within 0.5% of the best and at least 96% base identity\ with the genomic sequence are displayed in this track.\
\ \\ This track was produced at UCSC from EST sequence data\ submitted to the international public sequence databases by\ scientists worldwide.\
\ \\
Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW.\
\
GenBank.\
Nucleic Acids Res. 2013 Jan;41(Database issue):D36-42.\
PMID: 23193287; PMC:
\
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL.\
GenBank: update.\
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D23-6.\
PMID: 14681350; PMC:
\
Kent WJ.\
BLAT - the BLAST-like alignment tool.\
Genome Res. 2002 Apr;12(4):656-64.\
PMID: 11932250; PMC:
\ The orchestrated binding of transcriptional activators and repressors\ to specific DNA sequences in the context of chromatin defines the\ regulatory program of eukaryotic genomes. We developed a digital\ approach to assay regulatory protein occupancy on genomic DNA in vivo\ by dense mapping of individual DNase I cleavages from intact nuclei\ using massively parallel DNA sequencing. Analysis of >23 million\ cleavages across the Saccharomyces cerevisiae genome revealed\ thousands of protected regulatory protein footprints, enabling de\ novo derivation of factor binding motifs as well as the\ identification of hundreds of novel binding sites for major\ regulators. We observed striking correspondence between\ nucleotide-level DNase I cleavage patterns and protein-DNA\ interactions determined by crystallography. The data also yielded a\ detailed view of larger chromatin features including positioned\ nucleosomes flanking factor binding regions. Digital genomic\ footprinting provides a powerful approach to delineate the\ cis-regulatory framework of any organism with an available genome\ sequence.
\ \ \ \\ DNaseI-seq cleavage counts are displayed at nucleotide resolution,\ along with a 'mappability' track that indicates whether tag sequences\ starting at that location on both the forward and the reverse strands can be\ uniquely mapped to the yeast genome. Finally, the set of footprints\ with q values <0.1 are included, where the q value is\ defined as the minimal false discovery rate threshold at which the\ given footprint is deemed significant. The name associated with each\ footprint is its q value.
\ \\ To visualize regulatory protein occupancy across the genome of\ Saccharomyces cerevisiae, DNase I digestion of yeast nuclei was\ coupled with massively parallel DNA sequencing to create a dense\ whole-genome map of DNA template accessibility at the \ nucleotide-level.
\ \\ Yeast nuclei were isolated and treated with a DNase I concentration\ sufficient to release short (<300 bp) DNA fragments. Small\ fragments were derived from two DNase I "hits" in close proximity.\ Each end of those fragments represents an in vivo DNase I cleavage\ site. The sequence and hence genomic location of these sites were then\ determined by DNA sequencing.
\ \\ Footprints were identified using a computational algorithm that\ evaluates short regions (between 8 and 30 bp) over which the DNase I\ cleavage density was significantly reduced compared with the\ immediately flanking regions. FDR thresholds were assigned to each\ footprint by comparing p-values obtained from real and shuffled\ cleavage data.
\ \\ Detailed methods are given in Hesselberth et al. (2009), and\ supplementary data and source code are available\ here.
\ \\ This track was produced at the University of Washington by Jay\ R. Hesselberth, Xiaoyu Chen, Zhihong Zhang, Peter J. Sabo, Richard\ Sandstrom, Alex P. Reynolds, Robert E. Thurman, Shane Neph, Michael\ S. Kuehn, William S. Noble (william-noble@u.washington.edu), Stanley\ Fields (fields@u.washington.edu) and John A. Stamatoyannopoulos\ (jstam@stamlab.org).
\ \\
Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R, Reynolds AP, Thurman RE, Neph S, Kuehn MS,\
Noble WS et al.\
Global mapping of protein-DNA interactions in vivo by digital genomic footprinting.\
Nat Methods. 2009 Apr;6(4):283-9.\
PMID: 19305407; PMC: