Zara Tindall gets impressive new job and it's a royal first
Zara Tindall will start her new role at the beginning of next year, and it's something shes very, very passionate about
by Zoe Forsey Lifestyle Editor, Carl EveAs a non-working member of the Royal Family, Zara Tindall has been able to carve her own path in life - including a very, very impressive career.
The equestrian champion has a number of achievements to her name, including a silver medal at the London 2012 Olympics, when her proud family cheered her on.
And now the Queen's eldest grandaughter is set to have yet another feather in her cap as she prepares to become a director at Cheltenham Racecourse.
She is believed to be the first member of the royal family to take up such a role, and she's looking forward to using the opportunity to get involved.
Zara said: "I’m passionate about horseracing, particularly on the Jumps side, and the absolute pinnacle of that is Cheltenham.
"Racing is simply the most exciting sport and it’s open to all.
"It’s an honour to have been asked by Martin St Quinton on behalf of The Jockey Club to get involved in a more formal capacity and I look forward to doing my bit to support the executive team in the years to come."
Zara, who is a regular at the racecourse, will officially take up the non-executive role on Home of Jump Racing on New Year’s Day – its first fixture of 2020.
She will be tasked with supporting the chairman and executive in the running of the racecourse, which is part of The Jockey Club.
As well as competing, Zara has owned, bred and pre-trained several racehorses, just as her grandmother the Queen has done.
Martin St Quinton, chairman of the Cheltenham Racecourse Committee, added: " Zara is a wonderful advocate for our sport and I’m delighted that she has agreed to join us at Cheltenham Racecourse.
"I’m sure we will benefit significantly from her breadth of experience and a perspective that will prove invaluable to the racecourse.
Zara, who is mum to Mia and Lena, still regularly competes in competitions and suffered a nasty fall at the Burghley Horse Trials earlier this year.
She fell off her 10-year-old horse during the annual event.
Husband Mike Tindall is almost always in the crowds cheering her on along with the couple's two children, who always make the most of the fun summer events.


QPlaceAttribute Class
The QPlaceAttribute class represents generic attribute information about a place. More...
| Header: | #include <QPlaceAttribute> |
| qmake: | QT += location |
| Since: | Qt 5.6 |
Public Functions
| QPlaceAttribute() | |
| QPlaceAttribute(const QPlaceAttribute &other) | |
| virtual | ~QPlaceAttribute() |
| bool | isEmpty() const |
| QString | label() const |
| void | setLabel(const QString &label) |
| void | setText(const QString &text) |
| QString | text() const |
| bool | operator!=(const QPlaceAttribute &other) const |
| QPlaceAttribute & | operator=(const QPlaceAttribute &other) |
| bool | operator==(const QPlaceAttribute &other) const |
Static Public Members
| const QString | OpeningHours |
| const QString | Payment |
| const QString | Provider |
Detailed Description
The QPlaceAttribute class represents generic attribute information about a place.
A QPlaceAttribute instance stores an additional piece of information about a place that is not otherwise exposed through the QPlace class. A QPlaceAttribute encapsulates a localized label which describes the attribute and rich text string representing the attribute's value. Generally, both are intended to be displayed to the end-user as is.
Some plugins may not support attributes at all, others may only support a certain set, others still may support a dynamically changing set of attributes over time or even allow attributes to be arbitrarily defined by the client application. The attributes could also vary on a place by place basis, for example one place may have opening hours while another does not. Consult the plugin references for details.
Attribute Types
The QPlaceAttribute class defines some constant strings which characterize standard attribute types.
There is a class of attribute types of the format x_id_<provider> for example x_id_here. This class of attributes is a set of alternative identifiers of the place, from the specified provider's perspective.
The above types are used to access and modify attributes in QPlace via:
- QPlace::extendedAttribute()
- QPlace::setExtendedAttribute()
- QPlace::removeExtendedAttribute()
- QPlace::removeExtendedAttribute()
The attribute type is a string type so that providers are able to introduce new attributes as necessary. Custom attribute types should always be prefixed by a qualifier in order to avoid conflicts.
User Readable and Non-User Readable Attributes
Some attributes may not be intended to be readable by end users, the label field of such attributes are empty to indicate this fact.
Member Function Documentation
QPlaceAttribute::QPlaceAttribute()
Constructs an attribute.
QPlaceAttribute::QPlaceAttribute(const QPlaceAttribute &other)
Creates a copy of other.
[virtual]
QPlaceAttribute::~QPlaceAttribute()
Destroys the attribute.
bool QPlaceAttribute::isEmpty() const
Returns a boolean indicating whether the all the fields of the place attribute are empty or not.
QString QPlaceAttribute::label() const
Returns a localized label describing the attribute.
See also setLabel().
void QPlaceAttribute::setLabel(const QString &label)
Sets the label of the attribute.
See also label().
void QPlaceAttribute::setText(const QString &text)
Sets the text of the attribute.
See also text().
QString QPlaceAttribute::text() const
Returns a piece of rich text representing the attribute value.
See also setText().
bool QPlaceAttribute::operator!=(const QPlaceAttribute &other) const
Returns true if other is not equal to this attribute, otherwise returns false.
QPlaceAttribute &QPlaceAttribute::operator=(const QPlaceAttribute &other)
Assigns other to this attribute and returns a reference to this attribute.
bool QPlaceAttribute::operator==(const QPlaceAttribute &other) const
Returns true if other is equal to this attribute, otherwise returns false.
Member Variable Documentation
const QString QPlaceAttribute::OpeningHours
Specifies the opening hours.
const QString QPlaceAttribute::Payment
The constant to specify an attribute that defines the methods of payment.
const QString QPlaceAttribute::Provider
The constant to specify an attribute that defines which provider the place came from.


I came across a link today that would allow you to post “pretty” code in any HTML page. I’ve had plenty of problems (as have others) posting well-formatted source code to my Blogger-based blog. I’ve wanted to correct this problem for some time, and this project appears to be a decent solution. Here’s an example:
function chooseWinner() {

 $('p3').removeClassName('winner');

 participantBo.chooseWinnerForEvent(eventId, function(data) {

 people = $A(data);

 updateDivs();

 intervalId = setInterval(doShift, 500);

 });

}
Now that isn’t too bad is it? There seems to be a bit of a delay as the Javascript is loaded, but I think that’s more a symptom of my connection than it is a problem with the script.
Here’s how you can take advantage of this solution (quoted from http://google-code-prettify.googlecode.com/svn/trunk/README.html):
Download a distribution
Include the script and stylesheets in your document
(you will need to make sure the css and js file are on your server, and
adjust the paths in the script and link tag)
<link href="prettify.css" type="text/css" rel="stylesheet" />

Add
onload="prettyPrint()"
to your
document’s body tag.Modify the stylesheet to get the coloring you prefer
Put code snippets in
...
or...
and it will automatically be pretty printed.


They are at the beginning of learning how to get on with others, and better control their feelings and emotions. It’s exciting to see more and more of the person they will become!


Conditional elements in HTML
Tab 1
Tab 2
Tab 3
Trigget Default
Tab 1 is selected
Tab 2 is selected
Tab 3 is selected
No known tab selected


simSequence
Similarity between 2 sequences (e.g., for beat tracking)
Contents
Syntax
- f=simSequence(computed, gt, tolerance)
- f=simSequence(computed, gt, tolerance, plotOpt)
- [f, p, r, accuracy, tpCount, fpCount, fnCount]=simSequence(...)
Description
This function is often used to evaluate the similarity between two time sequences, for instance, beat positions generated from beat tracking of audio music.
f=simSequence(computed, gt, tolerance) returns the F-measure of two sequences (computed, gt) with the given tolerance.
f=simSequence(computed, gt, tolerance, 1) also plots the sequences for visualization.
[f, p, r, accuracy, tpCount, fpCount, fnCount]=simSequence(...) returns more info:
- f: F-measure, equal to 2pr/(p+r) = tp/(tp+(fn+fp)/2)
- p: precision, equal to tp/(tp+fp)
- r: recall, equal to tp/(fp+fn)
- accuracy: equal to tp/(fp+fn+tp)
- tpCount: true-positive (hit) count
- fpCount: false-positive (insertion) count
- fnCount: false-negative (deletion) count
Example
gt=[1 2 3 4 5]; computed=[0.5 1.86 2.1 3.1 4.5 5 6]; tolerance=0.15; [f, p, r, accuracy, tp, fp, fn]=simSequence(computed, gt, tolerance, 1); fprintf('Precision = tp/(tp+fp)=%d/(%d+%d) = %f\n', tp, tp, fp, p); fprintf('Recall = tp/(tp+fn)=%d/(%d+%d) = %f\n', tp, tp, fn, r); fprintf('F-measure = tp/(tp+(fn+fp)/2)=%d/(%d+(%d+%d)/2) = %f\n', tp, tp, fn, fp, f); fprintf('Accuracy = tp/(tp+fn+fp)=%d/(%d+%d+%d) = %f\n', tp, tp, fn, fp, accuracy);
Precision = tp/(tp+fp)=3/(3+4) = 0.428571 Recall = tp/(tp+fn)=3/(3+2) = 0.600000 F-measure = tp/(tp+(fn+fp)/2)=3/(3+(2+4)/2) = 0.500000 Accuracy = tp/(tp+fn+fp)=3/(3+2+4) = 0.333333


