The simplest patterns that you can have in DNA are words. It can be useful to carry out a statistical analysis of the different word lengths in DNA to see if there is a particular skew in their distribution and to determine which evolutionary models are most likely to apply to a particular set of sequences. This can be important if an organism has an unusual distribution of nucleotides.
The core R table function will summarise the total numbers of the nucleotides. To look for words you use the count function.
library ("seqinr" )<- read.fasta ("dnafasta.fas" )table (seq)
LEGACY
a c g t
238 265 228 190
<- as.data.frame (seq)count (seq$ LEGACY, 1 )<- count (seq$ LEGACY, 2 )
aa ac ag at ca cc cg ct ga gc gg gt ta tc tg tt
83 67 46 41 65 75 69 56 56 74 52 46 33 49 61 47
<- count (seq$ LEGACY, 3 )
aaa aac aag aat aca acc acg act aga agc agg agt ata atc atg att caa cac cag cat
33 28 16 5 13 27 14 13 6 21 11 8 5 17 12 7 17 14 25 9
cca ccc ccg cct cga cgc cgg cgt cta ctc ctg ctt gaa gac gag gat gca gcc gcg gct
24 17 22 12 19 15 20 15 5 14 26 11 22 15 2 17 16 13 24 21
gga ggc ggg ggt gta gtc gtg gtt taa tac tag tat tca tcc tcg tct tga tgc tgg tgt
9 21 7 15 8 9 11 18 11 10 3 9 12 18 9 10 22 17 14 8
tta ttc ttg ttt
15 9 12 11
<- count (seq$ LEGACY, 4 )
aaaa aaac aaag aaat aaca aacc aacg aact aaga aagc aagg aagt aata aatc aatg aatt
14 10 8 1 8 3 6 11 2 4 6 4 2 1 1 1
acaa acac acag acat acca accc accg acct acga acgc acgg acgt acta actc actg actt
4 5 1 3 9 8 7 3 3 4 4 3 1 3 8 1
agaa agac agag agat agca agcc agcg agct agga aggc aggg aggt agta agtc agtg agtt
2 2 0 2 5 5 7 4 2 4 3 2 0 1 5 2
ataa atac atag atat atca atcc atcg atct atga atgc atgg atgt atta attc attg attt
2 2 0 1 7 3 3 4 5 3 1 3 1 2 1 3
caaa caac caag caat caca cacc cacg cact caga cagc cagg cagt cata catc catg catt
6 7 3 1 0 11 3 0 4 15 3 3 1 5 1 2
ccaa ccac ccag ccat ccca cccc cccg ccct ccga ccgc ccgg ccgt ccta cctc cctg cctt
4 4 13 3 5 3 7 2 5 4 12 1 2 2 4 4
cgaa cgac cgag cgat cgca cgcc cgcg cgct cgga cggc cggg cggt cgta cgtc cgtg cgtt
5 6 1 7 1 4 4 6 3 10 0 7 2 4 3 6
ctaa ctac ctag ctat ctca ctcc ctcg ctct ctga ctgc ctgg ctgt ctta cttc cttg cttt
2 2 0 1 3 7 3 1 9 8 6 3 2 2 4 3
gaaa gaac gaag gaat gaca gacc gacg gact gaga gagc gagg gagt gata gatc gatg gatt
8 8 4 2 4 8 3 0 0 1 0 1 2 8 6 1
gcaa gcac gcag gcat gcca gccc gccg gcct gcga gcgc gcgg gcgt gcta gctc gctg gctt
6 3 6 1 4 1 3 5 7 6 2 9 1 7 10 3
ggaa ggac ggag ggat ggca ggcc ggcg ggct ggga gggc gggg gggt ggta ggtc ggtg ggtt
5 2 0 2 7 1 9 4 1 2 2 2 5 3 2 5
gtaa gtac gtag gtat gtca gtcc gtcg gtct gtga gtgc gtgg gtgt gtta gttc gttg gttt
4 2 2 0 1 3 3 2 5 2 2 2 7 4 5 2
taaa taac taag taat taca tacc tacg tact taga tagc tagg tagt tata tatc tatg tatt
5 3 1 1 1 5 2 2 0 1 2 0 0 3 3 3
tcaa tcac tcag tcat tcca tccc tccg tcct tcga tcgc tcgg tcgt tcta tctc tctg tctt
3 2 5 2 6 5 5 2 4 1 2 2 1 2 4 3
tgaa tgac tgag tgat tgca tgcc tgcg tgct tgga tggc tggg tggt tgta tgtc tgtg tgtt
10 5 1 6 3 3 4 7 3 5 2 4 1 1 1 5
ttaa ttac ttag ttat ttca ttcc ttcg ttct ttga ttgc ttgg ttgt ttta tttc tttg tttt
3 4 1 7 1 5 0 3 3 4 5 0 5 1 2 3
As tabulated data it is hard to see any differences. It is much easier with a graphical display of the data.
The TA and AT pairings are under-represented compared to the others and this is what you expect for a gene sequence which is GC rich. The T nucleotide is under-represented overall.
