\ regulation 1 exonArrows off\ group regulation\ longLabel Transcriptional Regulatory Code from Harbison Gordon et al.\ priority 92.5\ scoreFilter 500\ scoreFilterLimits 200:1000\ shortLabel Regulatory Code\ spectrum on\ track transRegCode\ type bed 5 +\ visibility hide\ transRegCodeProbe Reg. ChIP-chip bed 4 + ChIP-chip Results from Harbison Gordon et al. 0 92.6 0 0 0 127 127 127 0 0 0
Description
\ \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.\ \
Credits
\ \ The data for this track was provided by the Young and Fraenkel labs at\ MIT/Whitehead/Broad. The track was created by Jim Kent.\ \References
\ \ Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, MacIsaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J et al.\ Transcriptional regulatory code of a eukaryotic genome. \ Nature. 2004 Sep 2;431(7004):99-104.\ PMID: 15343339; PMC:Description
\\ This track shows the final assembly of the\ Saccharomyces cerevisiae S288c assembly (GCA_000146045.2) as of April 2011.\ of the S. cerevisiae genome. Please note the sequencing status at:\ SGD™.\
\ \\ Chromosomes available in this assembly: chrI, chrII, chrIII, chrIV ...\ etc ... chrXVI, and chrM.\ See also: SGD™ Genome Snapshot/Overview.\
\ \Credits
\\ The April 2011 Saccharomyces cerevisiae genome assembly \ is based on sequence from the\ NCBI R64-1-1 download directory.
\ map 1 altColor 230,170,40\ color 150,100,30\ group map\ html gold\ longLabel Assembly from Fragments\ shortLabel Assembly\ track gold\ type bed 3 +\ visibility hide\ augustusGene AUGUSTUS genePred AUGUSTUS ab initio gene predictions v3.1 0 100 12 105 0 133 180 127 0 0 0Description
\ \\ 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.
\ \Methods
\ \\ 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.\
\ \Credits
\ \ Thanks to the\ Stanke lab\ for providing the AUGUSTUS program. The training for the chicken version was\ done by Stefanie König and the training for the\ human and zebrafish versions was done by Mario Stanke.\ \References
\ \\ 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\ crispr CRISPR bed 3 CRISPR/Cas9 Sp. Pyog. target sites 0 100 0 0 0 127 127 127 0 0 0Description
\ \\ This track shows regions of the genome within 200 bp of transcribed regions and\ DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme\ from S. pyogenes (PAM: NGG).\ CRISPR target sites were annotated with predicted specificity\ (off-target effects) and predicted efficiency (on-target cleavage) by various\ algorithms through the tool CRISPOR.\
\ \Display Conventions and Configuration
\ \\ The track "CRISPR Regions" shows the regions of the genome where target\ sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by\ Ensembl transcript models.
\ \\ The track "CRISPR Targets" shows the target sites in these regions.\ The target sequence of the guide is shown with a thick (exon) bar. The PAM\ motif match (NGG) is shown with a thinner bar. Guides\ are colored to reflect both predicted specificity and efficiency. Specificity\ reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a\ sequence is, the more likely it is to cleave other locations of the genome\ (off-target effects). Efficiency is the frequency of cleavage at the target\ site (on-target efficiency).
\ \Shades of gray stand for sites that are hard to target specifically, as the\ 20mer is not very unique in the genome:
\| impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
\| unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
\ \
\
Mouse-over a target site to show predicted specificity and efficiency scores:
\
- \
- The MIT Specificity score summarizes all off-targets into a single number from\ 0-100. The higher the number, the fewer off-target effects are expected. We\ recommend guides with an MIT specificity > 50. \
- The efficiency score tries to predict if a guide leads to rather strong or\
weak cleavage. According to (Haeussler et al. 2016), the
- The
Click onto features to show all scores and predicted off-targets with up to\ four mismatches. The Out-of-Frame score by Bae et al. 2014\ is correlated with\ the probability that mutations induced by the guide RNA will disrupt the open\ reading frame. The authors recommend out-of-frame scores > 66 to create\ knock-outs with a single guide efficiently.
\ \
Off-target sites are sorted by the CFD (Cutting Frequency Determination) \ score (Doench et al. 2016). \ The higher the CFD score, the more likely there is off-target cleavage at that site. \ Off-targets with a CFD score < 0.023 are not shown on this page, but are availble when \ following the link to the external CRISPOR tool. \ When compared against experimentally validated off-targets by \ Haeussler et al. 2016, the large majority of predicted\ off-targets with CFD scores < 0.023 were false-positives.
\ \Methods
\ \Relationship between predictions and experimental data
\ \\ Like most algorithms, the MIT specificity score is not always a perfect\ predictor of off-target effects. Despite low scores, many tested guides \ caused few and/or weak off-target cleavage when tested with whole-genome assays\ (Figure 2 from Haeussler\ et al. 2016), as shown below, and the published data contains few data points\ with high specificity scores. Overall though, the assays showed that the higher\ the specificity score, the lower the off-target effects.
\ \
\
\
Similarly, efficiency scoring is not very accurate: guides with low\ scores can be efficient and vice versa. As a general rule, however, the higher\ the score, the less likely that a guide is very inefficient. The\ following histograms illustrate, for each type of score, how the share of\ inefficient guides drops with increasing efficiency scores:\
\ \
\
\
When reading this plot, keep in mind that both scores were evaluated on\ their own training data. Especially for the Moreno-Mateos score, the\ results are too optimistic, due to overfitting. When evaluated on independent\ datasets, the correlation of the prediction with other assays was around 25%\ lower, see Haeussler et al. 2016. At the time of\ writing, there is no independent dataset available yet to determine the\ Moreno-Mateos accuracy for each score percentile range.
