1) The document discusses using the Biostrings package in R to analyze DNA sequences of the Makorin1-p1 pseudogene from different Mus musculus subspecies, calculating the p-distance between sequences in regions B and C. 2) A phylogenetic tree shows the evolutionary relationships between different M. musculus subspecies and related species. Makorin1-p1 is found to have an ortholog in rats. 3) The methodology describes plans to use the Biostrings package to extract region B and region C sequences from a Makorin1-p1.fasta file for different M. musculus subspecies, and calculate p

1. 2010/06/24
kaneko.satoko(at)ocha.ac.jp

2.
Bioconductor(Biostrings)
(p distance)

3. Bioconductor Biostrings
Biostrings
> source("h>p://www.bioconductor.org/biocLite.R")
> biocLite(“Biostrings”) # 1
> library(Biostrings) # R

4. Bioconductor/Biostrings 1
> ls(“package:Biostrings”) #Biostrings
> x <‐ "CGTACGTAGTAGCTAGCTAGCTAGCTAGCTGATCGATGCTAGCTGATCGATGCT"
> DNAString(x) #DNA
54‐le>er "DNAString" instance
seq: CGTACGTAGTAGCTAGCTAGCTAGCTAGCTGATCGATGCTAGCTGATCGATGCT
> s <‐ DNAString(x) # s DNA (x)
> length(s)
[1] 54 # s DNA 54
> length(x)
[1] 1 # x 1

5. Bioconductor/Biostrings 2
54‐le>er "DNAString" instance
seq: CGTACGTAGTAGCTAGCTAGCTAGCTAGCTGATCGATGCTAGCTGATCGATGCT
> alphabetFrequency(s, baseOnly=TRUE) #
A C G T other
[1,] 12 12 15 15 0
> reverseComplement(s) #
54‐le>er "DNAString" instance
seq: AGCATCGATCAGCTAGCATCGATCAGCTAGCTAGCTAGCTAGCTACTACGTACG
> dna2rna(s) #RNA (T ‐>U)
54‐le>er "RNAString" instance
seq: CGUACGUAGUAGCUAGCUAGCUAGCUAGCUGAUCGAUGCUAGCUGAUCGAUGCU

6. Bioconductor/Biostrings 3
54‐le>er "DNAString" instance
seq: CGTACGTAGTAGCTAGCTAGCTAGCTAGCTGATCGATGCTAGCTGATCGATGCT
> m1 <‐ matchPa>ern(“GCTA”, s) #
> m1
Views on a 54‐le>er DNAString subject
subject: CGTACGTAGTAGCTAGCTAGCTAGCTAGCTGATCGATGCTAGCTGATCGATGCT
views:
start end width
[1] 12 15 4 [GCTA]
[2] 16 19 4 [GCTA]
[3] 20 23 4 [GCTA]
[4] 24 27 4 [GCTA]
[5] 38 41 4 [GCTA]

7. 1
Makorin1
22.61kb
(CDS:1446bp)
Makorin1‐p1
1592bp
region A region B region C
Makorin1‐p1 Makorin1 processed pseudogene
Makorin1‐p1 regionB Makorin1 regionB
Makorin1 mRNA regionB mRNA
regionC
regionB
regionC
regionB
regionB regionC

8. 1
Makorin1
22.61kb
(CDS:1446bp)
Makorin1‐p1
1592bp
region A region B region C
1) Makorin1‐p1 Makorin1
2) Makorin1 mRNA
Makorin1‐p1 Makorin1 Makorin1‐p1
Makorin1
3) Makorin1 mRNA
Makorin1‐p1 (regionB
)
regionB regionC

9. – 1‐
(Null hypothesis)

10. – 2‐
2
False negagve False posigve

11. 2
Makorin1‐p1
Makorin1‐p1 ortholog rat
Makorin1‐p1
Mus musculus domes3cus 5
Subgenus
M. booduga
M. fragilicauda India+Lao/Thai
booduga
M. terricolor
1.5 M. macedonicus
4.3 mya M. spicilegus
mya
M. spretus
M. musculus castaneus
Palearctic
Mus
musculus
M. m. domesticus
M. m. molossinus
M. caroli
M. cookii Southeast Asia
M. cervicolor cervicolor
M. pahari Coelomys
M. mattheyi Nannomys
M. platythrix Pyromys
Apodemus agrarius
Micromys minutus
Rattus norvegicus (from Suzuki et al. 2004 Mol. Phylogenet. Evol.
0.01 33:626-646, Figure 1, 4.)