It is a full scholarship based Accelerator program for Coding in Jaffna, Sri Lanka targeting students who has completed Advanced Level examinations and don’t have access to university education or any other form of vocational education. It is aimed to provide the necessary training to enter Computer Software industry or to start an IT startup. This is a joint initiative by Yarl IT Hub and SERVE Foundation. Yarl IT Hub has been functioning for the last five years as a not for profit social enterprise focusing on technology entrepreneurship in northern Sri Lanka and SERVE Foundation is a four year old foundation focusing on using technology to uplift secondary education standards in rural schools in northern Sri Lanka.
CourseOur Vision is to be a leading center of excellence in teaching, learning, research and scholarship.
|
CODING ACCELERATOR PROGRAMThis Coding Accelerator program is an intensive training on full-stack development with server-side Node.js and modern, client-side, front-end libraries and frameworks.Throughout this intensive course the student will study professional software development techniques and practices as he/she advances his/her skills on JavaScript. |
|
| 2017 ©Uki All Rights Reserved. A joint initiative by Yarl IT Hub and SERVE Foundation |


AggregatePlotter
AnnotateRegions
Bar2Gr
CelFileConverter
CelFileQualityControl
CelMasker
CelProcessor
CoordinateExtractor1lq
ConvertAgilentData
ConvertGeoData
ConvertNimblegenNDF2TPMap
ConvertNimblegenPAIR2Cela
CorrelationMaps
Correlate
BestWindowScoreExtractor
ExportIntergenicRegions
ExportIntronicRegions
FDRWindowConverter
FetchGenomicSequences
FindNeighboringGenes
FileCrossFilter
FileJoiner
FileSplitter
FilterTPMapByRegions
FindSubBindingRegions
Gr2Bar
HierarchicalClustering
IndexFastas
IntensityPrinter
IntersectLists
IntersectRegions
IntervalFilter
IntervalGFFPrinter
IntervalGraphPrinter
IntervalMaker
IntervalPlotter
IntervalReportPrinter
JQSub
LoadChipSetIntervalOligoInfo
LoadIntervalOligoInfo
MakeChromosomeSets
MergeWindowArrays
MultiWindowIntervalMaker
MummerMapper
OligoIntensityPrinter
OligoTiler
OverlapCounter
ScoreParsedBars
Primer3Wrapper
PrintSelectColumns
RankedSetAnalysis
ScanChip
ScanChromosomes
ScanGenes
ScatterPlot
ScoreChromosomes
ScoreIntervals
ScoreSequences
SetNumberIntervalMaker
Sgr2Bar
SynonymMatching
T2
TPMapOligoBlastFilter
TPMapProcessor
TPMapSort
VirtualCel
Windows2HeatMapSgr
**************************************************************************************
** Aggregate Plotter: Feb 2009 **
**************************************************************************************
Fetches point data contained within each region, inverts - stranded annotation, zeros
the coordinates, scales, sums, and window averages the values. Usefull for generating
class averages from a list of annotated regions. Use a spreadsheet app to graph the
results.
Options:
-t PointData directories, full path, comma delimited. These should contain chromosome
specific xxx.bar.zip files.
-b Bed file (chr, start, stop, name, score, strand(+/-/.), full path, containing
regions to stack.
-p Peak shift, average distance between + and - strand peaks. Will be used to shift
the PointData by 1/2 the peak shift, defaults to 0.
-u Strand usage, defaults to 0 (combine), 1 (use only same strand), 2 (opposite
strand), or 3 (ignore).
this option to select particular stranded data to aggregate.
-r Replace scores with 1. Useful for raw PointData bar files.
-d Delog2 scores. Do it if your data is in log2 space.
-v Convert each region scores to % of region total.
-s Scale all regions to a particular size. Defaults to max region size.
Example: java -Xmx1500M -jar pathTo/USeq/Apps/AgregatePlotter -t
/Data/PolIIRep1/,/Data/PolIIRep2/ -b /Anno/tssSites.bed -p 73 -u 1
**************************************************************************************
**************************************************************************************
** Annotate Regions: Jan 2005 **
**************************************************************************************
Annotates a picks file finding surrounding protein coding genes.
-g Full path file name for the DmelRel4.0 GFF3 file.
-p Full path file name for the binding region picks or Interval file.
-c Full path file name for the Filtered CG names file (optional)
-b Size of neighborhood in bp, default is 10000 (optional)
-r Number of random trials (optional)
-n Just print number of neighbors for random trials (optional)
-s Skip filtering GFF file
Example: java -jar pathTo/T2/Apps/AnnotateRegions -g /dmel/dmel_RELEASE4.0.gff3 -p
/affy/zeste/finalPicks.txt -c /dmel/CGs.txt
**************************************************************************************
**************************************************************************************
** Bar2Gr: Nov 2006 **
**************************************************************************************
Converts xxx.bar to text xxx.gr files.
-f The full path directory/file name for your xxx.bar file(s).
Example: java -Xmx1500M -jar pathTo/T2/Apps/Bar2Gr -f /affy/BarFiles/
**************************************************************************************
**************************************************************************************
** Cel File Converter: Feb 2008 **
**************************************************************************************
Converts text version cel files into serialized java float[][]s for use by TiMAT2
applications.
Parameters:
-f Full path to a text version 'xxx.cel' file or directory containing the same.
-b Optional, full path to bunzip2. Convert compressed cel files 'xxx.cel.bz2'
-s Full path to alternative save directory, defaults to cel file parent directory.
-c Save a float[] by concatinating the lines of the float[][]
-r Rotate cel file 90 degrees clockwise.
Example: java -Xmx512M -jar pathTo/T2/Apps/CelFileConverter -f /data/cels/
**************************************************************************************
**************************************************************************************
** Cel File Quality Control: May 2006 **
**************************************************************************************
Calculates a variety of qc statistics on serialized float[][] version cel files.
Uses hierarchical clustering of Pearson correlation coefficients based on cel files'
PM intensities to group like cel files and flag outliers.
Required Parameters:
-f Cel files (serialized float[][], 'xxx.cela', output from the CelFileConverter app)
to process, no spaces. Use key=value grouping to associate
files for comparison (e.g. grp1=/data/file1.cela,grp1=/data/file2.cela,
grp2=/data/file3.cela,grp2=/data/file4.cela,/data/file5.cela). Grouped/ ungrouped
mixing permitted. Directories can also be specified instead of individual files,
be sure each file you want examined ends in 'cela' (ie grp5=/data/treatDir/)
-x Full path to the serialized 1lq control coordinate file ('xxx.1lq.SerCont')
generated by the trans/qc/CoordinateExtractor1lq app.
-r Full path directory name where results should be saved.
Optional Parameters:
-l Load parameters file, full path file name, see -e option.
-s Switch Dim and Bright coordinates.
-e Print example parameters file with current defaults.
-c Minimum acceptable within group correlation coefficient (R) (defaults to 0.75).
-p Write outlier image PNG files to disk, defaults to false.
-a Write image PNG files for all files, defaults to no.
-d Display charts, default to no.
Example: java -Xmx512M -jar pathTo/T2/Apps/CelFileQualityControl -f tr=/data/file1.cel,
tr=/data/file2.cel,cont=/data/file3.cel,cont=/data/file4.cel,/data/file5.cel
-s -c 0.9 -p -d -x /maps/dmel.1lq.SerCont -r /data/QCDmelRNAResults/
**************************************************************************************
**************************************************************************************
** Cel Masker: Feb 2006 **
**************************************************************************************
CM builds and displays a virtual chip from a text version cel file. Problem areas on
the chip can be circled and assigned a different intensity. Use the median chip value
to neutrally mask the area.
-c Full path file name for a text version cel file or converted xxx.cela file
-r The number of rows on the chip, defaults to 2560, only needed for text cel files.
-m Maximum intensity value to color scale, defaults to 20000.
-v Value to assign to the masked region. Defaults to the median chip intensity.
Mouse click once and drag to create a selection ellipse.
Use the arrow keys for fine adjustment.
Use shift+ arrow keys to modify the ellipse.
Double click the mouse to set the ellipse and create another.
Use the i or o keys to zoom in and out.
Press the m key to apply the masks and redraw the image.
Press the q key to apply the masks and write the modified cel file to disk.
Example: java -Xmx256M -jar pathTo/T2/Apps/CelMasker -c /affy/badChip.cel -r 1280
-m 250
**************************************************************************************
**************************************************************************************
** Cel Processor: Sept 2008 **
**************************************************************************************
CP tpmaps, quantile normalizes, and median scales raw intensity values from float[][]
'xxx.cela' files. Group files using different directories.
Use the following options when running CP:
-t Full path directory name for the 'TPMapFiles' directory generated by the
TPMapProcessor.
-d A directory containing xxx.cela files for normalization or a comma delimited list.
-u Use MisMatch data, perform max((PM-MM),1) transformation, default is no.
-q Skip quantile normalization, default is to perform QN.
-c Median scale using control sequences instead of all the mapped intensities.
-p Scale using control genes and cubic splines (chip set dependent).
-m The target median value for scaling, defaults to 100.
-r Break and save each normalized cel file as chromosome specific IntensityFeature[]s
for downstream multi-chip merging.
-s Calculate and print statistics on each .cel file, default is no.
-i Correlate and hierarchical cluster raw cel file PM intensities. Provide a full path
name to save the chart.
Example: java -Xmx1500M -jar pathTo/T2/Apps/CelProcessor -t /affy/TPMapFiles/ -d
/affy/tCels/,/affy/cCels/ -u -s -m 50 -i /affy/QC/hcPlot.png
**************************************************************************************
**************************************************************************************
** Coordinate Extractor 1lq: Sept 2007 **
**************************************************************************************
Extracts control and pm coordinates from an unrotated text 1lq file. An int[][][]
is saved to disk for each 1lq file. Also creates virtual cel files for each class for
visualization using the VirtualCel app.
-f Full path to a 'xxx.1lq' file or directory containing such for extraction.
-r Alternative results directory, defaults to the xxx.1lq parent directory.
-s Save virtual 'xxx.cela' files for each set of coordinates, each is assigned an
intensity of 100, default is no.