\ \Track methods
\\
Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs\
on each side, searched for the -NGG motif. Flanking 20mer guide sequences were\
aligned to the genome with BWA and scored with MIT Specificity scores using the\
command-line version of crispor.org. Non-unique guide sequences were skipped.\
Flanking sequences were extracted from the genome and input for Crispor\
efficiency scoring, available from the
Data Access
\\ The raw data can be explored interactively with the Table Browser.\ For automated analysis, the genome annotation is stored in a bigBed file that\ can be downloaded from\ our download server.\ The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads server. Individual\ regions or the whole genome annotation can be obtained using our tool bigBedToBed,\ which can be compiled from the source code or downloaded as a precompiled\ binary for your system. Instructions for downloading source code and binaries can be found\ here. The tool\ can also be used to obtain only features within a given range, e.g. bigBedToBed\ http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crispr.bb -chrom=chr21\ -start=0 -end=10000000 stdout
\ \\ The file crisprDetails.tab includes the details of the off-targets. The last\ column of the bigBed file is the offset of the respective line in\ crisprDetails.tab. E.g. if the last column is 14227033723, then the following\ command will extract the line with the corresponding off-target details:\ curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table\ browser.
\ \\ The file crisprDetails.tab is a tab-separated text file with two fields. The\ first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" \ means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49\ off-targets at four mismatches. The second field is a pipe-separated list of\ semicolon-separated tuples with the genome coordinates and the CFD score. E.g.\ "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the\ first at chr1:123376795 on the positive strand and a CFD score 0.42
\ \Credits
\ \\ Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU).\
\ \References
\ \\
Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S,\
Shkumatava A, Teboul L, Kent J et al.\
Evaluation of off-target and on-target scoring algorithms and integration into the\
guide RNA selection tool CRISPOR.\
Genome Biol. 2016 Jul 5;17(1):148.\
PMID: 27380939; PMC:
\ Bae S, Kweon J, Kim HS, Kim JS.\ \ Microhomology-based choice of Cas9 nuclease target sites.\ Nat Methods. 2014 Jul;11(7):705-6.\ PMID: 24972169\
\ \\
Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C,\
Orchard R et al.\
\
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.\
Nat Biotechnol. 2016 Feb;34(2):184-91.\
PMID: 26780180; PMC:
\
Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O\
et al.\
\
DNA targeting specificity of RNA-guided Cas9 nucleases.\
Nat Biotechnol. 2013 Sep;31(9):827-32.\
PMID: 23873081; PMC:
\
Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ.\
\
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.\
Nat Methods. 2015 Oct;12(10):982-8.\
PMID: 26322839; PMC:
Description
\ \\ This track shows regions of the genome within 200 bp of transcribed regions and\ DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme\ from S. pyogenes (PAM: NGG).\ CRISPR target sites were annotated with predicted specificity\ (off-target effects) and predicted efficiency (on-target cleavage) by various\ algorithms through the tool CRISPOR.\
\ \Display Conventions and Configuration
\ \\ The track "CRISPR Regions" shows the regions of the genome where target\ sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by\ Ensembl transcript models.
\ \\ The track "CRISPR Targets" shows the target sites in these regions.\ The target sequence of the guide is shown with a thick (exon) bar. The PAM\ motif match (NGG) is shown with a thinner bar. Guides\ are colored to reflect both predicted specificity and efficiency. Specificity\ reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a\ sequence is, the more likely it is to cleave other locations of the genome\ (off-target effects). Efficiency is the frequency of cleavage at the target\ site (on-target efficiency).
\ \Shades of gray stand for sites that are hard to target specifically, as the\ 20mer is not very unique in the genome:
\| impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
\| unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
\ \
\
Mouse-over a target site to show predicted specificity and efficiency scores:
\
- \
- The MIT Specificity score summarizes all off-targets into a single number from\ 0-100. The higher the number, the fewer off-target effects are expected. We\ recommend guides with an MIT specificity > 50. \
- The efficiency score tries to predict if a guide leads to rather strong or\
weak cleavage. According to (Haeussler et al. 2016), the
- The
Click onto features to show all scores and predicted off-targets with up to\ four mismatches. The Out-of-Frame score by Bae et al. 2014\ is correlated with\ the probability that mutations induced by the guide RNA will disrupt the open\ reading frame. The authors recommend out-of-frame scores > 66 to create\ knock-outs with a single guide efficiently.
\ \
Off-target sites are sorted by the CFD (Cutting Frequency Determination) \ score (Doench et al. 2016). \ The higher the CFD score, the more likely there is off-target cleavage at that site. \ Off-targets with a CFD score < 0.023 are not shown on this page, but are availble when \ following the link to the external CRISPOR tool. \ When compared against experimentally validated off-targets by \ Haeussler et al. 2016, the large majority of predicted\ off-targets with CFD scores < 0.023 were false-positives.
\ \Methods
\ \Relationship between predictions and experimental data
\ \\ Like most algorithms, the MIT specificity score is not always a perfect\ predictor of off-target effects. Despite low scores, many tested guides \ caused few and/or weak off-target cleavage when tested with whole-genome assays\ (Figure 2 from Haeussler\ et al. 2016), as shown below, and the published data contains few data points\ with high specificity scores. Overall though, the assays showed that the higher\ the specificity score, the lower the off-target effects.
