ACT Tool - Revision history

Justin.jee: /* Other */

2011-07-18T16:53:16Z

Other

← Older revision		Revision as of 16:53, 18 July 2011
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	* Citation		* Citation

	~~A paper describing this site and software is currently in preparation . Currently~~, ~~please just reference act~~.~~gersteinlab.org if you use the tool~~.		J Jee, J Rozowsky, KY Yip*, L Lochovsky, R Bjornson, G Zhong, Z Zhang, Y Fu, J Wang, Z Weng, M Gerstein. ACT: Aggregation and Correlation Toolbox for Analyses of Genome Tracks. (2011) Bioinformatics

	* RSeq Tools		* RSeq Tools

	RSeq Tools is a suite of tools which analyze RNA Seq experiment data--one of its functions is to generate a signal track from experimental data, which can be fed into ACT. RSeq Tools can be found here: http://archive.gersteinlab.org/proj/rnaseq/rseqtools/		RSeq Tools is a suite of tools which analyze RNA Seq experiment data--one of its functions is to generate a signal track from experimental data, which can be fed into ACT. RSeq Tools can be found here: http://archive.gersteinlab.org/proj/rnaseq/rseqtools/

Justin.jee at 20:51, 13 April 2011

2011-04-13T20:51:54Z

← Older revision		Revision as of 20:51, 13 April 2011
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	==corr-sat-bundle==		==corr-sat-bundle==

			NOTE: When the corr-sat-bundle package is downloaded from the website, in the file "config.txt" all lines except for chr22 are commented out. If you uncomment the lines for other chromosomes the program should work properly.

	*Correlation		*Correlation

Justin.jee at 16:40, 23 October 2010

2010-10-23T16:40:56Z

← Older revision		Revision as of 16:40, 23 October 2010
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	ACT is a toolbox for harvesting useful results from a vast sea of genomic experimental data. In particular, it is a set of scripts (Aggregation, Correlation, and Saturation) designed to be downloaded and used to analyze signal or hit tracks. These scripts, along with their supporting material (documentation, example files) can be accessed by clicking on their respective icons on the act.gersteinlab.org home page. The downloads are designed primarily with Unix/Linux users in mind. Details of what each script is designed to do, i.e. what files it takes in, what it outputs, and important notes, including dependencies, are discussed below.		ACT is a toolbox for harvesting useful results from a vast sea of genomic experimental data. In particular, it is a set of scripts (Aggregation, Correlation, and Saturation) designed to be downloaded and used to analyze signal or hit tracks. These scripts, along with their supporting material (documentation, example files) can be accessed by clicking on their respective icons on the act.gersteinlab.org home page. The downloads are designed primarily with Unix/Linux users in mind. Details of what each script is designed to do, i.e. what files it takes in, what it outputs, and important notes, including dependencies, are discussed below.

	There are also several supporting features such as a [[ActGallery\|gallery]] ~~and~~ [[ACT_IntegratedExample\|integrated example]] which uses whole-genome ChIP-Seq data.		There are also several supporting features such as a [[ActGallery\|gallery]], [[ACT_IntegratedExample\|integrated example]] which uses whole-genome ChIP-Seq data, and the [[ACT_FAQ\|FAQ's page]].

	General contact: jjmg [at] gersteinlab.org		General contact: jjmg [at] gersteinlab.org

Justin.jee: /* agg-py */

2010-10-23T16:23:12Z

agg-py

← Older revision		Revision as of 16:23, 23 October 2010
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	Update: no longer requires numpy to run		Update: no longer requires numpy to run

			Update: Please see 'testrun.sh' in the samples folder for an example of how to link the text output from agg-py to the provided R script which generates graphical output.

	==corr-p==		==corr-p==

Justin.jee: /* agg-py */

2010-10-10T08:32:17Z

agg-py

← Older revision		Revision as of 08:32, 10 October 2010
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	chr1 40 5.0		chr1 40 5.0
	</pre>		</pre>

	If probewidth is say 5, these two lines are read to mean that locations 10 through 15 on chromosome 1 have a signal of 2.3, and locations 40 through 45 have signal of 5.0. If probewidth is left blank, these two lines are read to mean that locations 10 through 40 have a signal of 2.3. These two options would require one to select the “sgr” format. The “wiggle” format allows one to upload a file containing four columns: one for chromosome, one for start position, one for stop position, and one for intensity, respectively. The “density” option allows one to upload a two column file containing chromosome and location. The program will assume that all points’s have a width of 1 and will calculate the number of probes that fall into the various bins, rather than the average intensity.

	[[Image:Nbins.bmp\|thumb\|nbins upstream and downstream of a site]]		[[Image:Nbins.bmp\|thumb\|nbins upstream and downstream of a site]]
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	[[Image:Mbins.bmp\|thumb\|mbins between start and stop sites]]		[[Image:Mbins.bmp\|thumb\|mbins between start and stop sites]]

	~~Number of bins (n)~~. Specifies how many bins will be in each flanking region (for a total of 2xn bins). For jobs that do not use scaled bins in the region between start and stop sites, there will be 2xn bins: n bins on each side of the start site.		--nbins. Specifies how many bins will be in each flanking region (for a total of 2xn bins). For jobs that do not use scaled bins in the region between start and stop sites, there will be 2xn bins: n bins on each side of the start site.