-p Extract coordinates for pre 7G, unrotated, cel files, default is to rotate 1lq.
Example: java -Xmx512M -jar pathTo/T2/Apps/CoordinateExtractor1lq -f
/data/hmn1lqFiles/
**************************************************************************************
**************************************************************************************
** Convert Agilent Data: Nov 2007 **
**************************************************************************************
Parses Agilent two color data files to map and cela files for use in TiMAT2.
Assumes each oligo is a 60mer. Also creates log2(Green/Red) sgr files for IGB.
WARNING, Agilent file formats are so relaxed as to make this parser near useless.
Each raw txt data file must be tested extensively!
-f Full path file name or directory containing text Agilent two color tiling data.
-e Exclude any 'ControlType' data lines that are not equal to zero.
-d To filter data lines based on their 'SystematicName,' provide a Java RE that will
match. Non matches will be dropped. For example, '.*Spombe.+' .
-c To generate a tpmap and sgr files, provide a Java RE to extract the chromosome #
and the oligo start and stop positions, ie for 'Spombe|complement_chr2:10-70' one would
provide '.+chr(.+):(\d+)-(\d+)' from the 'SystematicName' column.
-n Number to subtract from oligo start position, defaults to zero. Use to change
coordinates to interbase/ zero based if needed.
-s Switch ratio to Red(cy5)/Green(cy3) for making sgr files.
Example: java -Xmx1500M -jar pathTo/T2/Apps/ConvertAgilentData -f /badData/ -c
'.+chr(.+):(\d+)-(\d+)' -s -d '.*Spombe.+' -e
**************************************************************************************
**************************************************************************************
** Convert Geo Data: April 2007 **
**************************************************************************************
Parses a Gene Expression Omnibus individual data file for a particular sample, not a
Download family file. The SOFT, MINiMl and TXT formats are useless since they don't
give access to the raw data only the processed ratios. This is very free form. Check
your results carefully! Assumes two color data files, makes a tpmap and cela files
for use in TiMAT2. Also creates log2(Green/Red) sgr files for IGB. Column indexes
start with zero! Your GEO data file should look something like the following:
#ID_REF =
#Rval = Cy5 (red) signal, Input total DNA from WI38 cells
#Gval = Cy3 (green) signal, MeDIP DNA from WI38 cells
#VALUE = log2(Gval/Rval)
ID_REF Rval Gval VALUE
CHR1P000009757_HSAP0406S00000003 2934.45 6303.33 1.1030
CHR1P000009637_HSAP0406S00000003 4742.78 13346.00 1.4926
CHR1P000009627_HSAP0406S00000003 3311.56 8825.89 1.4142
...
Options:
-f Full path file name or directory containing text files.
-o Length of oligo.
-c Column index of chromosome position.
-r Column index of green intensity values.
-g Column index of red intensity values.
-s Switch ratio to Red/Green for making sgr files.
Example: java -Xmx1500M -jar pathTo/T2/Apps/ConvertGeoData -f /data/ -c 0 -r 2 -g 1
**************************************************************************************
**************************************************************************************
** Convert Nimblegen NDF 2 TPMap: June 2008 **
**************************************************************************************
Converts a Nimblegen NDF text file to a tpmap.
-f Full path file name or directory containing text xxx.ndf file(s).
-a Parse all lines, default is to look for linese beginning with 'chr' or 'CHR'
Example: java -Xmx1500M -jar pathTo/T2/Apps/ConvertNimblegenNDF2TPMap -f /badData/
**************************************************************************************
**************************************************************************************
** Convert Nimblegen PAIR 2 Cela: Jan 2007 **
**************************************************************************************
Converts Nimblegen PAIR text data file(s) to cela.
-f Full path file name or directory containing text xxx.pair file(s).
Example: java -Xmx1500M -jar pathTo/T2/Apps/ConvertNimblegenPAIR2Cela -f /badData/
**************************************************************************************
**************************************************************************************
** Correlation Maps: Nov 2007 **
**************************************************************************************
CM calculates a correlation score for each window of genes and using permutation, an
empirical p-value. The correlation score is the mean of all pair Spearman ranks for
the gene expression profiles in each window. If a single value is given (unlogged!) for
each gene, a mean of the scores within each window is calculated.
To calculate p-values, X randomized datasets are created by shuffling the expression
profiles between genes, windows are scored and pooled. P-values for each real
score are calculated based on the area under the right side of the randomized score
distribution. In addition to a spread sheet report summary, heat map xxx.bar files
for the p-values and mean correlation are created for visualization in IGB.
Note, this analysis is not stranded. If so desired parse lists appropriately.
Parameters:
-f The full path file name for a tab delimited gene file (name,chr,start,stop,scores)
-o Region filter file, full path file name for a tab delimited region file to use in
removing genes from correlation analysis. (chrom, start, stop).
-g Genome version for IGB visualizations (e.g. C_elegans_May_2007).
-w Window size, default is 50000bp. Setting this too small may exclude some regions.
-n Minimum number of genes required in each window, defaults to 3. Setting this too
high will exclude some regions.
-r Number random trials, defaults to 100
Example: java -Xmx256M -jar pathTo/T2/Apps/CorrelationMaps -f /Mango/geneFile.txt
-w 30000 -n 2 -o /Mango/operons.txt
**************************************************************************************
**************************************************************************************
** Correlate: Nov 2008 **
**************************************************************************************
Calculates all pair-wise Pearson correlation coefficients (r) and if indicated will
perform a hierarchical clustering on the files.
Parameters:
-d The full path directory name containing serialized java float[] files (xxx.celp
see CelProcessor app).
-a Files provided are float[][] files (xxx.cela) and need to be collapsed to float[]
-c Cluster files.
Example: java -Xmx256M -jar pathTo/T2/Apps/Correlate -f /Mango/PCels/ -c
**************************************************************************************
**************************************************************************************
** Best Window Score Extractor: September 2008 **
**************************************************************************************
BWSE prints the best window score for every region. Preference is given to
the best centered and closest to the size to the region. Use the -s flag to bind the
best positive scoring window.
Parameters:
-w Full path file name for the serialized Window[] generated by ScanChip or
ScanChromosome.
-r Full path file name for the tab delimited text regions file (chrom, start, stop,
... etc.
-z Size of oligo, defaults to 25.
-i Score index to use in extracting Window scores see ScanChip, defaults to 1.
-s Find best scoring window within each region, defaults to best centered, similar size.
-m Multiply scores by -1
Example: java -Xmx1500M -jar pathTo/Apps/BestWindowScoreExtractor -w
/affy/wins -r /affy/qPCRRes.txt -i 0 -s
**************************************************************************************
**************************************************************************************
** Export Intergenic Regions May 2007 **
**************************************************************************************
EIR takes a gff file and uses it to mask a boolean array. Parts of the boolean array
that are not masked are returned and represent integenic sequences. Be sure to put in
a gff line at the end of each chromosome noting the last base so you caputure the last
intergenic region. (eg chr1 GeneDB lastBase 3600000 3600001 . + . lastBase). Base
coordinates are assumed to be end inclusive, not interbase.
Parameters:
-g Full path file name for a gff file or directory containing such.
-t Base pairs to trim from the ends of each intergenic region, defaults to 0.
-m Minimum acceptable intergenic size, those smaller will be tossed, defaults to 60bp
-s Subtract one from the start and stop coordinates of your gff file.
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportIntergenicRegions -s -m 100 -g
/user/Jib/GffFiles/Pombe/sanger.gff
**************************************************************************************
**************************************************************************************
** Export Intronic Regions June 2007 **
**************************************************************************************
EIR takes a UCSC Gene table and fetches the most conservative/ smallest intronic
regions. Base coordinates are assumed to be end inclusive, not interbase.
Parameters:
-g Full path file name for the UCSC Gene table.
-m Minimum acceptable intron size, those smaller will be tossed, defaults to 60bp
-s Subtract one from the stop coordinates of your UCSC table to convert from interbase.
Example: java -Xmx1000M -jar pathTo/T2/Apps/ExportIntronicRegions -s -m 100 -g
/user/Jib/ucscPombe.txt
**************************************************************************************
**************************************************************************************
** FDR Window Converter: May 2006 **
**************************************************************************************
Given a Window[] from a mock IP analysis (ie IgG) and Window[]s from real IPs, FDRWC
will associate an empirical FDR (# mock/ # real windows at each threshold) for each
real IP Window. The FDR will be appended on to each Window's score array as a
-10Log10(fdr) transformed value.
-m Full path file name for the mock IP serialized Window[] array.
-r Full path file name for a directory or file containing real IP serialized Window[]s.
-s Score index to use converting to FDRs, see ScanChip or ScanChromosome.
-z Size of oligo, defaults to 25.
-p Print sgr files for the empirical FDR scores.
Example: java -Xmx1500M -jar pathTo/T2/Apps/FDRWindowConverter -m /affy/mockWins -r
/affy/realIPs/ -s 1
**************************************************************************************
**************************************************************************************
** FetchGenomicSequences: May 2008 **
**************************************************************************************
Given a file containing genomic coordinates, fetches and saves the sequence (column
output: chrom origStart origStop fetchedStart fetchedStop completeFetch seq).
-f Full path file name to the file or directory containing tab delimited chrom, start,
stop files. Interbabase coordinates (zero based, end excluded).
