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Оптимизация генома на Английском (год написания -2006 )
 EVOLUTION
formation of modern forms GENOMEs
INTRODUCTION
Formation of polymeric structures is believed to had occurred in a step-wise manner. As a result, its primary organisation is likely to represent a binary system. BASIC ISSUES
Initial steps in development of proto-organisms on the Earth had been characterised as a growth of polymeric structure out of a mixture of monomeric pronucleotides. Hence, a synthesis of nucleotide sequences at this stage might be described with a hypothetical polymerization equation (1) :
(1) a*A + b*B + c*C = A
a
B
b
C
c ...
The rate of polymerization in turn can be presanted as follows :
(2) V = K*[A]
a
* [B]
b
* [C]
c
...
Where: V - growth rate of a polymeric circuit of types of monomers - A, B, C.. ;
K – common coefficient ;
[A], [B], [C]...- concentration of the individual monomers ;
a, b, c... - corresponding coefficients per ratio of monomers .
We are interested in the differences in the velocities of the grown under natural conditions, with biased concetrations of monomers. The behavior of the reaction of polymerization is described on the basis of the follow simplications:
(2a)
[A] = [B] = [C] .. = [S] (2b)
a = b = c .. = i/N
Then :
(3) V(N)
= K*[S]
i
;
Where:
[S] = hypothetical average concentration ;
N = number of different types of monomers ;
i = lenth of the pre-DNA-RNA polymer
Provided one of the concetration is lower ( e.g. = 0.5 * S ), it would result in :
N(2)=2 and N(4)=4 , for [S] = 1 , in length of 100 bases of pre-DNA-RNA.
If at least one monomer had the concentration lower than the average
e.g. = [ 0.5 * S ], it would result in the following :
(4)
R
2/4
= V(
2)
/V(
4)
Where :
R
2/4- ratio of polymerization velocity for N(2)=2 and N(4)=4 .
R
2/4= [S]
100
/([S]
75 *
[0.5*S]
25
) = 2
25
~ 3.3 * 10
7
If that particular concentration is smaller than others (e.g. 50 % (0,5* S) the propotion of the rates of polymerization in otherwise comparable conditions would be ~ 3,3 * 10
7
or seven orders of magnitude as, for instance, in the case for `A`+`T` and `A`+`T` +`C`+`G` . As a result, base pairing of the type `C`+`G` would never take part
. Bearing in mind the difficalties that stem from the natural synthesis and stability of `C`, the assumption regarding the natural occurrence of `C` is likely to be logical(3). It is thus likely that the rate of polymerization in the case of the primary model would be a lot of orders of magnitude larger, and it is therefore necessary to assume that the primary (archeal) polymers of pro-DNA-RNA structures are binary
complementary systems. An important issue is raised however regarding the measure of the archaicity. Probably, the simplest organisms (germs) are not necessarily archeal, moreover, the majority of them are likely to be recent simple organisms.
It is logical to apply a statistical analysis (1,2) on the basis of the length of the multiplets (A..),
(T..),
(C..),
(G..)
, (AT..),
(CG..)
e.g. the multiplet would be АААААА - А(6) rather than on the basis of the proportion of `A`+`T`/`C`+`G`.
Stretches (or islands or domains) of sequences that lack statistical meaning can be considered rudimentary with a high level of certainty.
From this perspective, the polymorphism of isofunctional DNA clusters is likely to inherently harbor its history
of the genesis and the deepness of the evolution of the clusters – the genomes. Probably, the appearance of an additional ‘C’-‘G’ couple was evolutionarily advantageous in terms of increasing the informational capacity of pro DNA-RNA within a comparable thermodynamically stable length.
It is believed that the evolution has found a more efficient mode of compacting RNA information into DNA carriers.
. However, this could become possible only after the appearance of its own synthesis of
`C` и `G `.