\ \\
\
Similarly, efficiency scoring is not very accurate: guides with low\ scores can be efficient and vice versa. As a general rule, however, the higher\ the score, the less likely that a guide is very inefficient. The\ following histograms illustrate, for each type of score, how the share of\ inefficient guides drops with increasing efficiency scores:\
\ \\
\
When reading this plot, keep in mind that both scores were evaluated on\ their own training data. Especially for the Moreno-Mateos score, the\ results are too optimistic, due to overfitting. When evaluated on independent\ datasets, the correlation of the prediction with other assays was around 25%\ lower, see Haeussler et al. 2016. At the time of\ writing, there is no independent dataset available yet to determine the\ Moreno-Mateos accuracy for each score percentile range.
\ \Track methods
\\
Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs\
on each side, searched for the -NGG motif. Flanking 20mer guide sequences were\
aligned to the genome with BWA and scored with MIT Specificity scores using the\
command-line version of crispor.org. Non-unique guide sequences were skipped.\
Flanking sequences were extracted from the genome and input for Crispor\
efficiency scoring, available from the
Data Access
\\ The raw data can be explored interactively with the Table Browser.\ For automated analysis, the genome annotation is stored in a bigBed file that\ can be downloaded from\ our download server.\ The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads server. Individual\ regions or the whole genome annotation can be obtained using our tool bigBedToBed,\ which can be compiled from the source code or downloaded as a precompiled\ binary for your system. Instructions for downloading source code and binaries can be found\ here. The tool\ can also be used to obtain only features within a given range, e.g. bigBedToBed\ http://hgdownload.soe.ucsc.edu/gbdb/hg19/crisprRanges/crispr.bb -chrom=chr21\ -start=0 -end=10000000 stdout
\ \\ The file crisprDetails.tab includes the details of the off-targets. The last\ column of the bigBed file is the offset of the respective line in\ crisprDetails.tab. E.g. if the last column is 14227033723, then the following\ command will extract the line with the corresponding off-target details:\ curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table\ browser.
\ \\ The file crisprDetails.tab is a tab-separated text file with two fields. The\ first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" \ means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49\ off-targets at four mismatches. The second field is a pipe-separated list of\ semicolon-separated tuples with the genome coordinates and the CFD score. E.g.\ "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the\ first at chr1:123376795 on the positive strand and a CFD score 0.42
\ \Credits
\ \\ Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU).\
\ \References
\ \\
Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S,\
Shkumatava A, Teboul L, Kent J et al.\
Evaluation of off-target and on-target scoring algorithms and integration into the\
guide RNA selection tool CRISPOR.\
Genome Biol. 2016 Jul 5;17(1):148.\
PMID: 27380939; PMC:
\ Bae S, Kweon J, Kim HS, Kim JS.\ \ Microhomology-based choice of Cas9 nuclease target sites.\ Nat Methods. 2014 Jul;11(7):705-6.\ PMID: 24972169\
\ \\
Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C,\
Orchard R et al.\
\
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.\
Nat Biotechnol. 2016 Feb;34(2):184-91.\
PMID: 26780180; PMC:
\
Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O\
et al.\
\
DNA targeting specificity of RNA-guided Cas9 nucleases.\
Nat Biotechnol. 2013 Sep;31(9):827-32.\
PMID: 23873081; PMC:
\
Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ.\
\
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.\
Nat Methods. 2015 Oct;12(10):982-8.\
PMID: 26322839; PMC:
Description
\ \\ This track shows regions of the genome within 200 bp of transcribed regions and\ DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme\ from S. pyogenes (PAM: NGG).\ CRISPR target sites were annotated with predicted specificity\ (off-target effects) and predicted efficiency (on-target cleavage) by various\ algorithms through the tool CRISPOR.\
\ \Display Conventions and Configuration
\ \\ The track "CRISPR Regions" shows the regions of the genome where target\ sites were analyzed, i.e. within 200 bp of transcribed regions as annotated by\ Ensembl transcript models.
\ \\ The track "CRISPR Targets" shows the target sites in these regions.\ The target sequence of the guide is shown with a thick (exon) bar. The PAM\ motif match (NGG) is shown with a thinner bar. Guides\ are colored to reflect both predicted specificity and efficiency. Specificity\ reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a\ sequence is, the more likely it is to cleave other locations of the genome\ (off-target effects). Efficiency is the frequency of cleavage at the target\ site (on-target efficiency).
\ \Shades of gray stand for sites that are hard to target specifically, as the\ 20mer is not very unique in the genome:
\| impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
\| unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
\ \
\
Mouse-over a target site to show predicted specificity and efficiency scores:
\
- \
- The MIT Specificity score summarizes all off-targets into a single number from\ 0-100. The higher the number, the fewer off-target effects are expected. We\ recommend guides with an MIT specificity > 50. \
- The efficiency score tries to predict if a guide leads to rather strong or\
weak cleavage. According to (Haeussler et al. 2016), the
- The
Click onto features to show all scores and predicted off-targets with up to\ four mismatches. The Out-of-Frame score by Bae et al. 2014\ is correlated with\ the probability that mutations induced by the guide RNA will disrupt the open\ reading frame. The authors recommend out-of-frame scores > 66 to create\ knock-outs with a single guide efficiently.
\ \
Off-target sites are sorted by the CFD (Cutting Frequency Determination) \ score (Doench et al. 2016). \ The higher the CFD score, the more likely there is off-target cleavage at that site. \ Off-targets with a CFD score < 0.023 are not shown on this page, but are availble when \ following the link to the external CRISPOR tool. \ When compared against experimentally validated off-targets by \ Haeussler et al. 2016, the large majority of predicted\ off-targets with CFD scores < 0.023 were false-positives.