-s Full path directory name containing containing genomic fasta files. The fasta
defines the name of the sequence, not the file name.
-b Fetch flanking bases, defaults to 0. Will set start to zero or end to last base if
boundaries are exceeded.
Example: java -Xmx1000M -jar pathTo/T2/Apps/FetchGenomicSequences -f /data/miRNAs.txt
-c /genomes/human/v35.1/ -b 5000.
**************************************************************************************
**************************************************************************************
** Find Neighboring Genes: Nov 2008 **
**************************************************************************************
FNG takes a list of genes in UCSC Gene Table format and intersects them with a list of
regions finding the closest gene to each region as well as all of the genes that fall
within a given neighborhood. Distance is measured from the center of the region to the
transcription start site/ 1st base position in 1st exon. See Tables link under
http://genome.ucsc.edu/ . Note, output coordinates are zero based, end inclusive.
-g Full path file name for a tab delimited UCSC Gene Table (name chrom strand txStart
txEnd cdsStart cdsEnd exonCount exonStarts exonEnds etc...) .
-p Full path file/directory name for tab delimited region list(s) (chr, start, stop) .
-b Size of neighborhood in bp, default is 10000
-f Find genes that overlap neighborhood irregardles of distance to TSS.
-c Only print closest genes.
-o Print neighbors on one line.
Example: java -jar pathTo/T2/Apps/FindNeighboringGenes -g /anno/hg17Ensembl.txt -p
/affy/p53/finalPicks.txt -b 5000 -c
**************************************************************************************
**************************************************************************************
** File Cross Filter: March 2008 **
**************************************************************************************
FCF take a column in the matcher file and uses it to parse the rows from other files.
Useful for pulling out and printing in order the rows that match the first file.
-m Full path file name for a tab delimited txt file to use in matching.
-f Ditto but for the file to parse, can specify a directory too.
-i Ignore duplicate keys.
-a Column index containing the unique matcher IDs, defaults to 0.
-b Ditto but for the files to parse.
Example: java -jar pathTo/T2/Apps/FileCrossFilter -f /extendedArrayData/ -m /old/
originalArray.txt -a 2 -b 2
**************************************************************************************
**************************************************************************************
** File Joiner: Feb 2005 **
**************************************************************************************
Joins text files into a single file, avoiding line concatenations. This is a problem
with using 'cat * >> combine.txt'. Removes empty lines.
Required Parameters:
-f Full path name for the directory containing the text files.
Example: java -jar pathTo/T2/Apps/FileJoiner -f /affy/SplitFiles/
**************************************************************************************
**************************************************************************************
** File Splitter: May 2009 **
**************************************************************************************
Splits a big text file into smaller files given a maximum number of lines.
Required Parameters:
-f Full path file name or directory for the text file(s) (.zip/.gz OK).
-n Maximum number of lines to place in each split file.
Example: java -Xmx256M -jar pathTo/T2/FileSplitter -f /affy/bpmap.txt -n 50000
**************************************************************************************
**************************************************************************************
** Filter TPMap By Regions: Jan 2006 **
**************************************************************************************
FTBR strips any oligos not contained within one or more of the user defined regions.
-t Full path file name for the tpmap file.
-r Full path file name for the tab delimited regions file (chr start stop
(inclusive, zero based)).
Example: java -Xmx1500M -jar pathTo/T2/Apps/FilterTPMapByRegions -t /affy/enc.tpmap
-r /affy/encRgns.txt
**************************************************************************************
**************************************************************************************
** Find Sub Binding Regions: Oct 2006 **
**************************************************************************************
FSBR takes Interval[] that have been loaded with oligo information and scans each for
the highest median ratio scoring sub window. FSBR also picks binding peaks using
smoothed intensity values (trimmed mean ratio sliding window) .
Parameters:
-i The full path file name for a serialize Interval[] file, alternatively, give a
directory, and all the Interval files within will be processed.
-w Sub Window size, default is 350bp.
-n Minimum number of oligos required in sub window, defaults to 4.
-s Peak Picker smoothing window size, default is 230bp.
-m Max number of peaks to find, defaults to 4.
-c Minimum binding peak ratio score, defaults to 1.3, set internally for flattops.
-f Pick flattop peaks, defaults to sloped peaks.
-g Max bp gap for expansion of flattop peaks, defaults to 75bp.
-d Maximum fraction score drop for expanding flattop peaks, defaults to 0.8.
Example: java -Xmx256M -jar pathTo/T2/Apps/FindSubBindingRegions -i /affy/Ints/ -w
300 -n 4
**************************************************************************************
**************************************************************************************
** Gr2Bar: Nov 2006 **
**************************************************************************************
Converts xxx.gr.zip files to chromosome specific bar files.
-f The full path directory/file name for your xxx.gr.zip file(s).
-v Genome version (ie hg18, dm2, ce2, mm8), get from UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases
Example: java -Xmx1500M -jar pathTo/T2/Apps/Gr2Bar -f /affy/GrFiles/ -v hg17
**************************************************************************************
**************************************************************************************
** Hierarchical Clustering: July 2007 **
**************************************************************************************
HC hierarchically clusters serialized float[] arrays using a Pearson correlation
coefficient (r) as a metric. For each round, all pairwise r values between the arrays
are calculated, the pair of arrays with the highest r value is removed from the pool,
their intensities are averaged, and the averaged array is added back to the pool.
Rounds of clustering continue until only one cluster remains.  For ChIP chip
experiments, clusters with an r < 0.75 (50% similar) are processed separately.
-d Full path directory name containing serialized float[] arrays, 'xxx.celp' files.
-c These are text files containing one column of floats, convert to 'xxx.celp'
-a AntiLog base 2 the values.
Example: java -Xmx1500M -jar pathTo/T2/HierarchicalClustering -d /affy/CelpFiles
**************************************************************************************
**************************************************************************************
** Index Fastas: April 2007 **
**************************************************************************************
IF is used to break apart long sequences into small binary files for rapid access.
-f Full path file name to a xxx.fasta sequence or directory containing such.
-n Number of bases permitted in each indexed file, defaults to 1,000,000.
-i Alternative save directory, defaults to fasta directory.
Example: java -Xmx1500M -jar pathTo/T2/Apps/IndexFastas -f /genomes/hg17/
**************************************************************************************
**************************************************************************************
** Intensity Printer: April 2007 **
**************************************************************************************
IP prints to file intensity scores in a .sgr text format for direct import into
Affy's IGB.
Use the following options when running IP:
-b Full path directory name for the 'xxxTPMapFiles' generated by the TPMapProcessor
-i The full path director or file name for the xxx.celp intensity files.
Example: java -Xmx256M -jar pathTo/T2/Apps/IntensityPrinter -b /affy/TPMapFiles/ -i
/affy/t.celp
**************************************************************************************
**************************************************************************************
** Intersect Lists: Dec 2008 **
**************************************************************************************
IL intersects two lists (of genes) and using randomization, calculates the
significance of the intersection and the fold enrichment over random. Note, duplicate
items are filtered from each list prior to analysis.
-a Full path file name for list A (or directory containing), one item per line.
-b Full path file name for list B (or directory containing), one item per line.
-t The total number of unique items from which A and B were drawn.
-n Number of permutations, defaults to 1000.
-p Print the intersection sets (common, unique to A, unique to B) to screen.
Example: java -Xmx1500M -jar pathTo/Apps/IntersectLists -a /Data/geneListA.txt -b
/Data/geneListB.txt -t 28356 -n 10000
**************************************************************************************
**************************************************************************************
** Intersect Regions: August 2008 **
**************************************************************************************
IR intersects lists of regions (tab delimited: chrom start stop(inclusive)). Random
regions can also be used to calculate a p-value and fold enrichment.
-f First regions files, a single file, or a directory of files.
-s Second regions files, a single file, or a directory of files.
-g Max gap, defaults to 0. A max gap of 0 = regions must abut, negative values force
overlap (ie -1= 1bp overlap, be careful not to exceed the length of the smaller
region), positive values enable gaps (ie 1=1bp gap).
-e Score intersections where second regions are entirely contained by first regions.
-r Make random regions matched to the second regions file(s) and intersect with the
first. Enter the full path directory name containing chromosome specific
interrogated regions files (ie named: chr1, chr2 ...: chrom start stop(inclusive)).
-c Match GC content of second regions file(s) when selecting random regions, rather
slow. Provide a full path directory name containing chromosome specific genomic
sequences. To speed the matching place the fraction GC in the last column of
your region file(s).
-n Number of random region trials, defaults to 1000.
-w Write intersection and difference files for the first and second region files.
-x Write paired intersections to file.
-p Print length distribution histogram for gaps between first and closest second.
-q Parameters for histogram, comma delimited list, no spaces:
minimum length, maximum length, number of bins. Defaults to -100, 2400, 100.
-i Serialized interval files are provided, not text region files.
Example: java -Xmx1500M -jar pathTo/Apps/IntersectRegions -f /data/miRNAs.txt
-s /data/DroshaLists/ -g 500 -n 1000 -r /data/InterrogatedRegions/
**************************************************************************************
**************************************************************************************
** Interval Filter: April 2007 **
**************************************************************************************
IF filters Interval[] arrays dividing and saving Intervals that pass and fail.
The reported number of cut intervals is sequential and may not reflect the total
number that would fail each particular test when using a single filter.
-i Score index to use in window filtering, see ScanChromosome or ScanChipNoPerm
-a Filter by the best window score based on the score index (minimum).
-b Filter by the best median ratio sub window (minimum).
-f Filter by the coefficient of the variation (stnd dev window oligos/ mean) for
the intensities, either treatment or control (singleton,
one chip driver) (maximum)
-e Flag intervals that intersect particular regions, enter full path names, comma
delimited, containing tab delimited: chrom start stop.
-m Minimal fraction of intersection with particular regions for removal, defaults to
0.25. Measured as cumulative bases covered, relative to the interval.
-g Remove particular intervals, enter a full path name for a tab delimited text
file containing rows with chromosome start stop.
-k Full path file name for the Interval[], if a directory is specified, all files
within will be processed. (Required)
Example: java -Xmx256M -jar pathTo/T2/Apps/IntervalFilter -k /affy/res/ -a 1.5 -i 1
-e /repeats/hg17_simpleSegDupRepMask.txt -m 0.5
**************************************************************************************
**************************************************************************************
** Interval GFF Printer: Feb 2007 **
**************************************************************************************
IGP prints a GFF3 txt file given a serialized Interval file, sorts by the median ratio
of the best sub window. Use the following options:
-f Full path file name for the Interval[], if a directory is specified, all files
within will be processed and saved to the same GFF3 file.
-d Don't print non interval lines (ie window, oligo).
-s Print simple GFF not GFF3.
-i Score index to use in assigning the best window summary score. See ScanChip.
Example: java -jar pathTo/T2/Apps/IntervalGFFPrinter -f /my/affy/res/ -i 2
**************************************************************************************
**************************************************************************************
** Interval Graph Printer: Jun 2006 **
**************************************************************************************
IGP converts serialized Interval[] arrays to graph files (.sgr & .bed) for import into
Affymetrix' IGB. Files will be written to the data directory. Intervals are sorted by
the median ratio of the best sub window, if present, or the best window.
Use the following options when running IGP:
-f Full path file name for the Interval[], if a directory is specified, all files
within will be processed. (Required)
-s Score cut off, print everything above this score, defaults to all.
-r Rank cut off, prints everything above this rank, ie the top 200, defaults to all.
-g Print goal post Interval representations using the sub window score, if present, or
the best window score, default is to print individual oligo log2(aveT/aveC)
scores.
-c Chromosome prefix to prepend onto each .sgr line. (This is sometimes needed to match
IGB's quickload seqIDs.)
Example: java -jar pathTo/T2/Apps/IntervalGraphPrinter -f /my/affy/res/ -s 50 -r 200
**************************************************************************************
**************************************************************************************
** Interval Maker: June 2007 **
**************************************************************************************
IM combines Windows into larger Intervals given a minimal score, minimum number of
oligos, and a maximum gap.
Options:
-s Minimal score, defaults to 0. Can also provide a comma delimited list, no spaces,
to use in generating multiple Interval arrays.
-i Score index, the score you wish to use in merging, see ScanChip or ScanChromosome.
-m Multiply window scores by -1. Useful for looking for reduced regions.
-o Minimum number of oligo positions in each window, defaults to 10.
-g Maximum allowable bp gap between starts of oligos, defaults to 24.
-z Size of oligo, defaults to 25.
-r Enter a full path tab delimited regions file name (chr start stop) to use in
removing intersecting windows. Coordinates are assumed to be zero based and end
inclusive.
-f Full path file name for the Window[] array, if a directory is specified,
all files will be processed.
Example: java -Xmx500M -jar pathTo/T2/Apps/IntervalMaker -f /affy/res/zeste.res -s 50
-i 0 -g -200 -o 5
**************************************************************************************
**************************************************************************************
** Interval Plotter: Nov 2005 **
**************************************************************************************
IP plots and saves graphs for serialized Interval[] arrays. Load the Intervals with
data from the FindSubBindingRegion, LoadIntervalOligoInformation, and optionally
ScoreIntervals programs prior to running the IntervalPlotter. Plotted are graphs for
averaged treatment intensity, the averaged control intensity, the average difference,
the average fold difference, a smoothed trimmed mean ratio, peak picks, the best
window, the best sub window, centered PSPM hit scores, the number of 1bp mis/matches
for each oligo in the genome, and the treatment and control intensities for each
individual processed cel file. Click and or drag to fetch the coordinates and sequence
for a selected region. Columns in console: interval rank, median ratio best sub window,
trimmed mean score for the closest oligo if just one click or the max trimmed mean
score within the dragged box, chromosome, start, stop, and sequence. Intervals are
sorted by the best median ratio sub window.
-f Full path file or directory name for the data loaded Interval[](s).
-s Score cut off, plot everything above this score, defaults to all.
-r Rank cut off, plot everything above this rank, ie the top 200, defaults to all.
-q Rank cut off, plot everything below this rank, defaults to all.
-p Save plots to disk, default is no.
-m Magnify saved plots (2 twice as big, 3 three times as big...), default is none.
-a Anti alias saved plots, good for printed figures, bad for computer display,
default is no.
-b Hide the PSPM graphs.
Example: java -jar pathTo/T2/Apps/IntervalPlotter -f /affy/res/Z.resAll -t -s 1.5 -r
200 -p -m 1.5 -a
Questions? Contact<EMAIL_ADDRESS>or<EMAIL_ADDRESS>**************************************************************************************
**************************************************************************************
** Interval Report Printer: April 2007 **
**************************************************************************************
IRP prints reports in spread sheet format or as detailed pages for Interval[] arrays.
Intervals are sorted by the median ratio of the best sub window.
Use the following options:
-f Full path file name for the Interval[], if a directory is specified, all files
within will be processed. (Required)
-s Score cut off, print everything above this score, defaults to all
-r Rank cut off, prints everything above this rank, ie the top 200, defaults to all
-i Score index to use in sorting when there is no sub window, defaults to 1.
-p Print sequences, default is no
-a Print best window sequence in summary line instead of interval sequence.
-b Print tab delimited summary line, default is to print a detailed report.
-c Print coordinates (chrom, start, stop).
Example: java -jar pathTo/T2/Apps/IntervalReportPrinter -f /my/affy/res/ -s 1.5
-r 200 -p -b
**************************************************************************************
**************************************************************************************
** JQSub: April 2007 **
**************************************************************************************
JQSub executes a given command line on the cluster logging your submission and results
in a temp directory designated by the ~/.paramFileJQSub.txt in your home directory.
Use the word 'return' to separate multiple shell commands. JQSub is java friendly and
will direct your job to the appropriate cluster depending on your -Xmx parameter.
Options:
-w Wall time in hours, defaults to 72. Set to 10-15% longer than expected runtime
for your application. Otherwise it gets the chop.
Example: java util/apps/JQSub -w 1.75 java -Xmx4000M trans/main/ScanChip
**************************************************************************************
**************************************************************************************
** Load Chip Set Interval Oligo Info : April 2007 **
**************************************************************************************
LCSIOI fetches oligo intensities, start positions, and their sequence for a given
array of intervals using the following parameters:
-o The 'OligoPositions' directory, full path, generated by ChipSetCelProcessor.
-s Load sequence information, provide the full path directory name containing indexed
genomic fasta sequences. Run the IndexFastas app on your chromosome split seqs.
-t Treatment chip set directories, full path, comma delimited, no spaces.
-c Control chip set directories, full path, comma delimited, no spaces.
-i The full path Interval[] file name or directory containing Interval[] files.
Example: java -Xmx256M -jar pathTo/T2/Apps/LoadChipSetIntervalOligoInfo -b
/affy/675bpTPMapFiles -s /seq/dmel/ -t /affy/tCels -c /affy/cCels
**************************************************************************************
**************************************************************************************
** Load Interval Oligo Info: Dec 2005 **
**************************************************************************************
LIOI fetches oligo intensities, start positions, and their sequence for a given
array of intervals using the following parameters:
-b The full path 'xxxTPMapFiles' directory name generated by the TPMapProcessor
-s The full path directory name containing the split genomic fasta files.
-t The full path directory name containing the serialized float[] treatment file(s)
-c The full path directory name containing the serialized float[] control file(s)
-i The full path Interval[] file name or directory containing Interval[] files.
Example: java -Xmx256M -jar pathTo/T2/Apps/LoadIntervalOligoInfo -b
/affy/675bpTPMapFiles -s /seq/dmel/ -t /affy/tCels -c /affy/cCels
