Giulia Menconi
, Giovanni Battaglia
, Roberto Grossi
, Nadia Pisanti
and Roberto Marangoni
Istituto Nazionale di Alta Matematica, Citt
a Universitaria, 00185 Roma, Italia
Dipartimento di Informatica, Universit
a di Pisa, 56127 Pisa, Italia
CNR-Istituto di Biofisica, 56124 Pisa, Italia
Residentome, Retrotransposons, Mobile DNA elements, L-grams.
We aim at finding all the mobile elements in a genome and understanding their dynamic behavior. Comparative
genomics of closely related organisms can provide the data for this kind of investigation. The comparison task
requires a huge amount of computational resources, which in our approach we alleviate by exploiting the
high similarity between homologous chromosomes of different strains of the same species. Our case study
is for RefSeq and two other strains of S. cerevisiæ. Our fast algorithm, called REGENDER, is driven by data
analysis. We found that almost all the chromosomes are composed by resident genome (more than 90% is
conserved). Most importantly, the inspection of the non-conserved regions revealed that these are putative
mobile elements, thus confirming that our method is useful to quickly find mobile elements. The software tool
REGENDER is available online at
Mobile elements play a prominent role in eukaryotic
genomes. They represent around 15–20% of the Hu-
man genome (Lewin, 2007). They are very viva-
cious, since they are able to jump over the genome,
to duplicate in different positions, and to possibly ex-
press their own genes. They can also change cells’
phenotype, when jumping within a gene sequence
(or its regulatory elements), thus altering its expres-
sion. Some complex pathologies whose molecular
mechanisms and global inheritance are hard to ex-
plain by standard inheritance laws, have found to be
correlated to mobile elements translocations (see e.g.
(Conti et al., 2006)). This scenario suggests to look
at eukaryotic genomes as evolving ecosystems, where
the resident genome (intended as the immotile DNA)
and the different elements of the mobilome (the to-
tal of mobile elements, mainly transposons) act like
different species competing for the available bioche-
mical resources (Le Rouzic et al., 2007; Venner et al.,
2009). The importance of mobile elements strongly
supports the development of tools for quickly map-
ping all of them in a genome. The “classic” strategy
to face this problem consists in taking the known mo-
bile element sequences and finding their occurrences
in the genome. However, we cannot apply this strat-
egy when we do not know a priori all the different
kinds of mobile elements. Moreover, there are further
problems. Mobile elements are subject to mutation,
fragmentation, and fusion of consecutive elements in
certain cases: hence, many false negative outcomes
are possible. Also, unresolved sequences are a pri-
mary source of difficulty.
A more promising strategy is to compare genomes
of closely related organisms. The rationale is that
most of the chromosomal rearrangements observed
in such datasets are probably caused by mobile ele-
Recently, a dataset suitable for this purpose has
been made available: 39 different strains of S. cere-
visiæ have been sequenced, and the relative genomes
published without annotations (Liti et al., 2009). The
coverage of the dataset is relatively low (one-to-
fourfold), and the genomic sequences contain a frac-
tion of unresolved sequences. Unfortunately, per-
forming a genome-wide alignment is computationally
Our proposed approach exploits the high sim-
ilarity between homologous sequences of different
strains of the same species, so as to perform a simple,
but powerful ad hoc alignment. Using L-grams we
detect the non-conserved regions by identifying and
masking the long conserved ones. In other words,
we identify the resident genome and then we ana-
lyze its complementary part to infer elements of the
Menconi G., Battaglia G., Grossi R., Pisanti N. and Marangoni R..
DOI: 10.5220/0003137001310136
In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2011), pages 131-136
ISBN: 978-989-8425-36-2
2011 SCITEPRESS (Science and Technology Publications, Lda.)
mobilome. Our method is very efficient, since the
resident genome localization requires just few sec-
onds for chromosomal sequences of millions of bases.
What remains for non-conserved regions is very small
when compared to the length of the chromosomes at
hand. Consequently, more sophisticated and expen-
sive methods can be focused to this restricted set of
Our method builds a map for the mobilome of
S. cerevisiæ as a case study. For any given chro-
mosome in our dataset, we classify its segments
as either conserved or non-conserved regions, using
RefSeq@SGD (SGD, 2010) as the reference genome
for this yeast (since RefSeq is the only annotated
strain). We then inspect and mark all the non-
conserved regions trying to infer putative mobile el-
ements. Since S. cerevisiæ is probably the organism
where mobile elements are best characterized, it is an
optimal benchmark for validating our approach.
