GADEM: Motif Analysis with rGADEM
In CharlesJB/mpiGadem: de novo motif discovery

Description Usage Arguments Author(s) Examples

It is an R implementation of GADEM, a powerful computational tools for de novo motif discovery.

GADEM(Sequences,seed=1,genome=NULL,verbose=FALSE,numWordGroup=3,numTop3mer=20,
       numTop4mer=40,numTop5mer=60,numGeneration=5,populationSize=100,
       pValue=0.0002,eValue=0.0,extTrim=1,minSpaceWidth=0,maxSpaceWidth=10,
       useChIPscore=0,numEM=40,fEM=0.5,widthWt=80,fullScan=0,slideWinPWM=6,
       stopCriterion=1,numBackgSets=10,weightType=0,
       bFileName="NULL",Spwm="NULL",minSites =-1,maskR=0,nmotifs=25)

`Sequences`	Sequences from BED or FASTA file are converted into XString object view
`seed`	When a seed is specified, the run results are deterministic
`genome`	Specify the genome
`verbose`	Print immediate results on screen [TRUE-yes (default), FALSE-no]. These results include the motif consensus sequence, number of sites (in sequences subjected to EM optimization, see -fEM, above), and ln(E-value).
`numWordGroup`	number of non-zero k-mer groups
`numTop3mer`	Number of top-ranked trimers for spaced dyads (default: 20).
`numTop4mer`	Number of top-ranked tetramers for spaced dyads (default: 40).
`numTop5mer`	Number of top-ranked pentamers for spaced dyads (default: 60).
`numGeneration`	Number of genetic algorithm (GA) generations (default: 5).
`populationSize`	GA population size (default: 100). Both default settings should work well for most datasets (ChIP-chip and ChIP-seq). The above two arguments are ignored in a seeded analysis, because spaced dyads and GA are no longer needed (numGeneration is set to 1 and populationSize is set to 10 internally, corresponding to the 10 maxp choices).
`pValue`	P-value cutoff for declaring BINDING SITES (default: 0.0002). Depending on data size and the motif, you might want to assess more than one value. For ChIP-seq data (e.g., 10 thousand +/-200-bp max-center peak cores), p=0.0002 often seems appropriate. However, short motifs may require a less stringent setting.
`eValue`	ln(E-value) cutoff for selecting MOTIFS (default: 0.0). If a seeded analysis fails to identify the expected motif, run GADEM with -verbose 1 to show motif ln(E-value)s on screen, then rerun with a larger ln(E-value) cutoff. This can help in identifying short and/or low abundance motifs, for which the default E-value threshold may be too low.
`extTrim`	Base extension and trimming (1 -yes, 0 -no) (default: 1).
`minSpaceWidth`	Minimal number of unspecified nucleotides in spaced dyads (default: 0).
`maxSpaceWidth`	Maximal number of unspecified nucleotides in spaced dyads (default: 10). minSpaceWidth and maxSpaceWidth control the lengths of spaced dyads, and, with exTrim, control motif lengths. Longer motifs can be discovered by setting maxSpaceWidth to larger values (e.g. 50).
`useChIPscore`	Use top-scoring sequences for deriving PWMs. Sequence (quality) scores are stored in sequence header (see documentation). 0 - no (default, randomly select sequences), 1 - yes.
`numEM`	Number of EM steps (default: 40). One might want to set it to a larger value (e.g. 80) in a seeded run, because such runs are fast.
`fEM`	Fraction of sequences used in EM to obtain PWMs in an unseeded analysis (default: 0.5). For unseeded motif discovery in a large dataset (e.g. >10 million nt), one might want to set -fEM to a smaller value (e.g., 0.3 or 0.4) to reduce run time.
`widthWt`	For -posWt 1 or 3, width of central sequence region with large EM weights for PWM optimization (default: 50). This argument is ignored when weightType is 0 (uniform prior) or 2 (Gaussian prior).
`fullScan`	GADEM keeps two copies of the input sequences internally: one (D) for discovering PWMs and one (S) for scanning for binding sites using the PWMs Once a motif is identified, its instances in set D are always masked by Ns. However, masking motif instances in set S is optional, and scanning unmasked sequences allows sites of discovered motifs to overlap.
`slideWinPWM`	sliding window for comparing pwm similarity (default : 6).
`stopCriterion`	Number of generations without new motifs before stopping analysis.
`numBackgSets`	Number of sets of background sequences (default: 10). The background sequences are simulated using the [a,c,g,t] frequencies in the input sequences, with length matched between the two sets. The background sequences are used as the random sequences for assessing motif enrichment in the input data.
`weightType`	Weight profile for positions on the sequence. 0 - no weight (uniform spatial prior, default), 1 (gaussian prior) and 2 (triangle prior) - small or zero weights for the ends and large weights for the center (e.g. the center 50 bp). Consider using 1 or 2 if you expect strong central enrichment (as in ChIP-seq) and your sequences are long(e.g. >200 bp).
`bFileName`	Reading user-specified background models.
`Spwm`	File name for the seed PWM, when a seeded approach is used. can be used as the starting PWM for the EM algorithm. This will help find an expected motif and is much faster than unseeded de novo discovery.Also, when a seed PWM is specified, the run results are deterministic, so only a single run is needed (repeat runs with the same settings will give identical results). In contrast, unseeded runs are stochastic, and we recommend comparing results from several repeat runs.
`minSites`	Minimal number of sites required for a motif to be reported (default: numSeq/20)
`maskR`	Mask low-complexity sequences or repeats; 'aaaaaaaa', 'tttttttt', 'cacacaca', 'tgtgtgtg', 'tatatatat', 'ggaggaggagga','gaggaggaggag','agaagaagaaga','ctcctcctcctc','tcctcctcctcc','tcttcttcttct','tagtagtagtag','aataataataat','attattattatt','ataataataata' or 'cagcagcagcagcag' (default: 0-no masking,1-masking)
`nmotifs`	Number of motifs sought (default: 25)

Arnaud Droit arnaud.droit@crchuq.ulaval.ca

	library(BSgenome.Hsapiens.UCSC.hg18)
	pwd<-"" #INPUT FILES- BedFiles, FASTA, etc.
	path<- system.file("extdata","Test_100.bed",package="rGADEM")
	BedFile<-paste(pwd,path,sep="")
	BED<-read.table(BedFile,header=FALSE,sep="\t")
	BED<-data.frame(chr=as.factor(BED[,1]),start=as.numeric(BED[,2]),end=as.numeric(BED[,3]))
	#Create RD files 
	rgBED<-IRanges(start=BED[,2],end=BED[,3])
	Sequences<-RangedData(rgBED,space=BED[,1])
	
	
gadem<-GADEM(Sequences,verbose=1,genome=Hsapiens)