12. 3
Makorin1‐p1.fasta
Mus musculus domes3cus
dom
[Macintosh HD/ /tg03/bin]
Mus musculus molossinus
mol
Mus musculus castaneus
cas
Makorin1‐p1.fasta
Mus musculus musculus
mus
regionB 1‐617, regionC 618‐1256
Mus spretus
spr
Mus caroli
car
p distance
2 (number of differences)/
alignment
dom CCTGCCCCAA ATGTCAGATC ACATCTCACT TTGTCATTCC AAGTAATCAC TGGGTGGAGT
spr1 .......... ...C...... .......... .......... ......GT.. ..........
car1 .......... ...C.GA... ......A... ..T....... ....G.GT.. .........G
dom‐spr1: 3/60 = 0.05
dom‐car1: 9/60 = 0.15
spr1‐car1: 6/60 = 0.10

13. region B region C
regionB regionC number of differences p distance
region B bp
region C bp
pair
number of differences
p distance
number of differences
p distance
dom – mol
dom – cas
dom – mus
dom – spr
dom – car
mol ‐ cas
mol – mus
mol – spr
mol – car
cas – mus
cas – spr
cas – car
mus – spr
mus – car
spr – car

14. region B region C
Makorin1‐p1 (region B, regionC)
p distance
1) domesgcus Biostrings DNAstring
2) B
3) B
4) C
5) C
6) domesgcus DNAstring B C
7)
8) 4

15. Biostrings p distance 1
library(Biostrings) #R
#Makorin1‐p1.fasta domesgcus ””
# DNA dom
> dom <‐ DNAString("")
#dom 1 617 domB
> domB <‐ substring(dom,1,617)
#domB lengthB (p distance )
> lengthB <‐ length(domB)
#dom 618 1256 domC
> domC <‐ substring(dom, 618,1256)
#domC lengthC (p distance )
> lengthC <‐ length(domC)
#
# lengthB
> lengthB
[1] 617

16. Biostrings p distance 2
#Makorin1‐p1.fasta molossinus ””
# DNA mol
> mol <‐ DNAString("")
#mol 1 617 molB
> molB <‐ substring(mol,1,617)
#mol 618 1256 molC
> molC <‐ substring(mol, 618,1256)
# castaneus(cas), musculus(mus), spretus(spr), caroli(car)
# number of differences p distance
#

17. Biostrings p distance 3
> x <‐ domB
> y <‐ molB
# x y( domB molB)
> comp<‐ c(compareStrings(x,y))
# ? ?
> subt <‐ gsub("(['?'])", "", comp)
#subt DNA ide
> ide <‐ DNAString(subt)
#ide len
> len <‐ length(ide)
#x y dif
> dif <‐ (lengthB – len )
> dif #x y
#regionB p distance
> pdis <‐ dif/lengthB
> pdis #p distance 4

18. Biostrings p distance 4
CotEditor pdistanceB.R
[Macintosh HD/ /tg03/bin]
comp <‐ c(compareStrings(x,y))
2 lengthB lengthC
subt <‐ gsub("(['?'])", "", comp)
pdistanceC.R bin
ide <‐ DNAString(subt)
len <‐ length(ide)
dif <‐ (lengthB ‐ len)
pdis <‐ dif/lengthB
x y
> x <‐
> y <‐
> source("/Users/tg03/bin/pdistanceB.R")
bin
> source("pdistanceB.R")
2
> dif
> pdis

19. region B region C ( )
region B 617 bp
region C 639 bp
pair
number of differences
p distance
number of differences
p distance
dom – mol
6
0.010
7 0.011
dom – cas
6
0.010
7
0.011
dom – mus
8
0.013
8
0.013
dom – spr
16
0.026
14
0.022
dom – car
30
0.049
39
0.061
mol – cas
0
0
0
0
mol – mus
4
0.006
1
0.002
mol – spr
14
0.023
17
0.027
mol – car
28
0.045
38
0.059
cas – mus
4
0.006
1
0.002
cas – spr
14
0.023
17
0.027
cas – car
28
0.045
38
0.059
mus – spr
14
0.023
18
0.028
mus – car
28
0.045
39
0.061
spr – car
32
0.052
37
0.058