**************************************************************************************
**************************************************************************************
** Make Chromosome Sets: Feb 2007 **
**************************************************************************************
MCS takes directories containing split chromosome intensity files from the
CelProcessor app and combines them into a master set of chromosome split intensity
files for use by ScanChromosomesCNV.
Use the following options when running CP:
-d Comma delimited list of directories containing split chromosome files from
CelProcessor, no spaces. These should represent one complete replica.
-n Full path directory name to save the combine chomosomes.
-s Skip making chromosome oligo positions (you already have them for this chip set).
Example: java -Xmx1500M -jar pathTo/T2/Apps/MakeChromosomeSets -d
/ProcCelFiles/Chip1RepA,/ProcCelFiles/Chip2RepA,/ProcCelFiles/Chip3RepA -n
/ProcCelFiles/RepA
**************************************************************************************
**************************************************************************************
** Merge Window Arrays: March 2007 **
**************************************************************************************
Concatinates serialized Window[]s into one.
-f Full path directory name for the folder containing eserialized Window[]s
from the ScanChromosomesCNV or ScanChip app.
-r Restrict files for merging to those ending in '_Win', defaults to all.
-n (Optional) Full path file name to use in saving the merged windows array.
-m Multiply window scores by -1. Useful for looking for reduced regions.
-t Score threshold, tosses any windows with a score < threshold, defaults to 0.2
-i Score index, defaults to 1. See ScanChromosome for index descriptions.
Example: java -Xmx5000M -jar /YourPathTo/T2/Apps/MergeWindowArrays -f /affy/win/
**************************************************************************************
**************************************************************************************
** Multi Window Interval Maker: July 2006 **
**************************************************************************************
MWIM combines Windows into larger Intervals given minimal score(s), a maximum gap, a
minimum number of windows per index that must pass, and a minimum number of oligos in
each window. For each index, the best window is used to represent the Interval. Must
have all the windows from ScanChip or ScanChromosome, no pruning.
Options:
-i Score index, the score you wish to use in merging, see ScanChipNoPerm or
ScanChromosome.
-o Minimum number of oligo positions in each window, defaults to 10.
-g Maximum allowable bp gap between starts of oligos, defaults to 24.
-z Size of oligo, defaults to 25.
-s Minimal score(s), comma delimited, no spaces, one for each Window[] file.
-f Full path file names for the Window[]s, comma delimited, no spaces.
-n Composite name to use as a base in saving.
-m For a given window index, minimum number of windows from the different arrays that
must pass to be included in building an interval. Defaults to all.
Example: java -Xmx1500M -jar pathTo/T2/Apps/MultiWindowIntervalMaker -f
/affy/win1,/affy/win2,/affy/win3 -i 1 -g -100 -s 0.5,0.5,0.3 -m 2
**************************************************************************************
**************************************************************************************
** Mummer Mapper: April 2007 **
**************************************************************************************
MM uses MUMMER to generate a 7G or pre 7G tpmap from a standard, unrotated, 1lq file
using genomic fasta file(s). Note, DO NOT use a rotated 7G specific 1lq file! Oligos
in the 1lq file are reversed prior to mapping. MUMMER matches are case-insensitive but,
restricted to GATC, thus use Ns or Xs to mask your genomic fasta files. For large
fasta's compile and run MUMMER on a 64 bit machine. Coordinate are zero based and
relative to the forward (+) strand. In addition to the genomic fasta files, it may be
usefull to include one named chrCtrls and populated with control sequences
found on the array (e.g. bacterial, Arabidopsis, known regions that don't change).
These can be used by the TiMAT2 CelProcessor app for defined region median scaling.
See MUMMER from http://sourceforge.net/projects/mummer .
Parameters:
-p Full path file name for the 1lq file.
-r Full path directory file name to save the results.
-g Full path file or directory name containing genomic (multi) fasta file(s).
-m Full path file name for the mummer application. Defaults to
/nfs/transcriptome/software/noarch/T2/64Bit_MUMmer3.18/mummer
-x Maximum number of exact matches allowed per oligo, defaults to 100, set to 1
for unique oligos.
-y Minimum size of oligos to map, others will be excluded. Defaults to 25.
-a Match forward complement, defaults to both.
-b Match reverse complement, defaults to both.
-c Reverse complement the 1lq oligo sequences. This switches the orientation of
the 1lq file S <-> AS. (Use of -b -c is the same as -a.)
-i Don't reverse the oligo sequence, by default oligo sequences are reversed
since seqs in 1lq format are listed 3' to 5'.
-o Replace the orientation column with the number of exact matches for a
particular oligo in the genome. Doing so enables graphing of matches when
generating interval plots in TiMAT2 (recommended).
-s Save temp files to '/scratch/'.
-e Remove control probes that also match non-control fasta sequences.
-f Enter a comma delimited list, no spaces, of particular DESTYPES to map, others
will be skipped. Using '-111,111' is recommended! (PM= -111,111; MM= -113,113;
'manufacture/ gridding controls= 0,1,-1,-3,3,-4,4,-6,6').
-d Don't print mismatch coordinates (ie they don't exist).
-n Make a new 7G scanner tpmap, enter the number of rows/ columns in a cel file,
default is to make an old pre-7G tpmap.
MM probes are assumed to be directly below their PM counter-part on the array. If
mapping MM oligos (not recommended) their 'MM but really PM' counter-part is
assumed to be directly above.
To make an antisense stranded tpmap from an antisense 1lq file use option -b. To
make a sense stranded tpmap from an antisense 1lq file use options -c -a.
To make a mock 1lq file, create a tab delimited text file with the following:
X Y Seq Destype ... Be sure to have this header immediately preceed your map
data. The Destype column is optional but can be used to filter your mock 1lq
for particular lines. If needed, use the -i flag to prevent reversing of your
sequences. Rember that X and Y are zero based. The PrintSelectColumns app can be
used to manipulate your map file to make a mock 1lq.
Example: java -Xmx1500M -jar pathTo/T2/Apps/MummerMapper -p /affy/human.1lq -f
-111,111 -g /affy/human/NCBIv34/ -m
/nfs/transcriptome/software/noarch/T2/64Bit_MUMmer3.18/mummer -r /affy/results/
-s -x 10 -n 2560 -y 22
**************************************************************************************
**************************************************************************************
** Oligo Intensity Printer: Dec 2005 **
**************************************************************************************
OIP prints to file the average treatment, control, and ratio oligo intensity scores
(no windowing) in a .sgr text format for direct import into Affy's IGB.
Use the following options when running OIP:
-b Full path directory name for the 'xxxTpmapFiles' generated by the TPMapProcessor
-t The full path file name for a directory or file containing serialized float[]
'xxx.celp' treatment file(s).
-c The full path file name for a directory or file containing serialized float[]
'xxx.celp' control file(s).
Example: java -Xmx256M -jar pathTo/T2/Apps/OligoIntensityPrinter -b
/affy/675bp3MinFsTPMapFiles/ -t /affy/t.celp -c /affy/c.celp
**************************************************************************************
**************************************************************************************
** Oligo Tiler: May 2009 **
**************************************************************************************
OT tiles oligos across genomic regions returning their forward and reverse sequences.
Won't tile oligos with non GATC characters, case insensitive. Replaces non GATC chars
in offset regions with 'a'.
Options:
-f Fasta file directory, should contain chromosome specific xxx.fasta files.
-r Regions file to tile (tab delimited: chr start stop ...) interbase coordinates.
-o Effective oligo size, defaults to 40.
-s Spacing to place oligos, defaults to 50.
-t Three prime offset, defaults to 20.
-m Minimum size of region to tile, defaults to 20.
-p Print reverse strand oligos.
-c Tile CpG (spacing not used, see max gap option).
-g Max gap between adjacent CpGs to include in same oligo, defaults to 8.
Example: java -Xmx4000M -jar pathTo/Apps/OligoTiler -s 40 -f /Genomes/Hg18/Fastas/
-r /Designs/cancerArray.bed -p
************************************************************************************
**************************************************************************************
** Overlap Counter: March 2005 **
**************************************************************************************
OC performs a pairwise intersection analysis between each Interval[] file within a
directory. Sets of overlapping and non-overlapping Intervals, for each file, can be
written to disk.
Parameters:
-f Full path file name for a directory containing Interval files, required.
-n The number of Intervals to compare. Enter say 200 to compare the top 200 after
sorting. If they don't exist, all will be used. Default is all.
-m Max gap between boarders, default is 0bp. Negative values force an overlap.
-p Print distance to closest interval peak.
-w Write the overlapping and non-overlapping Interval files to disk, the default is no.
Example: java -Xmx128M -jar pathTo/T2/Apps/OverlapCounter -f /affy/res/Intervals/
-n 250 -p -w -m -100
**************************************************************************************
**************************************************************************************
** ScoreParsedBars: Sept 2008 **
**************************************************************************************
For each region finds the underlying scores from the chromosome specific bar files.
Prints the scores as well as their mean . A p-value for each region's score can be
calculated using chromosome, interrogated region, length, # scores, and gc matched
random regions. Be sure to set the -u flag if your scores are log2 values.
-r Full path file name for your region file (tab delimited: chr start stop(inclusive)).
-b Full path directory name for the chromosome specific data xxx.bar files.
-o Bp offset to add to the position bar file coordinates, defaults to 0.
-s Bp offset to add to the end of each region, defaults to 0.
-u Unlog the bar file values, set this flag if your scores are log2 transformed.
-g Estimate a p-value for the score associated with each region. Provide a full path
directory name for chromosome specific gc content boolean arrays. See
ConvertFasta2GCBoolean app. Complete option -i
-i If estimating p-values, provide a full path file name containing the interrogated