We apply the following classification by refer-
ring to the available annotations of RefSeq: (i) com-
pleted transposons, including their bounding seg-
ments called Long Terminal Repeats (LTRs), in this
case indicated as Ty; (ii) pairs of LTRs without the
transposon in between; (iii) soloLTRs, where a sin-
gle LTR element is found in each of them. Then, the
map annotates all these occurrences of putative mo-
bile elements. Interestingly, the map can be seen as a
puzzle with some missing pieces (the non-conserved
regions). A global view at its configuration allows
to locate a single candidate position for several non-
conserved regions with high confidence, even if their
relocation involves unresolved symbols marked by Ns.
Note that we deploy the peculiar structure of the trans-
posons that can be evinced from RefSeq: they are
long between 5 000b–6000b (bases) and are delim-
ited by two LTRs of 200b–300b. We illustrate our
approach by involving the S. cerevisiæ strains Y55
and YPS128 from the given dataset (Liti et al., 2009),
whose chromosomes are compared against RefSeqs.
What we present in this paper can be easily extended
to the rest of the strains in the above dataset. More-
over, given its speed, the method can be applied to
the analysis of similar strains of species with possibly
much longer genomes.
Preliminary Data Analysis. Our approach is driven
by data analysis. We performed a preliminary
study to understand how to grasp the high similar-
ity in our dataset. Consider a chromosomes’ pair
, ChrN
), where A is RefSeq and B is either
Table 1: Statistics (%) for the L-grams (L = 32) satisfying
properties (a)(c). The length of each chromosome N in
Y55 is also given.
N bases (a) (b) (c) N bases (a) (b) (c)
1 248 261 81.32 59.92 0.43 9 467 776 89.53 74.02 0.29
2 800 992 98.54 82.55 0.14 10 770 597 94.60 76.43 0.62
3 321 691 93.82 83.74 2.13 11 693 726 97.64 78.98 0.11
4 1 522 688 96.24 77.38 0.89 12 1 067 059 95.12 78.66 2.15
5 577 152 96.33 77.66 0.39 13 923 317 96.96 84.35 0.52
6 273 660 97.56 76.05 0.19 14 781 629 98.20 82.46 0.15
7 1 113 452 95.05 78.63 0.73 15 1 105 914 95.67 81.07 0.33
8 566 494 95.47 78.70 0.84 16 946 183 96.53 83.55 0.65
Y55 or YPS128; also, 1 N 16 since the yeast
genome consists of 16 chromosomes. Examine all
the possible (overlapping) L-grams of ChrN
as can-
didates, where an L-gram is a segment of L consecu-
tive bases.
Assuming that ChrN
contains m bases, there are
m L + 1 L-grams, accounting for possible dupli-
cates. Call valid the L-grams that do not contain any
symbol N. The common L-grams are the valid L-grams
that occur exactly (i.e. fully conserved with no muta-
tion) both in ChrN
and ChrN
In our experiments, L = 32 resulted to be a good
choice, leading to the following empirical facts that
were observed for chromosomes N = 2, 3, . . . , 16,
with chromosome N = 1 (whose percentages are
shown inside parentheses below) being an outlier.
The reported percentages are absolute, as they are ob-
tained by dividing the number of wanted L-grams by
m L + 1.
(a) The valid L-grams are numerous: they are in
the range 89.53%–98.54% in Y55 and from 88.84%
to 97.27% in YPS128 (81.32% in Y55 and 77.29% in
YPS128 for chromosome 1).
(b) The common L-grams are also numerous:
they are between 74.02%–84.35% in Y55 and be-
tween 71.71%–83.24% in YPS128 (59.92% in Y55
and 58.47% in YPS128 for chromosome 1).
(c) The common L-grams that occurs once in each
genome are the vast majority: indeed, those occur-
ring two or more times are very few, between 0.11%–
2.15% in Y55 and 0.07%–1.93% in YPS128.
A summary reporting the above percentages for
the L-grams in the 16 chromosomes of Y55 is shown
in Table 1. The implication of (a)(c) is that we can
localize the conserved regions using the common L-
grams, as discussed next.