	~~Number of bins (m)~~. Optional ~~field to be defined if~~ stop and ~~start sites (regions) are~~ to ~~be considered in the aggregation process. Defines the~~ number of bins ~~assigned to~~ the ~~region between~~ start and stop ~~sites (~~for a ~~total number of m+2n bins)~~.		--regions. Optional tag. If included, the program will aggregate "radius" base pairs upstream of the start site, downstream of the stop site, AND the "region" between the start and stop site. The way it will do this is to create "mbins" number of bins between the start and stop site for each segment in the annotations file, and scale the bins based on the size of the segment. Note that, as a result, mbins must be smaller than the smallest segment in the annotation file.

	~~Length~~ of ~~flanking~~ region. Specifies number of base pairs the program should analyze in both directions from either the start site or the start/stop pair, depending on the option specified.		--mbins. Optional field to be defined if stop and start sites (regions) are to be considered in the aggregation process. Defines the number of bins assigned to the region between start and stop sites (for a total number of m+2n bins).

	~~Probe width. For signal files that~~ present ~~data for probes~~ of ~~a specific width (see Signal file above for use instructions~~).		--radius. Specifies number of base pairs the program should analyze in both directions from either the start site or the start/stop pair, depending on the option specified. (in the output, the program will present radius base pairs divided into nbins upstream and downstream of start sites or upstream of start sites and downstream of stop sites, depending on if --regions is used or not)

	~~Minimum gene length~~: Tells the program not to consider annotations for start/stop pairs that are shorter than a certain distance.		--mingenelen: Tells the program not to consider annotations for start/stop pairs that are shorter than a certain distance.

	~~Include intergenic regions. Tells the program to scale bins inside the start/stop sites, or tells program to analyze in separate directions from the start site if unchecked.~~		--mean. The program will automatically ensure that only the median signal of an individual gene (or number of probes, if the density option is selected) contributes to the final “averaging” calculation in each bin to avoid bias against shorter genes. If use mean is selected, the program will take the mean signal across all genes per bin and report the final result. Otherwise, it will use the median signal (of the median signal of each gene).

	~~Use~~ mean. The program will automatically ensure that only the median signal of an individual gene (or number of probes, if the density option is selected) contributes to the final “averaging” calculation in each bin to avoid bias against shorter genes. If use mean is selected, the program will take the mean signal across all genes per bin and report the final result. Otherwise, it will use the median signal (of the median signal of each gene).

	*Specific use instructions		*Specific use instructions

Justin.jee: /* agg-py */

2010-10-07T23:07:56Z

agg-py

← Older revision		Revision as of 23:07, 7 October 2010
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	*Specific use instructions		*Specific use instructions

	Accepted file input types: .sgr~~, .wig~~ (for signal track); abbreviated .bed file (for annotation track)		Accepted file input types: .sgr (for signal track); abbreviated .bed file (for annotation track)
			Note: .wig signal tracks accepted in Web ACT only.

	After unzipping, the main source file (which includes a detailed header with use instructions) is ACT.py. A makefile with example runs can be found in Agg/samples/Makefile		After unzipping, the main source file (which includes a detailed header with use instructions) is ACT.py. A makefile with example runs can be found in Agg/samples/Makefile

Mbg at 23:34, 18 September 2010

2010-09-18T23:34:11Z

← Older revision		Revision as of 23:34, 18 September 2010
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	Here is some information on ~~'''http://act.gersteinlab.org''' (~~'''A'''ggregation & '''C'''orrelation '''T'''oolbox)		Here is some information on the '''A'''ggregation & '''C'''orrelation '''T'''oolbox ([http://ACT.gersteinlab.org '''ACT'''.gersteinlab.org])

	==Overview==		==Overview==

Justin.jee: /* agg-py */

2010-09-11T17:48:24Z

agg-py

← Older revision		Revision as of 17:48, 11 September 2010
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	==agg-py==		==agg-py==

	The aggregation script agg-p takes values from multiple points on a single genomic signal track and creates an average signal profile around a set of anchor points, such as Transcription Start Sites (TSS's).		The aggregation script agg-py takes values from multiple points on a single genomic signal track and creates an average signal profile around a set of anchor points, such as Transcription Start Sites (TSS's).

	The main download is written in Python. Each run takes two input files: a signal or hit track (in the form of an sgr file or point file), and an annotations file in bed format. The output is a columnar file with explanatory headers--the files can be plotted in programs like gnuplot, excel, or matlab. The main download package has an R script in the samples folder which shows one way of plotting the output data with error bars.		The main download is written in Python. Each run takes two input files: a signal or hit track (in the form of an sgr file or point file), and an annotations file in bed format. The output is a columnar file with explanatory headers--the files can be plotted in programs like gnuplot, excel, or matlab. The main download package has an R script in the samples folder which shows one way of plotting the output data with error bars.

Justin.jee: /* agg-p */

2010-09-11T17:46:48Z

agg-p

← Older revision		Revision as of 17:46, 11 September 2010
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	General contact: jjmg [at] gersteinlab.org		General contact: jjmg [at] gersteinlab.org

	==agg-p==		==agg-py==

	The aggregation script agg-p takes values from multiple points on a single genomic signal track and creates an average signal profile around a set of anchor points, such as Transcription Start Sites (TSS's).		The aggregation script agg-p takes values from multiple points on a single genomic signal track and creates an average signal profile around a set of anchor points, such as Transcription Start Sites (TSS's).

Justin.jee: /* corr-sat bundle */

2010-09-10T18:48:15Z

corr-sat bundle

← Older revision		Revision as of 18:48, 10 September 2010
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	A specific example run can be found in EXAMPLE.txt		A specific example run can be found in EXAMPLE.txt

	==corr-sat bundle==		==corr-sat-bundle==

	*Correlation		*Correlation