\ \Methods
\ \Relationship between predictions and experimental data
\ \\ Like most algorithms, the MIT specificity score is not always a perfect\ predictor of off-target effects. Despite low scores, many tested guides \ caused few and/or weak off-target cleavage when tested with whole-genome assays\ (Figure 2 from Haeussler\ et al. 2016), as shown below, and the published data contains few data points\ with high specificity scores. Overall though, the assays showed that the higher\ the specificity score, the lower the off-target effects.
\ \\
\
Similarly, efficiency scoring is not very accurate: guides with low\ scores can be efficient and vice versa. As a general rule, however, the higher\ the score, the less likely that a guide is very inefficient. The\ following histograms illustrate, for each type of score, how the share of\ inefficient guides drops with increasing efficiency scores:\
\ \\
\
When reading this plot, keep in mind that both scores were evaluated on\ their own training data. Especially for the Moreno-Mateos score, the\ results are too optimistic, due to overfitting. When evaluated on independent\ datasets, the correlation of the prediction with other assays was around 25%\ lower, see Haeussler et al. 2016. At the time of\ writing, there is no independent dataset available yet to determine the\ Moreno-Mateos accuracy for each score percentile range.
\ \Track methods
\\
Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs\
on each side, searched for the -NGG motif. Flanking 20mer guide sequences were\
aligned to the genome with BWA and scored with MIT Specificity scores using the\
command-line version of crispor.org. Non-unique guide sequences were skipped.\
Flanking sequences were extracted from the genome and input for Crispor\
efficiency scoring, available from the
Data Access
\\ The raw data can be explored interactively with the Table Browser.\ For automated analysis, the genome annotation is stored in a bigBed file that\ can be downloaded from\ our download server.\ The files for this track are called crispr.bb and crisprDetails.tab and are located in the /gbdb/sacCer3/crispr directory of our downloads server. Individual\ regions or the whole genome annotation can be obtained using our tool bigBedToBed,\ which can be compiled from the source code or downloaded as a precompiled\ binary for your system. Instructions for downloading source code and binaries can be found\ here. The tool\ can also be used to obtain only features within a given range, e.g. bigBedToBed\ http://hgdownload.soe.ucsc.edu/gbdb/hg19/crisprTargets/crispr.bb -chrom=chr21\ -start=0 -end=10000000 stdout
\ \\ The file crisprDetails.tab includes the details of the off-targets. The last\ column of the bigBed file is the offset of the respective line in\ crisprDetails.tab. E.g. if the last column is 14227033723, then the following\ command will extract the line with the corresponding off-target details:\ curl -s -r 14227033723-14227043723 http://hgdownload.soe.ucsc.edu/gbdb/hg19/crispr/crisprDetails.tab | head -n1. The off-target details can currently not be joined with the table\ browser.
\ \\ The file crisprDetails.tab is a tab-separated text file with two fields. The\ first field contains the numbers of off-targets for each mismatch, e.g. "0,0,1,3,49" \ means 0 off-targets at zero mismatches, 1 at two mismatches, 3 at three and 49\ off-targets at four mismatches. The second field is a pipe-separated list of\ semicolon-separated tuples with the genome coordinates and the CFD score. E.g.\ "chr10;123376795+;42|chr5;148353274-;39" describes two off-targets, with the\ first at chr1:123376795 on the positive strand and a CFD score 0.42
\ \Credits
\ \\ Track created by Maximilian Haeussler and Hiram Clawson, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU).\
\ \References
\ \\
Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S,\
Shkumatava A, Teboul L, Kent J et al.\
Evaluation of off-target and on-target scoring algorithms and integration into the\
guide RNA selection tool CRISPOR.\
Genome Biol. 2016 Jul 5;17(1):148.\
PMID: 27380939; PMC:
\ Bae S, Kweon J, Kim HS, Kim JS.\ \ Microhomology-based choice of Cas9 nuclease target sites.\ Nat Methods. 2014 Jul;11(7):705-6.\ PMID: 24972169\
\ \\
Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C,\
Orchard R et al.\
\
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.\
Nat Biotechnol. 2016 Feb;34(2):184-91.\
PMID: 26780180; PMC:
\
Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O\
et al.\
\
DNA targeting specificity of RNA-guided Cas9 nucleases.\
Nat Biotechnol. 2013 Sep;31(9):827-32.\
PMID: 23873081; PMC:
\
Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ.\
\
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.\
Nat Methods. 2015 Oct;12(10):982-8.\
PMID: 26322839; PMC:
Description
\ \\ These gene predictions were generated by Ensembl.\
\ \\ For more information on the different gene tracks, see our Genes FAQ.
\ \Methods
\ \\ 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. \
\ \Data access
\\
Ensembl Gene data can be explored interactively using the\
Table Browser or the\
Data Integrator. \
For local downloads, the genePred format files for sacCer3 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.\
\ \Credits
\ \\ We would like to thank Ensembl for providing these gene annotations. For more information, please see\ Ensembl's genome annotation page.\
\ \References
\ \\
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:
Description
\\ There are no gaps in the S. cerevisiae assembly. \
\ \Credits
\\ The April 2011 Saccharomyces cerevisiae genome assembly \ is based on sequence from the:\ NCBI genbank/genomes/Eukaryotes/fungi/Saccharomyces_cerevisiae/R64-1-1/ download directory.