COMPARISON OF GENOME MULTIPLETS
In this study, a model for a development of Primary Alive Matrix (PAM
) is proposed. In this model,
`AT`
had formed the basic, ancient matrix. Biochemical START – AUU and STOP – UAA codons had been preserved. ‘CG’ matrix had appeared only after the biochemical synthesis of these nucleic bases. It is likely that the appearance of this synthesis, ‘C’ and ‘G’ have gradually resulted in a replacement of ‘A’ and ‘T’ in the genetic material. Initially, the ‘CG’ matrix had formed an ancient genome as an evolutionary mistake. The most ancient genomes had copied them mechanistically. However, with the appearance of a new source of monomers – the biochemical cycles for synthesis of ‘C’ and ‘G’, their presence have not limited (as for other mistakes) the development and the self-renewal (e.g. the generation of progeny). The new couple of nucleic bases had fitted in evolution. The present-day genomes are apparently far from that exciting époque – THE BINARY LIFE. Nevertheless, we are able to observe a number of complex evolutionary processes:
1. An ‘AT’ platform decay;
2. A replacement of ‘AT’ platforms with ‘CG’ ones;
3. A degeneration of ‘AT’ platforms;
4. A degeneration of ‘CG’ platforms.
Matrix decay can be deduced from the following considerations:
The rate of a decay of a multiplet with a size ‘N’ would depend on its connections with the genome.
(5)
dN / dt = F
(N)
Where : N -
size of the multiplet; F(N)- function that depends on the interconnection between the `N` - size multiplets within the genome
; t -
a
`
normalized time’
.
The rate of a decay of a number of multiplets – (n) is proportional to their amount.
(6)
dn
(N)
/ dt = Kn
*
n
(N)
Where
: n
(N) - number of multiplets
with a size
`N`
; K
n -
coefficient
.
Applying an equation
(5) to equation
(6) would give:
(7)
dN = F
(N)
* dn / (
Kn
*
n
(N)
) The function F(N) depends on the degree of participation of the multiplets in vitally important clusters: as keys, that initiate sequences and other signs in the genome ’grammar’
. One can expect three most simple cases:
:
a) F
(N)
= K
N
* N
–
multiplets
with a
size
`N` are not related to the genome function by their sizes
Where: K
N
- coefficient .
b) F
(N)
= K
N
– when multiplets size`N` - is related with genome`s function.
c) F
(N)
= K
N
/ N
- when multiplets size`N` - depends on the genome`s function and its permissible mutations proportionaly to it`s size
.
Thus we can draw the following basic equations
:
(8) for 7 а
Ln ( Ni/Nk ) = K1 * Ln ( n
(Ni ) /
n
(Nk)
)
(9) for 7 б
Ni -Nk = K1 * Ln (n
(Ni) /
n
(Nk)
) (10) for 7 в
Ni
2
-Nk
2
= K1 * Ln (n
(Ni ) /
n
(Nk)
) Where : К1 = K
N
/ Kn
One should expect that different sizes and types of multiplets would be in a varying level of dependence of the genome which in turn would be described by different equations. These dependences would allow to measure ‘discrete’ values of the rates of mutations in distinct stretches of clusters of the genomes. Clearly, these values bear probabilistic character and require additional confirmations. Usually, the function F(N) should have a negative value, whereas upon a positive value one would expect a growth of the size of the multiplets.
As far as we see only one side of the genome evolution that determines the basic vector of mutations into itself, it would be helpful to use the term ‘
INFORMATIONAL PERFECTION
’ of the genomes (
Q1
). The degree of Q1
is an approximation of the proportion of CG/AT to ‘1’. To this end, it is convenient to compare the sums of logarithms of multiplicities of the sizes of multiplets ‘CG’ and ‘AT’ with their amount ‘n’.
N=1
N=1
(11) Q1
=
∑ Ln(nCG*N) ) /
∑ Ln(nAT*N) N=max N=max
Where : Q1
– first level of evolution . nAT
,
nCG
- amounts of multiplet size `N` for : - (nAT) and -(nCG) .
Since this criterion includes itself rudimentary traits, large platforms largely determine the level of the genome evolution.