regions (tab delimited: chr start stop ...) to use in drawing random regions.
-n Number of random region sets, defaults to 1000.
-d Don't print individual scores to screen.
Example: java -jar pathTo/Apps/ScoreParsedBars -b /BarFiles/Oligos/
-r /Res/miRNARegions.bed -o -30 -s -60 -i /Res/interrRegions.bed
-g /Genomes/Hg18/GCBooleans/
**************************************************************************************
**************************************************************************************
** Primer3 Wrapper: Dec 2006 **
**************************************************************************************
Wrapper for the primer3 application. Extracts sequence, formats for primer3, executes
and parses the output to a spreadsheet. See http://frodo.wi.mit.edu/primer3/
-f Full path file name for your sequence file, tab delimited, sequence in 1st column.
-s Pick small product sizes (45-80bp), defaults to standard (80-150bp)
-p Full path name for the primer3_core application. Defaults to
/nfs/transcriptome/software/noarch/T2/64Bit_Primer3_1.0.0/src/primer3_core
-m Full path file name for the mispriming library. Defaults to
/nfs/transcriptome/software/noarch/T2/64Bit_Primer3_1.0.0/
cat_humrep_and_simple.cgi.txt
Example: java -jar pathTo/T2/Apps/Primer3Wrapper -f /home/dnix/seqForQPCR.txt -p
/nfs/transcriptome/software/noarch/T2/64Bit_Primer3_1.0.0/src/primer3_core
-m /nfs/transcriptome/software/noarch/T2/64Bit_Primer3_1.0.0/
cat_humrep_and_simple.cgi.txt -s
**************************************************************************************
**************************************************************************************
** Print Select Columns: July 2006 **
**************************************************************************************
Spread sheet manipulation.
Required Parameters:
-f Full path file or directory name for tab delimited text file(s)
-i Column indexs to print, comma delimited, no spaces
-n Number of initial lines to skip
-l Print only this last number of lines
-c Column word to append onto the start of each line
-r Append a row number column as the first column in the output file
-d Append file name onto the start of each line
-s Skip blank lines and those with less than the indicated number of columns.
Example: java -jar pathTo/T2/PrintSelectColumns -f /TabFiles/ -i 0,3,9 -n 1 -c chr
**************************************************************************************
**************************************************************************************
** Ranked Set Analysis: Jan 2006 **
**************************************************************************************
RSA performs set analysis (intersection, union, difference) on lists of
genomic regions (tab delimited: chrom, start, stop, score, (optional notes)).
-a Full path file name for the first list of genomic regions.
-b Full path file name for the second list of genomic regions.
-d (Optional) Full path directory containing region files for all pair analysis.
-m Max gap, bps, set negative to force an overlap, defaults to -100
-s Save comparison as a PNG, default is no.
Example: java -jar pathTo/T2/Apps/RankedSetAnalysis -a /affy/nonAmpA.txt -b
/affy/nonAmpB.txt -s
**************************************************************************************
**************************************************************************************
** Scan Chip: March 2007 **
**************************************************************************************
SC uses a sliding window to calculate window level statistics on processed cel
files (xxx.celp) using a Wilcoxon Rank Sum test and either a trimmed mean (10%) or
pseudo median on the windowed relative differences. To estimate confidence, SC wraps
Bourgon's Symmetric Null P-value estimator and John Storey's QValue R package. SC can
also use random label permutation to estimate a p-value and FDR. A variety of sgr
files can be saved for direct viewing in Affymetrix' IGB. xxxHM.sgr should be viewed
as heat maps or stair-step graphs. Negative scores/ p-values/ FDRs indicate reduction,
positive accumulation.
Note, the order of scores associated with each window are:
[0]= Wilcoxon Rank Sum, -10Log10(p-val)
[1]= Trimmed Mean or Layered Pseudo Median Relative Difference
[2]= -10Log10(uncorrected p-value) based on rnd perm of cel file labels (if set)
[3]= -10Log10(FDR) based on rnd perm of cel file labels (if set)
[4]= -10Log10(q-valFDR) based on symmetric null distribution, (if set)
-b The full path 'TPMapFiles' directory name generated by the TPMapProcessor program
-r The full path file name to use in saving the results
-t The full path directory name containing the serialized float[] treatment file(s)
-c The full path directory name containing the serialized flaot[] control file(s)
-m Minimal number of oligo positions required in each window. Defaults to 10.
-z Size of oligo, defaults to 25.
-a Use a pseudo median of all pair relative differences instead of a trimmed mean.
This is very slow but much more robust.
-x Maximum number of window scores before random sampling for pseudo median
calculation, defaults to 1000. (#T x #C x #oligos in window).
-y Use average treatment/ average control relative differences instead of all layer in
pseudo median calculation.
-p Convert window scores to q-value FDRs, -10log10(multiple test corr pval).
Requires config R, and possibly compiling the SymPTest.
-s The full path to the symmetric_p_test, defaults to
'/home/BioApps/T2/OthersCode/RBSymPTest/symmetric_p_test'
-q The full path to qvalue loaded R, defaults to
'/usr/local/R/bin/R'
-l Use random permutation of the chip labels to estimate a p-value and FDR for the
window scores.
-n Number of random permutations, defaults to 10.
-i Print an individual oligo log2 ratio (aveT/aveC) sgr file.
-d Print heat map window summary sgr files.
-e Print point window summary sgr files.
Example: java -Xmx1500M -jar pathTo/T2/Apps/ScanChip -b /affy/TPMapFiles -r
/affy/res/zeste.res -t /affy/tCels -c /affy/cCels -m 5 -i -e
**************************************************************************************
**************************************************************************************
** Scan Chromosmes: Dec 2007 **
**************************************************************************************
SC uses a sliding window to calculate to window level statistics on processed cel
file directories (see CelProcessor -r and MakeChromosomeSets) using a Wilcoxon Rank
Sum test and one of the following statistics: a trimmed mean (10%) or pseudo median
on the average replica relative differences or a pseudo median on the all pair
relative differences within each window. To estimate confidence SC wraps Bourgon's
Symmetric Null P-value estimator and John Storey's QValue R package. SC can also use
random label permutation to estimate a p-value and FDR. A variety of bar files can be
saved for direct viewing in Affymetrix' IGB. xxxHM.bar should be viewed as heat maps.
Negative scores/ p-values/ FDRs indicate reduction, positive accumulation.
Note, the order of scores associated with each window are:
[0]= Wilcoxon Rank Sum, -10Log10(p-val)
[1]= Trimmed Mean or Layered Pseudo Median Relative Difference
[2]= -10Log10(uncorrected p-value) based on rnd perm of cel file labels (if set)
[3]= -10Log10(FDR) based on random permutation (if set)
[4]= -10Log10(q-val FDR) based on symmetric null distribution, (if set)
Required:
-o The 'OligoPositions' directory, full path, generated by MakeChromosomeSets.
-r The full path directory name to use in saving the results.
-t Treatment chip set directories, full path, comma delimited, no spaces.
-c Control chip set directories, full path, comma delimited, no spaces.
-v Genome version (ie hg17, dm2, ce2, mm8), get from UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases, for bar files.
Optional:
-w Window size, defaults to 675bp.
-m Minimal number of unique oligo positions required in each window. Defaults to
10. Set to 1 to save all windows.
-z Size of oligo, defaults to 25.
-f Name(s) of chromosomes, comma delimited (e.g. chr21,chr22), to process, others
will be skipped, defaults to all.
-b Break and process part of each chromosome(s), (e.g. 2-1 to split in 1/2 and
and process the first half, 3-2 split in thirds, process the 2nd 3rd).
-a Use a layered pseudo median instead of a trimmed mean relative difference.
This is very slow but much more robust. (For every oligo, all rel diffs are
calculated between its treatment and control intensities. These are pooled
and a median is then calculated on all pairwise means.)
-y Use an average of the treatment and control replicas as input for the relative
difference instead of making all pairwise relative differences in the
pseudo median calculation.
-l Use random permutation of the chip labels to estimate a p-value and FDR for the
window score.
-u Use random permutation of the intensity positions to estimate a p-value and FDR
for the window score.
-n Number of random permutations, defaults to 10.
-x Randomize within replica intensities.
-p Convert window ratio scores to q-value FDRs, -10log10(multiple test corr pval).
Requires installing Storey's R Q-Value Pkg and possibly compiling the SymPTest.
-s The full path to the symmetric_p_test, defaults to
'/home/BioApps/T2/OthersCode/RBSymPTest/symmetric_p_test'
-q The full path to qvalue loaded R, defaults to
'/usr/local/R/bin/R'
-i Print an individual oligo log2 ratio (aveT/aveC) bar files.
-e Print point window summary bar files.
-d Print heat map window summary bar files.
-k Strand (either '+' or '-') for stranded data, used when writing bar files.
-j Convert relative difference scores to log2.
-g Exclude windows falling within this range (ie -0.2,0.2) when making heat maps.
Example: java -Xmx8000M -jar pathTo/T2/Apps/ScanChromosomesCNV -o /affy/OliPosHWG14 -r
/affy/res/p53/ -t /Cels/T/B10_ChrNorm,/Cels/T/B11_ChrNorm -c /Cels/C/B10_ChrNorm,
/Cels/C/B11_ChrNorm,/Cels/C/B12_ChrNorm -m 5 -i -e -l -n 3 -v hg17
**************************************************************************************
**************************************************************************************
** Scan Genes: Sept 2007 **
**************************************************************************************
SG parses a UCSC table format gene file, see http://genome.ucsc.edu/cgi-bin/hgTables
(tab delimited: #name chrom strand txStart txEnd cdsStart cdsEnd exonCount exonStarts
exonEnds) to identify the exons associated with each gene. These are then used to
fetch the underlying intensity values. A median is then taken of the aveT/aveC ratio
scores for each gene.
-o The 'OligoPositions' directory, full path, generated by CelProcessor.
-r The full path file name to use in saving the results.
-t Treatment chip set directories, full path, comma delimited, no spaces.
-c Control chip set directories, full path, comma delimited, no spaces.
-g The full path file name for the UCSC table format gene file.
-s Number to subtract from ends, defaults to 1. Used to convert UCSC interbase
numbering to end inclusive numbering. Set to 0 if already end inclusive.
-z Size of oligo. Defaults to 25.
-p Print associated treatment and control intensities.