\ map 1 chromosomes none\ group map\ html gap\ longLabel Gap Locations\ shortLabel Gap\ track gap\ type bed 3 +\ visibility hide\ gc5BaseBw GC Percent bigWig 0 100 GC Percent in 5-Base Windows 0 100 0 0 0 128 128 128 0 0 0Description
\\ The GC percent track shows the percentage of G (guanine) and C (cytosine) bases\ in 5-base windows. High GC content is typically associated with\ gene-rich areas.\
\\ This track may be configured in a variety of ways to highlight different\ apsects of the displayed information. Click the\ "Graph configuration help"\ link for an explanation of the configuration options.\ \
Credits
\The data and presentation of this graph were prepared by\ Hiram Clawson.\
\ \ map 0 altColor 128,128,128\ autoScale Off\ color 0,0,0\ graphTypeDefault Bar\ gridDefault OFF\ group map\ html gc5Base\ longLabel GC Percent in 5-Base Windows\ maxHeightPixels 128:36:16\ shortLabel GC Percent\ track gc5BaseBw\ type bigWig 0 100\ viewLimits 30:70\ visibility hide\ windowingFunction Mean\ blastHg18KG Human Proteins psl protein Human Proteins Mapped by Chained tBLASTn 0 100 0 0 0 127 127 127 0 0 0Description
\\ This track contains tBLASTn alignments of the peptides from the predicted and \ known genes identified in the hg18 UCSC Genes track.
\ \Methods
\ 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.\ \Credits
\\ 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):403-10.\ PMID: 2231712\
\ \\ 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 hg18.blastKGRef04\ colorChromDefault off\ group genes\ longLabel Human Proteins Mapped by Chained tBLASTn\ pred hg18.blastKGPep04\ shortLabel Human Proteins\ track blastHg18KG\ type psl protein\ visibility hide\ microsat Microsatellite bed 4 Microsatellites - Di-nucleotide and Tri-nucleotide Repeats 0 100 0 0 0 127 127 127 0 0 0Description
\\ This track displays regions that are likely to be useful as microsatellite\ markers. These are sequences of at least 15 perfect di-nucleotide and \ tri-nucleotide repeats and tend to be highly polymorphic in the\ population.\
\ \Methods
\\ The data shown in this track are a subset of the Simple Repeats track, \ selecting only those \ repeats of period 2 and 3, with 100% identity and no indels and with\ at least 15 copies of the repeat. The Simple Repeats track is\ created using the \ Tandem Repeats Finder. For more information about this \ program, see Benson (1999).
\ \Credits
\\ Tandem Repeats Finder was written by \ Gary Benson.
\ \References
\ \\
Benson G.\
\
Tandem repeats finder: a program to analyze DNA sequences.\
Nucleic Acids Res. 1999 Jan 15;27(2):573-80.\
PMID: 9862982; PMC:
Description
\\ 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:
\
- \
- S. cerevisiae - https://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/\
- S. paradoxus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta\
- S. mikatae - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta\
- S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz\
- S. bayanus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Sbay_contigs.fasta\
- S. castelli - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM476.fsa.gz\
- S. kluyveri - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM479.fsa.gz\
Display Conventions and Configuration
\\ In full and pack display modes, conservation scores are displayed as a\ wiggle track (histogram) in which the height reflects the \ size of the score. \ The conservation wiggles can 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.
\\ Pairwise alignments of each species to the S. cerevisiae genome are \ displayed below the conservation histogram as a grayscale density plot (in \ pack mode) or as a wiggle (in full mode) that indicates alignment quality.\ In dense display mode, conservation is shown in grayscale using\ darker values to indicate higher levels of overall conservation \ as scored by phastCons.
\\ Checkboxes on the track configuration page allow selection of the\ species to include in the pairwise display. \ Configuration buttons are available to select all of the species (Set \ all), deselect all of the species (Clear all), or \ use the default settings (Set defaults).\ Note that excluding species from the pairwise display does not alter the\ the conservation score display.
\\ To view detailed information about the alignments at a specific\ position, zoom the display in to 30,000 bases or fewer, then click on\ the alignment.
\ \Gap Annotation
\\ The Display chains between alignments configuration option \ enables display of gaps between alignment blocks in the pairwise alignments in \ a manner similar to the Chain track display. The following\ conventions are used:\
- \
- Single line: No bases in the aligned species. Possibly due to a\ lineage-specific insertion between the aligned blocks in the S. cerevisiae genome\ or a lineage-specific deletion between the aligned blocks in the aligning\ species.\
- Double line: Aligning species has one or more unalignable bases in\ the gap region. Possibly due to excessive evolutionary distance between \ species or independent indels in the region between the aligned blocks in both\ species. \
- Pale yellow coloring: Aligning species has Ns in the gap region.\ Reflects uncertainty in the relationship between the DNA of both species, due\ to lack of sequence in relevant portions of the aligning species. \
- \
- \ Multiz alignments (MAF format), and phylogenetic trees\
- \ PhastCons conservation (WIG format)\
Base Level
\\ When zoomed-in to the base-level display, 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 the space is insufficient and the gap size is a multiple of 3, a \ "*" is displayed; other gap sizes are indicated by "+".
\\ Codon translation is available in base-level display mode if the\ displayed region is identified as a coding segment. To display this annotation,\ select the species for translation from the pull-down menu in the Codon\ Translation configuration section at the top of the page. Then, select one of\ the following modes:\
- \
- \ No codon translation: The gene annotation is not used; the bases are\ displayed without translation. \
- \ Use default species reading frames for translation: The annotations from the genome\ displayed \ in the Default species to establish reading frame pull-down menu are used to\ translate all the aligned species present in the alignment. \
- \ Use reading frames for species if available, otherwise no translation: Codon\ translation is performed only for those species where the region is\ annotated as protein coding.\
- Use reading frames for species if available, otherwise use default species:\ Codon translation is done on those species that are annotated as being protein\ coding over the aligned region using species-specific annotation; the remaining\ species are translated using the default species annotation. \
\ Codon translation uses the following gene tracks as the basis for\ translation, depending on the species chosen (Table 2). \ Species listed in the row labeled "None" do not have \ species-specific reading frames for gene translation.\ \
\\ \\
\ Table 2. Gene tracks used for codon translation.\\ Gene Track Species \ SGD Genes S. cerevisae \ No annotation all the other yeast strains
Methods
\\ Best-in-genome pairwise alignments were generated for each species \ using lastz, 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.