STATISTIC ( drafts)
A statistical analysis of the genomes of different classes of living organisms was performed for a presence of multiplets of `A`,
`T`,
`A`+`T`,`C`+`G`,
`AT`, and `CG`. The analysis estimated the amount of multiplets (e.g. ‘A’ – in a stretch of AAA..), as well as the amount of `A`+`T` and `C`+`G` - simple sum of the corresponding sizes of multiplets `A` и `T`, `C` и `G`- correspondingly
. `AT`
-
and
`CG`- was got by
replacement `T`- by `A`, and `G` – by `C` - accordingly, in researched sequence of genome
.
Using this approach, the principle of evolution by replacement of `A` and `T` with ‘C` and `G’ was studied. FIG.
1 modern
genomes:
A
,
E.Coli 0157-H7
;
B
,
Encephalitozoon cuniculi Chr 4
;
C
,
Giardia lamblia Chr 1.
FIG.
2 new type
I
:
A
,
C
a
ndida Chr J
;
B- Saccharomyces cerevisiae Chr 2
;
C-Yarrowia lipolica Chr f
.
FIG.
3 new type
I
:
B
,
Trypanosoma brucei Chr 2
; C
,
Plasmodium falciparum Chr 6.
FIG.
4 new type
I
:
A
,
Caenorhabditis Elegans Chr IV
; B
,
Drosophila melanogaster Chr 3L
;
C- Arabidopsis thaliana Chr 1
.
FIG
.
5 Fugu cluster.
FIG
. 6
new type
I , B- Homo Sapiens Chr 7 , C- Mouse Chr 4.
FIG.
7
Drosophila melanogaster – Chr 2L, 2R, 3L, 3L, X, 4
.
FIG.
8
Examples of approximation for decayed multiplets.
FIG.
9
modern S
: A,
Leishmania major
; B,
Leishmania infanta.
Behavior of ‘AT’ and ‘CG’ matrices
Statistical analysis revealed a presence of ‘AT’-matrices. A tendency of narrowing the space between AT and CG matrices was also observed. Multiplets in turn also tended to split. Similar pattern of development was observed upon analysis of other genomes as well to reveal apparent vectors of development. These in turn allow drawing conclusions regarding the relative evolutionary level of the genome.The model allows performing genetic comparison of the development of species of different classes and kingdoms of animals and plants as well as prediction of their ierarchy.The rate of decay and replacement of individual groups opens an opportunity to evaluate the local rates of mutations and their vectors.The Chromosomal genomes demonstrate collinear behavior of the chromosomes.‘Synchronized’ behavior of the mutations in the chromosomes of one genome was observed. This was demonstrated on the chromosomes Leishmania major (a) and Leishmania infanta (b). These are members of a new type of genomes, that apparently were formed in the course of cooking in the broth of destroyed nuclei from members of modern genomes – vivisection of `AT’-rich clusters (e.g. heat treatment). Leishmania genome apparently demonstrates similar pattern of chromosomal organization with different polarity – ‘CG’ was gradually replaced with ‘AT’ at the ‘AT’-‘CG’.Table1 summarizes coefficients `
K`
for different multiplets sizes`N` from the individual genomes. A significantly higher mobility of ‘C’, ‘G’, and ‘CG’ multiplets compared to the the analogous ‘A’, ‘T’, and ‘AT’ was observed. The difference in the mutational mobility of different parts of the clusters can reach tens of orders of magnitude!!!. Indirectly indicating the overall instability of the genomes and their high plasticity.
Q1
values for different genomes were estimated. A broad variability at the level of informational evolutionary development of the genomes was observed. The human genome turns out to be only slightly better than Caenorhabditis Elegans but it significantly steps back from the Fugu genome. Yeast also revealed a significantly high level of Q1
. As expected, E. Coli, Giardia lamblia, Encephalitozoon cuniculi revealed highest Q1
levels.The tendency ' compression ' places between 'AT ' and 'CG ' - matrixes is observed . The tendency to splitting multiplets is shown also. Accordingly, we can observe this process in development for various genomes . Ticking development genome`s gives vectors of their development which allow to judge a relative evolutionary level genome . The model allows to make genetic comparison of development of organisms of various classes of Empires of Animals and Plants.