Example: java -Xmx1500M -jar pathTo/T2/Apps/ScanGenes -o /affy/OligoPositionsHWG14 -r
/genes.xls -t /Cels/T/B10_ChrNorm,/Cels/T/B11_ChrNorm -c /Cels/C/B10_ChrNorm,
/Cels/C/B11_ChrNorm,/Cels/C/B12_ChrNorm -g /ucscHg17RefSeq.txt -p
**************************************************************************************
**************************************************************************************
** Scatter Plot: Aug 2005 **
**************************************************************************************
To draw a simple scatter plot and calculates a Pearson correlation coefficient for two
serialized int[] or float[] arrays, enter full path names on the command line. If you
wish to skip zero values in the analysis, type 'skip' after the second file name.
Example: java -Xmx750M -jar pathTo/T2/Apps/ScatterPlot /my/int/array1 /my/int/array2
**************************************************************************************
**************************************************************************************
** Score Chromosomes: Sept 2008 **
**************************************************************************************
SC scores chromosomes for the presence of transcription factor binding sites. Use the
following options:
-g The full path directory name to the split genomic sequences (i.e. chr2L.fasta,
chr3R.fasta...), FASTA format.
-t Full path file name for the FASTA file containing aligned trimmed examples of
transcription factor binding sites. A log likelihood position specific
probability matrix will be generated from these sequences and used to scan the
chromosomes for hits to the matrix.
-s Score cut off for the matrix. Defaults to the score of the lowest scoring sequence
used in making the LLPSPM.
-p Print hits to screen, default is no.
-v Provide a versioned genome (ie hg18, dm2, ce2, mm8), see UCSC Browser,
http://genome.ucsc.edu/FAQ/FAQreleases, if you would like to write graph LLPSPM
scores to bar files for direct viewing in IGB.
Example: java -Xmx4000M -jar pathTo/T2/Apps/ScoreChromosomes -g /my/affy/Hg18Seqs/ -t
/my/affy/fgf8.fasta -s 4.9 -v H_sapiens_Mar_2006
**************************************************************************************
**************************************************************************************
** Score Intervals: Jan 2005 **
**************************************************************************************
SI scores serialized Interval[] arrays for the presence of transcription factor
binding sites. Use the following options:
-f Full path file name for the Interval[], if a directory is specified, all files
within will be processed. (Required)
-g The full path directory name to the split genomic sequences (i.e. chr2L.fasta,
chr3R.fasta...). The file prefix names should match the chromosome names in the
Intervals (ie chr2L, chr3R...). (Required)
-t Full path file name for the fasta file containing aligned trimmed examples of
transcription factor binding sites. A log likelihood position specific
probability matrix will be generated from these sequences and used to scan all
the Intervals for hits to the matrix.
-s Score cut off for the matrix. Defaults to the score of the lowest scoring sequence
used in making the LLPSPM.
-w A window size in bp for calculating the maximum binding cluster, defaults to 350bp
-i Score the best average intensity window instead of the full interval.
-a Just print average hits per kb for best windows in interval array.
Example: java -jar pathTo/T2/Apps/ScoreIntervals -f /my/affy/intervals/ -g
/my/affy/DmelSeqs/ -t /my/affy/zeste.fasta -s 4.9 -w 375 -i
**************************************************************************************
**************************************************************************************
** Score Sequences: July 2007 **
**************************************************************************************
SS scores sequences for the presence of transcription factor binding sites. Use the
following options:
-g The full path FASTA formatted file name for the sequence(s) to scan.
-t Full path file name for the FASTA file containing aligned trimmed examples of
transcription factor binding sites. A log likelihood position specific
probability matrix will be generated from these sequences and used to scan the
sequences for hits to the matrix.
-s Score cut off for the matrix. Defaults to zero.
Example: java -Xmx500M -jar pathTo/T2/Apps/ScoreSequences -g /my/affy/DmelSeqs.fasta
-t /my/affy/zeste.fasta
**************************************************************************************
**************************************************************************************
** Set Number Interval Maker: Feb 2007 **
**************************************************************************************
SNIM determines the threshold needed to create a set number of intervals for each score
index.
-n Number of intervals, single value or comma delimited list.
-s Particular score index to use, default is to scan all.
-i Multiply indexed window scores by -1. Useful for looking for reduced regions.
-o Minimum number of oligos in each window, defaults to 10.
-z Size of oligo, defaults to 25.
-m Max gap between starts of oligos for collapsing windows when scanning intervals,
defaults to 24.
-a Make intervals using found thresholds.
-f Full path file name for the Window[] array, if a directory is specified,
all files will be processed.
Example: java -Xmx500M -jar pathTo/T2/Apps/SetNumberIntervalMaker -f
/affy/res/zeste.res -n 600,1200,24000 -a
**************************************************************************************
**************************************************************************************
** Sgr2Bar: July 2008 **
**************************************************************************************
Converts xxx.sgr(.zip) files to chromosome specific bar files.
-f The full path directory/file name for your xxx.sgr(.zip) file(s).
-v Genome version (ie H_sapiens_Mar_2006, M_musculus_Jul_2007), get from UCSC Browser.
-s Strand, defaults to '.', use '+', or '-'
-t Graph file should be viewed as a stair step, defaults to bar
Example: java -Xmx1500M -jar pathTo/Apps/Sgr2Bar -f /affy/sgrFiles/ -s + -t
-v D_rerio_Jul_2006
**************************************************************************************
**************************************************************************************
** Synonym Matching: Jan 2008 **
**************************************************************************************
SM attempts to assign a standard name to each pick using synonym tables. For each name
in the picks file, it is used to fetch the associated synonyms and then SM attempts to
match it to a standard name or the alternative name. If a match is found, the original
pick and it's associated standard name are printed to screen.
-s The full path file name for a two column tab delimited text file containing the
standard name and an alternative name.
-a The full path file name for a multi column tab delimited text file containing
synonyms. All names on a line are considered synonyms.
-p The full path file name for a one column tab delimited text file containing select
names from the synonyms file.
Example: java -Xmx500M -jar pathTo/T2/Apps/SynonymMatching -s /Anno/zv7Stnd_GbNames.txt
-a /Anno/zv7AgilentSynonyms.txt -p /Data/upRegPicksAgilent.txt
**************************************************************************************
**************************************************************************************
** T2: April 2007 **
**************************************************************************************
T2 launches many of the TiMAT2 applications based on a tab delimited parameter file
(see T2_xxx/Documentation/t2ParamFileTemplate.xls). It converts, normalizes, splits,
and merges txt cel files and then launches ScanChromosomes. If you wish to make use
of a cluster for parallel processing, configure and test the JQSub TiMAT2 app.
-p Full path file name for the tab delimited parameter file.
-c Use the cluster(s) specified by JQSub.
Example: java pathTo/T2/Apps/T2 -c -p /my/t2ParamFile.txt
**************************************************************************************
**************************************************************************************
** TPMapOligoBlastFilter: Feb 2005 **
**************************************************************************************
TOBF takes an Affymetrix text bpmap file and BLASTs each oligo against a BLAST
database. Use formatdb on a complete genome.fasta file to create the database.
BOBF writes three files, one that assigns new coordinates to the oligo and replaces the
ori column with the number of 1bp mismatches and exact matches. A second file is
written containing oligos with only one exact match in the genome, the ori column is
replaced with the number of 1bp mismatches. The third contains oligos with no exact
match to the genome. The number of 1bp mismatches is assigned to the ori column.
This combo program is quite slow, 0.45sec
per oligo, so break up your tpmap file into separate files using the FileSplitter
program and farm the jobs out to a cluster. After processing, combine the files using
FileJoiner and sort using the TPMapSort program. If you don't care about 1bp mismatches
use MUMMERMapper, it should take about an hour.
Required Parameters:
-b Full path file name for text bpmap file.
-d Full path file name for BLAST database.
-s Full path file name for the blastall program.
Example: java -Xmx256M -jar pathTo/T2/Apps/TPMapOligoBlastFilter -b /affy/tpmap1 -s
/ncbi/bin/blastall -d /seq/dmel/whole_genome.fasta
**************************************************************************************
**************************************************************************************
** TPMap Processor: Aug 2006 **
**************************************************************************************
The TPMapProcessor takes a tpmap file, creates sets of oligos (Windows) to be used
in statistical analysis, and splits the tpmap file into separate chromosomes. The
entire output folder is needed by several other TiMAT2 applications. Save it!
Parameters:
-w The maximum length for a Window in bp, default 675.
-f Full path file name for the text tpmap file.
-n Minimum number of oligos, defaults to 1. Don't change if using the q-value
estimation in ScanChip! Minimal oligo requirements can be set later.
Example: java -Xmx1000M -jar pathTo/T2/Apps/TPMapProcessor -f /affy/tpmap.txt -w 500
**************************************************************************************
**************************************************************************************
** TPMap Sort: Jan 2005 **
**************************************************************************************
Sorts a text bpmap file based on chromosome and oligo start positions.
Required Parameters:
-f Full path file name for the text tpmap file.
Example: java -Xmx500M -jar pathTo/T2/Apps/TPMapSort -f /affy/tpmap.txt
**************************************************************************************


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