Conserved Regions. Our algorithm for the rapid
detection of large highly-conserved segments, called
REGENDER (REsident GENome DEtectoR), is driven
by the above data analysis. It performs a two-phase
processing of all the possible chromosomes’ pairs
, ChrN
), where A is RefSeq, B is either Y55
BIOINFORMATICS 2011 - International Conference on Bioinformatics Models, Methods and Algorithms
or YPS128, and N ranges from 1 to 16. In the first
phase, REGENDER finds the common L-grams be-
tween ChrN
and ChrN
. In the second phase, RE-
GENDER aggregates consecutive L-grams in a greedy
fashion using some user-defined parameters that con-
trol when the next conserved region begins in both
and ChrN
. We provide the details of the algo-
rithm in Section 3.
REGENDER is somewhat related to the anchor-
based algorithms (Ohlebusch and Abouelhoda, 2006)
that circumvent the quadratic costs. Such algorithms
share a common mechanism. First, they build a dic-
tionary to store the fragments or seeds that are com-
mon to both ChrN
and ChrN
. Second, they extend
the fragments/seeds into longer sequences called an-
chors using dynamic programming, except chaining
algorithms (Ohlebusch and Abouelhoda, 2006). The
sequence of anchors thus found are required to be co-
linear; namely, the anchors should occur in the same
relative order inside both ChrN
and ChrN
. Third,
these algorithms apply an expensive dynamic pro-
gramming scheme to the regions of ChrN
and ChrN
that are left uncovered by the anchors. Driven by our
data analysis, REGENDER can go simpler. First, the L-
grams of ChrN
are stored in a hash table, and those
of ChrN
are searched in the table during a scan of
. The high similarity of ChrN
and ChrN
tifies our choice of exact L-grams as fragments. Se-
cond, our dataset gives almost surprisingly a natural
set of anchors: contrarily to the anchor-based algo-
rithms, we do not need any dynamic programming
or chaining techniques to enforce the colinearity and
the non-overlapping property, since there is almost a
one-to-one mapping between the occurrences of the
L-grams (see Section 2). Actually, we take advantage
of the fact the L-grams overlap and, if they are not co-
linear, we get a hint for a possible translocation. As a
result, REGENDER performs just a scan of ChrN
. One execution of REGENDER takes less than
a second on a standard PC with limited amount of
memory. This is a major requirement, since we need
to execute REGENDER for all pairs of corresponding
chromosomes of ChrN
and ChrN
. Third, we remark
that we do not need a complete alignment of ChrN
or ChrN
for the purposes of the analysis performed
in this paper. A high-quality alignment of the con-
served regions in ChrN
or ChrN
is unnecessary in
our case, as illustrated by the clear patterns emerg-
ing from Fig. 1. What we really care about is the de-
scription of the dynamics of the mobilome, identify-
ing and locating all the mobile elements in the input
sequences, together with the genomic rearrangements
they are involved into. A merit of our approach is that
of being able to select a small set of candidates for the
Figure 1: A plot of the common L-grams for Chr4 (1
095 000–1 155 000) of RefSeq (top sequence) and Y55 (bot-
tom sequence), where L = 32. Each line connects the start-
ing positions of a common L-gram. Thus, the empty trian-
gles or trapezoids represent non-conserved regions. Anno-
tated mobile elements are represented by the green rectan-
gles just below the top line; unresolved sequences are the
black rectangles just above the bottom line. High resolution
plots are available online.
latter investigation, as discussed next.
Non-conserved Regions. The outcomes of our ex-
periments with REGENDER are analyzed as follows.
Graphically, we represent the two homologous
chromosomes as two horizontal straight lines, and
place A in the top and B in the bottom, as in Fig. 1.
We mark the conservations with some color. The non-
conserved regions are then detectable as non-colored
trapezoids. The action of a transposable element T
that has changed position from region X of strain A to
region Y of strain B within two homologous chromo-
somes is then represented by two triangles (Fig. 1):
we detect a white downward triangle inside region X
(marking presence of T only in region X of strain A
and absence in strain B), and an upward white tri-
angle in Y (marking presence of T only in region Y
of strain B and absence in corresponding position on
strain A). Therefore, when strain A is the referring se-
quence RefSeq, we can infer that T probably moved
from X to Y inside strain B by projecting region X of
A onto the corresponding part in B.
A picture of possible situations is shown with
some detail in Fig. 2 and described in section 4.