Conclusions Evolution of the genomes is apparently driven by an universal pressing vector that tends to increase the compactability of the information in the genome. This is achieved mostly by equalizing the proportion of A, T, C, and G and also via optimization of the sizes of the multiplets. An apparent ‘soft’ impact of this factor fits in the kinetics of the processes of regeneration. Thus, development follows a set of multipurpose goals. Part of them is likely to be connected to the informational perfection of the genome, including:
1. Maximal utilization of the informational size of the genome;
2. Optimization of the sizes of the coding functions;
3. Optimization of the sizes of groups of genes and clusters.
The statistical analysis revealed the probabilistic nature of the vectors for the genomes development.
Acknowledgements
.
The algorithms for the statistical analysis in this study were developed by Georgi Ivanov.
Dimiter Demirov critically read the manuscript.
REFERENCES
1.`The guanine and cytosine content of genomic DNA and
bacterial evolution `
AKIRA MUTO AND SYOZO OSAWA
- Vol. 84, pp. 166-169, January 1987
2.
`
A simple model based on in codon and amino-acid usage and GC composition within and across genomes`
Robin D Knight
, Stephen J Freeland
and Laura F Landweber ,-
Genome Biology
2001.
3.
Origin of Life: Instability of Building Blocks - Jonathan Sarfati ,Vol. 13, No. 2 of the Creation Ex Nihilo Technical Journal
Author : Sergey Astashkin
0
5
10
15
20
25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A
Ehctericia Coli O 157 H7
(5,4)
Col B- A+T
Col C- C+G
Col D- CG
Col E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
1
2
3
4
5
6
7
8
9
10
11
B
Encephalitoz oon c uniculi Chr 4
(0,228)
Colum B- A
Colum C- C
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
1
2
3
4
5
6
7
8
9
10
11
C
Giardia lamblia Chr 1
Colum B- A+T
Colum C- C+G
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Graph 1
0
5
10
15
20
25
30
35
40
45
50
55
60
65
1
2
3
4
5
6
7
8
9
10
11
12
13
B
Plasmodium falciparum
Chr 6 ( 1,3)
Col B- A
Col C- C
Col D- cg
Col E- at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
35
40
1
2
3
4
5
6
7
8
9
10
11
12
13
A
Trypanosoma brucei Chr 2
(1,31)
Colum B- A
Colum C- C
Colum D- cg
Colum E- dt
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Graph 2
0
5
10
15
20
25
30
35
40
45
50
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A
Carnorhabditis Elegans Chr IV (17,5)
Col B chr1-A
Col C chr1-C
Col D chr1-cg
Col E chr1-at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
60
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
B
Drosophla melanogaster
l3L (23,3)
Col B -A+T
Col C -C+G
Col D -CG
Col E -AT
Column B
Column C
Column D
Column E
multiplets
L
N