20. 1
1) regionB p distance x x
> x <‐ c(x )
2) regionC p distance y y
> y <‐ c(y )
3) x y
4) plot()
> plot(x,y,xlim=c( , ), ylim=c( , ))

21. 1
1) regionB p distance x x
> x <‐ c(0.010, 0.010, 0.013, 0.026, 0.049, 0, 0.006 ,0.023, 0.045, 0.006 , 0.023 , 0.045,
0.023, 0.045, 0.052 )
2) regionC p distance y y
> y <‐ c(0.011, 0.011, 0.013, 0.022, 0.061, 0, 0.002, 0.027, 0.059, 0.002, 0.027, 0.059,
0.028, 0.061, 0.058 )
3) max() x y
> max(x)
[1] 0.052
> max(y)
[1] 0.061
4) plot()
> plot(x,y,xlim=c(0,0.065), ylim=c(0,0.065))

22. 2
4’)
> plot(x,y,xlab='regionB',ylab='regionC', xlim=c(0,0.065), ylim=c(0,0.065))
regionB regionC p distance
regionB regionC

23. 1
(d)
(x3,y3)
d3
d4
(x1,y1)
(x4,y4)
d1
d2
(x2,y2)

24. 2
> xdev <‐ (x‐mean(x)) # x
> ydev <‐ (y‐mean(y)) # y
> bmul<‐ xdev*ydev # x y
> bnum <‐ sum(bmul) # x y ( )
> bsqu <‐ xdev^2 # x 2
> bden <‐ sum(bsqu) # x 2 ( )
> b <‐ bnum/bden # ( )
> b
[1] 1.317939

25. 3
> a1 <‐ sum(y)/length(y)
> a2 <‐ b*(sum(x)/length(x))
> a <‐ a1‐a2
[1] ‐0.003636326
> abline (a,b)
#a b y = a + bx

26. regionB regionC y=x
regionB regionC
y=‐0.0036+1.3x
(y=‐0.0036+1.3x y=x )
regionB regionC

27.
p distance 1
‐pdis_line.R‐
library("Biostrings");
x <‐""
dom <‐ "[domesgcus ]";
mol <‐ "[molossinus ]";
cas <‐ "[castaneus ]";
mus <‐ "[musculus ]";
spr <‐ "[spretus ]";
car <‐ "[caroli ]";
seqs <‐ c(dom,mol,cas,mus,spr,car);
seqnames <‐ c("dom","mol","cas","mus","spr","car");
nseqs <‐ length(seqs);
npoints <‐ length(x);
x = vector(length=npoints);
y = vector(length=npoints);
k = 0;

28.
p distance 2
for (i1 in 1:(nseqs‐1)){
for (i2 in (i1+1):nseqs ){
k = k + 1;
# cat(sprint("%d %dn",i1,i2));
seq1 = DNAString(seqs[i1]);
seq2 = DNAString(seqs[i2]);
seq_b1 = substring( seq1, 1, 617 );
seq_c1 = substring( seq1, 618, 1256 );
seq_b2 = substring( seq2, 1, 617 );
seq_c2 = substring( seq2, 618, 1256 );
len_b = length( seq_b1 );
cmp_b = c(compareStrings(seq_b1,seq_b2));
sub_b = gsub("(['?'])","",cmp_b);
subt_b = DNAString(sub_b);
dif_b = length(subt_b);
n_b = len_b ‐ dif_b;
pdis_b = n_b / len_b;

29.
p distance 3
‐pdis_line.R‐
x[k] = pdis_b;
len_c = length( seq_c1 );
cmp_c = c(compareStrings(seq_c1,seq_c2));
sub_c = gsub("(['?'])","",cmp_c);
subt_c = DNAString(sub_c);
dif_c = length(subt_c);
n_c = len_c ‐ dif_c;
pdis_c = n_c / len_c;
y[k] = pdis_c;
cat(sprint('%s %s %d %g %gn',seqnames[i1],seqnames[i2],k,pdis_b,pdis_c));
}
}
xdev <‐ x‐mean(x);
ydev <‐ y‐mean(y);
b <‐ sum(xdev*ydev)/sum(xdev*xdev);
a <‐ mean(y) ‐ b*mean(x);
cat(sprint('a=%g, b=%gn',a,b));

30.
p distance 4
pdis_line.R
R
( bin path )
(p distance )