\ \Credits
\\ This track was created at UCSC using the following programs:\
- \
- \ Lastz (formerly Blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the \ Penn State Bioinformatics \ Group. \
- \ AxtBest, axtChain, chainNet, netSyntenic, and netClass \ by Jim Kent at UCSC. \
- PhastCons by Adam Siepel at Cornell University. \
- "Wiggle track" plotting software by Hiram Clawson at UCSC.\
The phylogenetic tree is based on the\ Saccharomyces Phylogeny page from the Department\ of Genetics at Washington University in St. Louis.\ \
References
\ \Phylo-HMMs and phastCons:
\\ 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, 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:
\ Siepel A, Haussler D.\ Phylogenetic Hidden Markov Models.\ In: Nielsen R, editor. Statistical Methods in Molecular Evolution.\ New York: Springer; 2005. pp. 325-351.\
\ \\
Yang Z.\
A space-time process model for the evolution of DNA\
sequences.\
Genetics. 1995 Feb;139(2):993-1005.\
PMID: 7713447; PMC:
Chain/Net:
\\
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:
Multiz:
\\
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:
Lastz (formerly Blastz):
\\ Chiaromonte F, Yap VB, Miller W.\ Scoring pairwise genomic sequence alignments.\ Pac Symp Biocomput. 2002:115-26.\ PMID: 11928468\
\ \\ Harris RS.\ Improved pairwise alignment of genomic DNA.\ Ph.D. Thesis. Pennsylvania State University, USA. 2007.\
\ \ compGeno 1 altColor 0,90,10\ color 0, 10, 100\ frames multiz7wayFrames\ group compGeno\ irows on\ itemFirstCharCase noChange\ longLabel Multiz Alignments of 7 Yeasts\ noInherit on\ parent cons7wayViewalign on\ priority 100\ shortLabel Multiz Align\ speciesCodonDefault sacSer3\ speciesOrder sacPar sacMik sacKud sacBay sacCas sacKlu\ subGroups view=align\ summary multiz7waySummary\ track multiz7way\ treeImage phylo/sacCer2_7way.gif\ type wigMaf 0.0 1.0\ oreganno ORegAnno bed 4 + Regulatory elements from ORegAnno 0 100 102 102 0 178 178 127 0 0 0Description
\\ 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. \ \
\ \Display Conventions and Configuration
\ \The display may be filtered to show only selected region types, such as:
\ \- \
- regulatory regions (shown in light blue) \
- regulatory polymorphisms (shown in dark blue) \
- transcription factor binding sites (shown in orange) \
- regulatory haplotypes (shown in red) \
- miRNA binding sites (shown in blue-green) \
To exclude a region type, uncheck the appropriate box in the list at the top of \ the Track Settings page.
\ \Methods
\\ 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:\
- \
- A stable ORegAnno identifier.\
- A valid taxonomy ID from the NCBI taxonomy database.\
- A valid PubMed reference. \
- A target gene that is either user-defined, in Entrez Gene or in EnsEMBL.\
- A sequence with at least 40 flanking bases (preferably more) to allow the\ site to be mapped to any release of an associated genome.\
- At least one piece of specific experimental evidence, including the\ biological technique used to discover the regulatory sequence. (Currently\ only the evidence subtypes are supplied with the UCSC track.)\
- A positive, neutral or negative outcome based on the experimental results\ from the primary reference. (Only records with a positive outcome are currently\ included in the UCSC track.)\
- \
- A transcription factor that is either user-defined, in Entrez Gene\ or in EnsEMBL.\
- A specific cell type for each piece of experimental evidence, using the\ eVOC cell type ontology.\
- A specific dataset identifier (e.g. the REDfly dataset) that allows\ external curators to manage particular annotation sets using ORegAnno's\ curation tools.\
- A "search space" sequence that specifies the region that was\ assayed, not just the regulatory sequence. \
- A dbSNP identifier and type of variant (germline, somatic or artificial)\ for regulatory polymorphisms.\
Credits
\\ 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.\ \
References
\\
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\ pennantIcon 1.jpg ../goldenPath/help/liftOver.html "lifted from sacCer1"\ 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 0Description
\\ 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.
\ \Display Conventions and Configuration
\\ 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. \
- \
- Label: By default, items are labeled by gene name. Click the \ appropriate Label option to display the accession name instead of the gene\ name, show both the gene and accession names, or turn off the label \ completely.\
- Codon coloring: This track contains an optional codon coloring \ feature that allows users to quickly validate and compare gene predictions.\ To display codon colors, select the genomic codons option from the\ Color track by codons pull-down menu. For more information about\ this feature, go to the\ \ Coloring Gene Predictions and Annotations by Codon page.\
- Hide non-coding genes: By default, both the protein-coding and\ non-protein-coding genes are displayed. If you wish to see only the coding\ genes, click this box.\
Methods
\\ 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.\
\ \Credits
\\ This track was produced at UCSC from RNA sequence data\ generated by scientists worldwide and curated by the \ NCBI RefSeq project.