(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
60
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
C
Arabidopsis thaliana Chr 1
(30,1)
Colum B- A+T
Colum C- C+G
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Graph 3
Graph 4
0
5
10
15
20
25
30
1
2
3
4
5
6
7
8
9
10
11
12
13
Condida Chr J
Col B- A
Col C- C
Col D- cg
Col E- at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
1
2
3
4
5
6
7
8
9
10
11
12
13
SACCHAROMYCES CEREVISIAE Chr IV
Col B- A
Col C- C
Col D- cg
Col E- at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
35
40
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Yarrowia lipolytica
Chr f
Colum B- A
Colum C- C
Colum D- cg
Colum E- at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
B
HUMAN Chr 7
(158)
Colum B- A+T
Colum C- C+G
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
C
Mouse Chr 4
(149)
Colum B- A+T
Colum C-C+G
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Graph 5
0
5
10
15
20
25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A
Ehctericia Coli O 157 H7
(5,4)
Col B- A+T
Col C- C+G
Col D- CG
Col E- AT
Column B
Column C
Column D
Column E
mul ti pl ets
L
n
(n
)
Graph 6
0
5
10
15
20
25
30
35
40
45
50
55
60
65
1
2
3
4
5
6
7
8
9
10
11
12
13
B
Plasmodium falciparum
Chr 6 ( 1,3)
Col B- A
Col C- C
Col D- cg
Col E- at
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Clone I
Clone II
Clone III
Graph 7
0
5
10
15
20
25
30
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
A
Drosophila melanogaster
2L,2R,3L,3R,X,4
Col B-2L -A+T
Col C-2L -C+G
Col D-2R -A+T
Col E-2R-C+G
Col F-3L -A+T
Col G-3L -C+G
Col H-3R -A+T
Col I-3R-C+G
Col J-X -A+T
Col K-X -C+G
Col L-4 -A+T
Col M-4-C+G
Column B
Column C
Column D
Column E
Column F
Column G
Column H
Column I
Column J
Column K
Column L
Column M
multiplets
L
N(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
B
Drosophila melanogaster
2L,2R,3L,3R,X,4
Col B-2L -cg
Col C-2L -at
Col D-2R -cg
Col E-2R-at
Col F-3L -cg
Col G-3L -at
Col H-3R -cg
Col I-3R-at
Col J-X -cg
Col K-X -at
Col L-4 -cg
Col M-4-at
Col umn B
Col umn C
Col umn D
Col umn E
Col umn F
Col umn G
Col umn H
Col umn I
Col umn J
Col umn K
Col umn L
Col umn M
multiplets
L
N(
n
)
0
5
10
15
20
25
30
35
40
45
50
55
60
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
A
FUGU Cluster
(320)
Col B -A+T
Col C -C+G
Col D -CG
Col E -AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
0
5
10
15
20
25
0
1
2
3
4
5
6
7
8
9
10
11
Leishmania major
Col B-A+T
Col C-C+G
Col D-CG
Col E-AT
Column B
Column C
Column D
Column E
multiplets
L
n(
n
)
0
5
10
15
20
25
30
35
40
0
2
4
6
8
10
12
14
16
18
Leishmania infanta
Col B-A+T
Col C-C+G
Col D-CG
Col E-AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
GENOME (A+T)
Hi
N1/N2- K(a+t)
N2/N3- K(a+t)
N3/N4- K(a+t)
N1/N2- K(c+g)
N2/N3- K(c+g)