We followed the above conceptual scheme to col-
lect statistics for all the chromosomal rearrangements
among the 16 chromosomes’ pairs from the selected
strains (B is Y55 or YPS128) with the same chromo-
some in A=RefSeq, thus classifying any resulting re-
arrangement. We refer the reader to Section 4 for
an aggregate view of all the chromosomal differences
found and their relationships with the mobilome. We
remark that we considered significant events that in-
volve regions containing at least 200b, since very
short indels or mutations are not linked with mo-
bilome nor with chromosomal rearrangements.
The proposed approach allowed us to obtain a fast
and efficient localization of the resident genome, by
working on a standard computer. Our results clearly
show that the significant chromosomal indels involve
almost exclusively the mobilome. Moreover, we show
that unresolved sequences take place almost always in
the correspondence of telomeres or mobile elements.
Our approach allows us to infer putative insertions
and deletions of transposons or LTR elements also in
the presence of unresolved sequences.
As previously mentioned, we exploit the high simila-
rity between genomes of different strains by running a
massive computation involving all the possible chro-
mosomes pairs (ChrN
, ChrN
), where A is RefSeq, B
is either Y55 or YPS128 strain, and N = 1, . . . , 16. We
follow a two-phase approach for REGENDER, whose
inputs are two chromosomes ChrN
and ChrN
, the
length L of the grams, and two user-defined parame-
ters δ
and δ
to be used in the second phase. First,
we find all the common L-grams between ChrN
. Second, we detect highly conserved regions
by aggregating consecutive L-grams. Finally, we in-
spect the non-conserved regions that are found by RE-
GENDER, so as to infer mobilome elements. Due to
space constraint we omit the implementation details.
Phase 1 of REGENDER: Common L-grams. We
aim at finding which L-grams of ChrN
occur inside
, where an L-gram is any sequence of L con-
secutive bases. First, we construct a dictionary for all
the L-grams in ChrN
and, then, we search for the L-
grams of ChrN
inside the dictionary. This task can
be performed in expected linear time by employing a
rolling hash approach based on cyclic polynomial, as
described in (Cohen, 1997). Note that using a gen-
eral purpose hash function would be more expensive
by a multiplicative factor of L. Also, using a trie-
based dictionary instead of hashing would guarantee
a linear-time worst-case performance, but hashing is
faster in practice.
A detailed description of the rolling hashing is be-
yond the scope of the current paper. However, it can
be easily proved that the first phase of the algorithm
REGENDER requires O(|ChrN
| + |ChrN
|) time on
The output of the first phase is a mapping M, as-
sociating each L-gram s
of ChrN
, with its occur-
rence list occs(s
) in ChrN
. If s
does not occur in
, occs(s
) is empty. Although not optimal in
the worst case, our hash based approach turned out
to be effective on our datasets, yielding few colli-
sions, and allowing us to compare two entire chromo-
somes in few seconds. We implemented a prototype
in Java, using the fastutil Java collections library
to reduce as much as possible the memory usage (Vi-
gna, 2006). The experiments have been performed on
an Intel Core 2 Duo P8400 notebook, with 4GB of
RAM. The code is single-threaded, and the maximum
amount of RAM available for the first phase has been
set to 200MB. The value of the parameter L has been
set to 32, and the load factor of the hash table is set to
α = 0.75.
Phase 2 of REGENDER: Conserved Regions. Du-
ring the second phase, the information about the L-
gram occurrences, stored in the mapping M computed
in the first phase, is used to establish a correspondence
between segments of consecutive bases in ChrN
, mapping a segment I
= ChrN
, r
] into a
corresponding segment I
= ChrN
, r
]. This infor-
mation is represented by the mapping M
, and it is
graphically shown with green lines in Fig. 1.
We perform a left-to-right scan of ChrN
, according to the following greedy rule. Ini-
tially, I
and I
are empty. During the scan, the cur-
rent segments I
and I
are extended when the fol-
lowing conditions are met: (a) there exists a common
L-gram s, which occurs both to the right of I
and I
and no other L-gram with this property can be found
between I
and s, and I
and s; (b) letting d
be the
number of bases between I
and s, and d
be the num-
ber of bases between I
and s, it is |d
| δ
(hence, d
+ δ
To compute the time complexity of the second
phase of REGENDER algorithm, we observe that the
sum of the sizes of the occurrence lists in M is upper
bounded by |ChrN
| L + 1. In other words, the size
of the mapping M is O(|ChrN
|+ |ChrN
|), hence the
REGENDER algorithm requires O(|ChrN
|+ |ChrN
time on average.
