## I am firstly downloading upstream sequences using BSGenome and the latest human sequence build ##
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg19)
ls('package:BSgenome.Hsapiens.UCSC.hg19')
## this creates subject a so-called DNAStringSet of upstream gene sequences i.e. promoters##
subject=Hsapiens$upstream1000
 
## I use biomaRt here to pull out the annotation data for the refseq DNAs that are extracted from
## the BSGenome upstream sequences- especially the familiar HGNC symbol
 
mart <- useMart('ensembl')
mart<-useDataset("hsapiens_gene_ensembl",mart)
atts=c('chromosome_name', 'start_position', 'strand', 'hgnc_symbol', 'ens_hs_gene', 'refseq_dna')
 
## this gobbledeygook just extracts a name string splits it into a list 
## then gets the first element (the refseq) back and unlists it to vector nm
nm=unlist(lapply(strsplit(names(subject), split='_up'), function(x)x[1]))
gb= getBM(attributes=atts, filters='refseq_dna', values=nm, mart=mart)
 
## NB Lookout! biomaRt neither returns answers (i.e rows) for all the query values (refseqs)
## nor does it return them in the same order as the query (they are alphabetic) 
## -- so you cant just reorder results and use the same index
 
## match loks for a first match this gets all matches OFC
match.all=function(query, set)
	{
	wh=vector()
	for(i in query) 
		wh=c(wh, which(set==i))
	return(wh)	
	}
 
## these are genes of particular interest TO ME- ie. gene symbols
my_genes=c('PROX1', 'FOXC2', 'SOX18', 'ANPEP', 'NR2F2')
 
## so for the right index we go hgnc -> refseq -> position of refseq in BioStringSet
my_refseqs=gb$refseq[match.all(my_genes, gb$hgnc_symbol)]
my_refseqs_index=match.all(my_refseqs, nm)
 
## I make a smaller subest of interesting gene promoters 'subject2'
subject2=subject[my_refseqs_index]
 
 
## the query string and its reverse complement - for now: will experiment later - ##
CMARE = DNAString('TGCTGACGTCAGCA')
CMARE_complement = complement(CMARE)
TRE= DNAString('TGACTCA')
TRE_complement= complement(TRE)
CRE= DNAString('TGACGTCA')
CRE_complement= complement(TRE)
TMARE=DNAString('TGCTGACTCAGCA')
TMARE_complement=complement(TMARE)
 
## this is a Degenerate MARE where IUPAC DNA code N is any base
DMARE=DNAString('TGCNNNNNNNNGCA')
DMARE_complement=complement(DMARE)
 
## I am using the IUPAC DNA code 'W' for A/T there are 3 'W' but this is just an approx guess from the lit.
## hence the name ATMARE
ATMARE=DNAString('WWWTGCTGAC')
ATMARE_complement=complement(ATMARE)
 
## make  big NAMED list of these DNAStrings
DNASTRLIST=list(CMARE=CMARE, CMARE_complement=CMARE_complement, 
TRE=TRE, TRE_complement=TRE_complement,
CRE=CRE, CRE_complement=CRE_complement,
TMARE=TMARE, TMARE_complement=TMARE_complement,
DMARE=DMARE, DMARE_complement=DMARE_complement,
ATMARE=ATMARE, ATMARE_complement=ATMARE_complement)
 
 
 
 
 
## extract the accession name with a bit of messing about ##
nm=names(subject)
nm=strsplit(nm, '_up')
nm=unlist(lapply(nm, function(x)x[1]))
 
 
matching=function(myDNAString, subject, max.mismatch=2)
{
 
 
vmatch=vmatchPattern(myDNAString, subject, max.mismatch=max.mismatch)
wh=which(countIndex(vmatch)>0)
 
for(i in wh)
	{
	if(exists('match_list'))
		{
		match_list=c(match_list, list(Views(subject[[i]], vmatch[[i]])))
		names(match_list)[length(match_list)]=names(subject[i])
		}
	if(!exists('match_list'))
		{
		match_list= list(Views(subject[[i]], vmatch[[i]]))
		names(match_list)[length(match_list)]=names(subject[i])
		}
 
	}
return(match_list)
}
 
 
lapply(DNASTRLIST, function(eachMARE) matching(eachMARE, subject2))

> test[3]
$TRE
$TRE$CMARE
MIndex object of length 8
 
$TRE$CMARE_complement
MIndex object of length 8
 
$TRE$TRE
MIndex object of length 8
 
$TRE$TRE_complement
MIndex object of length 8
 
$TRE$CRE
MIndex object of length 8
 
$TRE$CRE_complement
MIndex object of length 8
 
$TRE$TMARE
MIndex object of length 8
 
$TRE$TMARE_complement
MIndex object of length 8
 
$TRE$DMARE
MIndex object of length 8
 
$TRE$DMARE_complement
MIndex object of length 8
 
$TRE$ATMARE
MIndex object of length 8
 
$TRE$ATMARE_complement
MIndex object of length 8
 
$TRE$`NM_002763_up_1000_chr1_214160860_f chr1:214160860-214161859`
  Views on a 1000-letter DNAString subject
subject: CGGCGAACTCGATCAGCTGTATCTGGGAAATGAAAAAAGAAAAAGAAAAAAAAAA...GTCCCACTGCTCCCTGCACCGCGTAAGTATCTTCTTCTTCCCCTCGTGAGTCCCT
views:
    start end width
[1]   136 142     7 [TGGCTCG]
[2]   333 339     7 [TGAATCC]
[3]   420 426     7 [TGTCTTA]
[4]   789 795     7 [TGTCGCA]
[5]   797 803     7 [AGACTCA]
[6]   992 998     7 [TGAGTCC]
 
$TRE$`NM_005251_up_1000_chr16_86599857_f chr16:86599857-86600856`
  Views on a 1000-letter DNAString subject
subject: CCTCTAAAACCTCGATAGGTTATCCTTGACGACCCCGAGCCTGGAAACTCCCTGT...GCCAGGAGCCCGGGGCCGCCCCTCCCGCTCCCCTCCTCTCCCCCTCTGGCTCTCT
views:
     start end width
 [1]    59  65     7 [TGATTAA]
 [2]    71  77     7 [TGATTAA]
 [3]   131 137     7 [TGAATCC]
 [4]   184 190     7 [TCACTCA]
 [5]   204 210     7 [TGTCCCA]
 [6]   249 255     7 [TTACTCT]
 [7]   485 491     7 [CGACTCC]
 [8]   510 516     7 [TGGCTCA]
 [9]   622 628     7 [GGGCTCA]
[10]   658 664     7 [TGACCCT]
[11]   992 998     7 [TGGCTCT]
 
$TRE$`NM_018419_up_1000_chr20_62680980_r chr20:62680980-62681979`
  Views on a 1000-letter DNAString subject
subject: CCTTCTCCAGTAGAACCATGCGTGGCAGTGGGAGGGGAGGACCTCCAGAGGGAGA...GCCCTGAGCCGCTATATCTGGGCGCCCGCCCGGCCCGAGGCCACCGCCGTCCCCA
views:
     start end width
 [1]   186 192     7 [GGACCCA]
 [2]   202 208     7 [TGCCTCT]
 [3]   374 380     7 [TGTGTCA]
 [4]   431 437     7 [TGTCCCA]
 [5]   443 449     7 [GCACTCA]
 [6]   469 475     7 [TTACACA]
 [7]   509 515     7 [GGACTCA]
 [8]   567 573     7 [GGACTTA]
 [9]   712 718     7 [CGACTCC]
[10]   849 855     7 [GGCCTCA]
 
and so on and on.....