N3/N4- K(c+g)
N4/N5-K(c+g)
An gambae ch2
0,50
1/6 -1,12
6/17- 0,66
0,37
1/9 – 1,42
9/11 - -0,08
11/17-0,02
0,7
Apis malifera Gn
0,37
1/7-0,91
0,37
1/9 – 1,46
9/12 – 0,21
0,59
Dr melan Ch X
0,45
1/8 – 1,03
0,33
1/8 – 1,41
8/12 – 0,04
0,38
Candida ch J
0,50
1/9 – 1,1 A
1,1
1/6 – 1,69 C
0,48
Sacsa cer ch IV
0,45
1/9 – 1,01 A
0,43
1,75 C
Yarrow lip ch f
0,57
1/4 – 1,26 A
4/11 – 0,89
0,24
1/5 – 1,87 C
1,1
E. Coli genome
0,89
1,22
1,6
Giar lamblia Gen
0,9
1,38 A
1,53 C
Ence cuniculi G
0,83
1,3 A
1,47 C
Trip brucei ch2
0,49
1/7 – 1.1 A
7/9 – 0,18
0,42
1,5 C
Plas falci ch6
0,13
1/3 – 1,1 A
3/7 – 0,75
7/10 – 0,56
0,16
1/4 – 2,25
1,3
Carn eleg ch IV
0,48
1/16 – 0,89
0,67
1/8 – 1,7
8/14 - - 0,1
0,41
Arab tha ch1
0,31
1/6 – 1,05
6/12 – 0,62
0,35
1/8 – 1,9
8/11 - - 0,1
FUGU genome
0,63
1/5 – 1,2
5/11 – 0,69
0,32
1/8 – 1,23
8/13 – 0,24
0,38
Gal Gallus chr1
0,54
1/7 – 1,06
7/13 – 0,39
0,27
1/8 – 1,46
10/17 – 0,23
0,48
Human ch 1
0,53
1/8 – 1,07
8/11 – 0,5
11/24 – 0,16
0,3
1/8 – 1,56
0,48
Mouse ch 3
0,46
1/8 – 1,1
8/12 – 0,5
12/20 – 0,27
20/29 – 0,11
0,3
1/8 – 1,1
8/10 – 0,0
0,51
Tularemia G-me
0,31
1/7 – 1,0
2,0
2,0
Graph 8
TABLE 1
GENOME (A+T)
N1/N2- K(cg)
N2/N3- K(cg)
N3/N4- K(cg)
N1/N2- K(at)
N2/N3- K(at)
N3/N4- K(at)
An gambae ch2
1/10/-0,74
10/16-0,51
0,48
1/6-0,61
6/14-0,44
14/29-0,28
0,11
Apis malifera Gn
1/12 – 0,78 0,37
1/7 – 0,43
7/16 /-0,27
18/40 – 0,15
0,09
Dr melan Ch X
1/10 – 0,78
0,47
1/7 -0,5
7/13-0, 39
13/28 – 0,21
0,17
Candida ch J
1/7 – 1,0
0,73
1/8 – 0,53
8/21 – 0,43
0,17
Sacsa cer ch IV
1/9 – 0,98
0,73
1/14 – 0,47
14/23 – 0,37
0,36
Yarrow lip ch f
1/14 – 0,81
1,1
1/6 – 0,81
6/13 – 0,53
13/23- 0,29
0,13
E. Coli genome
0,65
1/12 – 0,65
0,5
Giar lamblia Gen
0,82
0,72
Ence cuniculi G
0,82
0,71
Trip brucei ch2
0,77
1/7 – 0,64
7/14 – 0,4
14/23 – 0,23
Plas falci ch6
1/7 – 1,5
0,7
1/9 – 0,29
9/22 – 0,19
0,08
Carn eleg ch IV
1/7 – 0,97
9/13 – 0,65
13/16 – 0,17
16/18 – 0,52
0,56
1/3 -0,54
3/13 – 0,37
0,27
Arab tha ch1
1/8 – 1,1
0,75
1/9 – 0,45
9/14 – 0,38
14/24 – 0,26
0,16
FUGU genome
1/6 – 0,88
6/10 – 0,63
10/14 – 0,45
14/27 – 0,36
0,21
1/3 – 0,76
3/12 – 0,51
14/23 – 0,29
23/29 – 0,24
29/37 – 0,18
Gal Gallus chr1
1/8 – 1,06
8/12 – 0,44
0,26
1/7 – 0,58
7/14 – 0,41
14/19 – 0,31
19/33 – 0,2
33/48 – 0,14
Human ch 1
1/7 – 0,92
7/17 – 0,49
0,22
1/5 – 0,58
5/12 – 0,41
12/20 – 0,26
20/33 – 0,2
0,11
Mouse ch 3
1/7 – 1,02
7/13 – 0,55
0,29
1/7 – 0,59
7/13 – 0,41
13/21 – 0,35
21/34 – 0,18
34/46 – 0,06
Tularemia G-me
1,27
1/17 – 0,39
0,31
TABLE 2
0
5
10
15
20
25
30
35
40
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Tularemia
(1.8)
Colum B- A+T
Colum C- C+G
Colum D- CG
Colum E- AT
Column B
Column C
Column D
Column E
multiplets
L
n
(
n
)
Автор
Sergey Astashkin
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