\ \References
\\
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:
Description
\\ 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.\ \
Methods
\\ 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. \ \
Credits
\\ Thanks to Eran Segal for providing the data analysis that forms the \ basis for this track. The display was programmed by \ Jim Kent.\ \
References
\\ 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 0Description
\ \\ 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.\
\ \Display Conventions and Configuration
\ \\ 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:\
- \
- Type a term in one or more of the text boxes to filter the EST\ display. For example, to apply the filter to all ESTs expressed in a specific\ organ, type the name of the organ in the tissue box. To view the list of\ valid terms for each text box, consult the table in the Table Browser that\ corresponds to the factor on which you wish to filter. For example, the\ "tissue" table contains all the types of tissues that can be\ entered into the tissue text box. Multiple terms may be entered at once,\ separated by a space. Wildcards may also be used in the filter. \
- If filtering on more than one value, choose the desired combination\ logic. If "and" is selected, only ESTs that match all filter\ criteria will be highlighted. If "or" is selected, ESTs that\ match any one of the filter criteria will be highlighted. \
- Choose the color or display characteristic that should be used to\ highlight or include/exclude the filtered items. If "exclude" is\ chosen, the browser will not display ESTs that match the filter criteria.\ If "include" is selected, the browser will display only those\ ESTs that match the filter criteria. \
\ 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.\
\ \Methods
\ \\ 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.\
\ \Credits
\ \\ This track was produced at UCSC from EST sequence data\ submitted to the international public sequence databases by\ scientists worldwide.\
\ \References
\\
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:
Description
\\ The mRNA track shows alignments between S. cerevisiae mRNAs\ in GenBank and the genome.
\ \Display Conventions and Configuration
\\ 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:\
- \
- Type a term in one or more of the text boxes to filter the mRNA \ display. For example, to apply the filter to all mRNAs submitted by a specific\ author, type the name of the individual in the author box. To view the list of\ valid terms for each text box, consult the table in the Table Browser that \ corresponds to the factor on which you wish to filter. For example, the \ "author" table contains the names of all individuals who can be \ entered into the author text box. Wildcards may also be used in the\ filter.\
- If filtering on more than one value, choose the desired combination\ logic. If "and" is selected, only mRNAs that match all filter \ criteria will be displayed. If "or" is selected, only mRNAs that \ match any one of the filter criteria will be displayed.\
- Choose the color or display characteristic that should be used to \ highlight or include/exclude the filtered items. If "exclude" is \ chosen, the browser will not display mRNAs that match the filter criteria. \ If "include" is selected, the browser will display only those \ mRNAs that match the filter criteria.\
\ 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.\
\ \Methods
\\ 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.\
\ \Credits
\\ The mRNA track was produced at UCSC from mRNA sequence data\ submitted to the international public sequence databases by \ scientists worldwide.
\ \References
\\
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:
Description
\\ 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.
\ \Methods
\\ For more information about the TRF program, see Benson (1999).\
\ \Credits
\\ TRF was written by \ Gary Benson.
\ \References
\ \\
Benson G.\
\
Tandem repeats finder: a program to analyze DNA sequences.\
Nucleic Acids Res. 1999 Jan 15;27(2):573-80.\
PMID: 9862982; PMC:
Description
\ \\ 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.\
\ \Display Conventions and Configuration
\ \\ 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:\
- \
- Type a term in one or more of the text boxes to filter the EST\ display. For example, to apply the filter to all ESTs expressed in a specific\ organ, type the name of the organ in the tissue box. To view the list of\ valid terms for each text box, consult the table in the Table Browser that\ corresponds to the factor on which you wish to filter. For example, the\ "tissue" table contains all the types of tissues that can be\ entered into the tissue text box. Multiple terms may be entered at once,\ separated by a space. Wildcards may also be used in the filter. \
- If filtering on more than one value, choose the desired combination\ logic. If "and" is selected, only ESTs that match all filter\ criteria will be highlighted. If "or" is selected, ESTs that\ match any one of the filter criteria will be highlighted. \
- Choose the color or display characteristic that should be used to\ highlight or include/exclude the filtered items. If "exclude" is\ chosen, the browser will not display ESTs that match the filter criteria.\ If "include" is selected, the browser will display only those\ ESTs that match the filter criteria. \
\ 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.\
\ \Methods
\ \\ 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.\
\ \Credits
\ \\ This track was produced at UCSC from EST sequence data\ submitted to the international public sequence databases by\ scientists worldwide.\
\ \References
\\
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:
Description
\\ 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:
\
- \
- S. cerevisiae - https://downloads.yeastgenome.org/sequence/S288C_reference/genome_releases/\
- S. paradoxus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Spar_contigs.fasta\
- S. mikatae - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Smik_contigs.fasta\
- S. kudriavzevii - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM6553.fsa.gz\
- S. bayanus - ftp://ftp.broadinstitute.org/pub/annotation/fungi/comp_yeasts/S1a.Assembly/Sbay_contigs.fasta\
- S. castelli - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM476.fsa.gz\
- S. kluyveri - http://www.genetics.wustl.edu/saccharomycesgenomes/Contigs/YM479.fsa.gz\
Display Conventions and Configuration
\\ In full and pack display modes, conservation scores are displayed as a\ wiggle track (histogram) in which the height reflects the \ size of the score. \ The conservation wiggles can 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.
\\ Pairwise alignments of each species to the S. cerevisiae genome are \ displayed below the conservation histogram as a grayscale density plot (in \ pack mode) or as a wiggle (in full mode) that indicates alignment quality.\ In dense display mode, conservation is shown in grayscale using\ darker values to indicate higher levels of overall conservation \ as scored by phastCons.
\\ Checkboxes on the track configuration page allow selection of the\ species to include in the pairwise display. \ Configuration buttons are available to select all of the species (Set \ all), deselect all of the species (Clear all), or \ use the default settings (Set defaults).\ Note that excluding species from the pairwise display does not alter the\ the conservation score display.
\\ To view detailed information about the alignments at a specific\ position, zoom the display in to 30,000 bases or fewer, then click on\ the alignment.