Inspection of Non-conserved Regions. The contri-
bution of REGENDER is that of reducing a potentially
huge number of candidates to very few of them, so
that the direct inspection of the non-conserved regions
is doable. We perform this crucial analysis of the re-
gions that have not been mapped into segments by M
These are the potential candidates for being mobile
elements. Following, we discuss in details the per-
formed analysis.
BIOINFORMATICS 2011 - International Conference on Bioinformatics Models, Methods and Algorithms
Table 2: Running time in seconds for each chromosome of
the Y55 strain (columns). We run each tool with its default
parameters, specifying, whenever possible, the L, δ
, δ
guments (rows). Experiments performed on an Intel Core 2
Duo P8400 notebook, with 4GB of RAM, running Ubuntu
Chrm Avid Lagan Lastz GSAligner Mauve Murasaki REGENDER
1 5.60 11.50 5.10 2.20 5.60 14.10 1.20
2 21.30 24.10 12.30 16.50 14.50 24.10 2.90
3 7.60 8.60 5.50 3 6 10.60 1.50
4 53.40 36.90 42.60 51.90 27.70 36.50 5.60
5 29.70 12.70 12.40 11.10 11.40 28.70 2.10
6 6.10 4.30 4.40 3.50 5.60 9.50 1.40
7 30.90 30.10 29.90 28.90 21.40 31.10 3.90
8 25.50 15.10 9 8 11.40 25 2.10
9 13.90 14 7.60 4.90 8.80 15.90 1.90
10 77.60 17.10 13.10 15.50 15.10 31 2.70
11 55.10 15.30 10.70 11.80 13.80 20.60 2.60
30.10 34.40 19 24.70 20.30 31.80 3.80
13 23.20 37.60 18 21.50 17.60 25.50 3.50
14 88.60 20.60 7.80 14.30 14.80 25.80 2.80
15 30.30 33.30 23.10 24.90 21.70 36.70 3.90
16 24.50 64.40 19 20.20 18.20 34.50 3.60
REGENDER Performances. After the preliminary
data analysis described in Section 2 that led to the
choice of L = 32 for this dataset, we performed some
experiments on all the 16 chromosomes of the two se-
lected strains against RefSeq. REGENDER has proven
to be very fast (δ
= δ
= 100): for instance, it can
process the longest pair of chromosomes (Chr4, about
2Mb) in only 6 seconds.
In Table 2 REGENDER is compared with other ex-
isting tools, reporting the running time for each chro-
mosome of the Y55 strain. Although the REGENDER
prototype has been implemented in Python and Java,
and the code has not been fine tuned, it is, on average,
from four to ten times faster than the other tools. This
is not surprising, since REGENDER does not perform
a high-quality alignment of the input chromosomes as
the other tools do (as explained in Section 2 this is not
necessary in identifying and locating the mobile ele-
ments in the input sequences).
Qualitative Analysis of REGENDER Results. A lo-
cal exploration of the results obtained by REGENDER
showed the following scenario: most of the chro-
mosomes appear to be conserved, therefore they are
graphically covered by a uniform color given by the
overlapping of parallel straight lines connecting iden-
tical L-grams. A set of examples is reported in Fig. 2,
where the top line always represents a region of a
chromosome of RefSeq, while the bottom line rep-
resents the same region in either Y55 or YPS128; here
the mobile elements annotated in RefSeq are repre-
sented by green rectangles placed just below the top
line, while unresolved sequences are represented by
black rectangles placed just above the bottom line.
pINSu Ty-c prox-mobil pINS Ty-c
pDEL Ty1
b) c) d)
Figure 2: Features detected after REGENDER results.
Conserved regions belong to this category (includ-
ing the mobile elements, when they are annotated on
the RefSeq) and appear to be identically maintained
in the screened strain. These regions are marked as
CONS on Fig. 2(a).
This uniform coverage can be interrupted when,
for example, the screened strain has a long run of un-
resolved bases. Such unresolved sequences are gra-
phically marked by black rectangles. When the lines
connecting their flanking regions are all parallel, it is
likely that this fragment contains exactly the same se-
quence as RefSeq. In this case, we have an exam-
ple of putative conservation, marked as pCONSu, that
graphically appears as shown in Fig. 2(b).
Cases in which there is a sequence on RefSeq that
has no correspondent on the homologous region of the
screened strain are putative deletions. They can oc-
cur when a mobile element is annotated in RefSeq,
in which case they are marked as pDEL-Ty or pDEL-
LTR, if they occur for transposons or LTR, respec-
tively. They are, instead, marked as pDEL when this
putative deletion is not related to mobile elements
(Fig. 2(c),(d)).
Putative insertions are more difficult to categorize,
as the screened strain where they take place are not
annotated. If the sequence is resolved, we employ
standard alignment tools to search it in the RefSeq,
trying to detect whether the fragment has actually
been moved rather than deleted. On the other hand,
when the sequence is unresolved, we can explore only
two features. First, whether or not the length of the
inserted sequence is compatible with either a trans-
poson (when the length of the inserted sequence is
4000b) or an LTR (when the length of the inserted
sequence is 500b). Second, whether these inser-
tions take place in a region where in the RefSeq a
mobile element is annotated at a distance less than
200b. For example, the event marked as ”pINSu
Ty-c prox-mobil” in Fig. 2(e) accounts for an inser-
tion (in the Chromosome 11) in YPS128 strain with
respect to RefSeq, in an unresolved sequence. Since
such insertion takes place less than 200b away from
an LTR annotated in RefSeq, we consider this event
as ”proximal” to a mobile element. This is relevant,
since several observations in the literature suggest that
transposons prefer to migrate in zones where there are
LTR. Finally, Fig. 2(f) shows an event of ”pINS Ty-c”
since the inserted sequence length is compatible with
a transposon.
These cases represent a complete spectrum of the
situations we have found in our screening. The fol-
lowing subsections report an aggregation of the data
we collected for all these categories of events.
Conserved Regions and Mobile Elements. More
than 95% in Y55 and 93% in YPS128 are conserved re-
gions. Most of these are part of the resident genome,
but not all of them. The fraction of conserved trans-
posons or LTRs (i.e. mobilome) within conserved re-
gions contains two possible elements: the truly con-
served transposons (only 1) or LTRs (in a relative low
number), which are annotated onto the RefSeq and
exactly mapped on the screened strain and the puta-
tive conservations of annotated transposons or LTRs,
which are mapped onto unresolved sequences in the
screened strain: in this case, a direct attribution is
impossible. The pCONSu are always found in the
telomeres because the presence of long repeats is a
source of noise for the assembly phase. In all cases
but one, telomeres do not involve sequences related
with mobile elements. Concerning pCONSu that are
outside the telomeres, the number of unresolved se-
quences that are located in correspondence or in prox-
imity of mobile elements, is greater than 90% for Y55
and around 70% for YPS128. This supports the hy-
pothesis that unresolved regions are often located in
correspondence of a mobile element (annotated onto
Deletions. Deletions concern almost only the mo-
bilome. In Y55 strain, for instance, there are four pu-
tative deleted regions on RefSeq that do not corre-
spond to annotated Ty or soloLTR (against more than
90 pDELs corresponding to mobilome annotations).
We found that the length of the two regions is com-
patible to that of a soloLTR. This evidence strongly
suggests that in genomes closely related, the only sig-
nificant (i.e., for our work, those involving sequences
at least 200 long) chromosomal rearrangements are
due to the mobilome.
Insertions. The landscape for the putative inser-
tions, without (pINS) and with (pINSu) unresolved
regions is rich. We may label the inserted sequences
by proximity to annotated Ty or soloLTR in the inser-
tion site on RefSeq: from 40% to 50% of the cases,
it is a putative mobilome-proximal insertion. As for
deletions, we may also distinguish on the basis of in-
serted sequence length: Ty-c, LTR-c or in-between.
Also in this case, the large majority of events are con-
cerned with the mobilome. Even if this result is partly
derived from our classification methods, it supports
the same conclusion anyway.
We proposed an approach aimed at a rapid and ef-
ficient localization of the resident genome through
algorithm REGENDER. We shall generalize this ap-
proach to a multiple comparison extracting all the
chromosomal rearrangements on a dataset of 39
strains of the same specie S. cerevisiæ (Liti et al.,
2009). Our long term goal is to develop a model able
to describe the dynamics of the mobilome in these
We thank Emiliano Biscardi for performing tests
whose results are reported in Table 2.
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