\ \Gap Annotation
\\ The Display chains between alignments configuration option \ enables display of gaps between alignment blocks in the pairwise alignments in \ a manner similar to the Chain track display. The following\ conventions are used:\
- \
- Single line: No bases in the aligned species. Possibly due to a\ lineage-specific insertion between the aligned blocks in the S. cerevisiae genome\ or a lineage-specific deletion between the aligned blocks in the aligning\ species.\
- Double line: Aligning species has one or more unalignable bases in\ the gap region. Possibly due to excessive evolutionary distance between \ species or independent indels in the region between the aligned blocks in both\ species. \
- Pale yellow coloring: Aligning species has Ns in the gap region.\ Reflects uncertainty in the relationship between the DNA of both species, due\ to lack of sequence in relevant portions of the aligning species. \
- \
- \ Multiz alignments (MAF format), and phylogenetic trees\
- \ PhastCons conservation (WIG format)\
Base Level
\\ When zoomed-in to the base-level display, 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 the space is insufficient and the gap size is a multiple of 3, a \ "*" is displayed; other gap sizes are indicated by "+".
\\ Codon translation is available in base-level display mode if the\ displayed region is identified as a coding segment. To display this annotation,\ select the species for translation from the pull-down menu in the Codon\ Translation configuration section at the top of the page. Then, select one of\ the following modes:\
- \
- \ No codon translation: The gene annotation is not used; the bases are\ displayed without translation. \
- \ Use default species reading frames for translation: The annotations from the genome\ displayed \ in the Default species to establish reading frame pull-down menu are used to\ translate all the aligned species present in the alignment. \
- \ Use reading frames for species if available, otherwise no translation: Codon\ translation is performed only for those species where the region is\ annotated as protein coding.\
- Use reading frames for species if available, otherwise use default species:\ Codon translation is done on those species that are annotated as being protein\ coding over the aligned region using species-specific annotation; the remaining\ species are translated using the default species annotation. \
\ Codon translation uses the following gene tracks as the basis for\ translation, depending on the species chosen (Table 2). \ Species listed in the row labeled "None" do not have \ species-specific reading frames for gene translation.\ \
\\ \\
\ Table 2. Gene tracks used for codon translation.\\ Gene Track Species \ SGD Genes S. cerevisae \ No annotation all the other yeast strains
Methods
\\ Best-in-genome pairwise alignments were generated for each species \ using lastz, 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.
\ \Credits
\\ This track was created at UCSC using the following programs:\
- \
- \ Lastz (formerly Blastz) and multiz by Minmei Hou, Scott Schwartz and Webb Miller of the \ Penn State Bioinformatics \ Group. \
- \ AxtBest, axtChain, chainNet, netSyntenic, and netClass \ by Jim Kent at UCSC. \
- PhastCons by Adam Siepel at Cornell University. \
- "Wiggle track" plotting software by Hiram Clawson at UCSC.\
The phylogenetic tree is based on the\ Saccharomyces Phylogeny page from the Department\ of Genetics at Washington University in St. Louis.\ \
References
\ \Phylo-HMMs and phastCons:
\\ 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, 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:
\ Siepel A, Haussler D.\ Phylogenetic Hidden Markov Models.\ In: Nielsen R, editor. Statistical Methods in Molecular Evolution.\ New York: Springer; 2005. pp. 325-351.\
\ \\
Yang Z.\
A space-time process model for the evolution of DNA\
sequences.\
Genetics. 1995 Feb;139(2):993-1005.\
PMID: 7713447; PMC:
Chain/Net:
\\
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:
Multiz:
\\
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:
Lastz (formerly Blastz):
\\ Chiaromonte F, Yap VB, Miller W.\ Scoring pairwise genomic sequence alignments.\ Pac Symp Biocomput. 2002:115-26.\ PMID: 11928468\
\ \\ Harris RS.\ Improved pairwise alignment of genomic DNA.\ Ph.D. Thesis. Pennsylvania State University, USA. 2007.\
\ \ compGeno 1 compositeTrack on\ dragAndDrop subTracks\ group compGeno\ html multiz7way\ longLabel Multiz Alignment & Conservation (7 Yeasts)\ priority 103.29\ shortLabel Conservation\ subGroup1 view Views align=Multiz_Alignments phyloP=Basewise_Conservation_(phyloP) phastcons=Element_Conservation_(phastCons) elements=Conserved_Elements\ track cons7way\ type bed 4\ visibility full\ cons7wayViewelements Conserved Elements bed 4 Multiz Alignment & Conservation (7 Yeasts) 1 103.29 0 0 0 127 127 127 0 0 0 compGeno 1 longLabel Multiz Alignment & Conservation (7 Yeasts)\ parent cons7way\ shortLabel Conserved Elements\ track cons7wayViewelements\ view elements\ visibility dense\ cons7wayViewphastcons Element Conservation (phastCons) bed 4 Multiz Alignment & Conservation (7 Yeasts) 2 103.29 0 0 0 127 127 127 0 0 0 compGeno 1 longLabel Multiz Alignment & Conservation (7 Yeasts)\ parent cons7way\ shortLabel Element Conservation (phastCons)\ track cons7wayViewphastcons\ view phastcons\ visibility full\ cons7wayViewalign Multiz Alignments bed 4 Multiz Alignment & Conservation (7 Yeasts) 3 103.29 0 0 0 127 127 127 0 0 0 compGeno 1 longLabel Multiz Alignment & Conservation (7 Yeasts)\ parent cons7way\ shortLabel Multiz Alignments\ track cons7wayViewalign\ view align\ viewUi on\ visibility pack\