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Journal of Clinical Microbiology, May 1998, p. 1318-1323, Vol. 36, No. 5
0095-1137/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
Computerized Analysis of Restriction Fragment
Length Polymorphism Patterns: Comparative Evaluation of Two
Commercial Software Packages
P.
Gerner-Smidt,1,*
L. M.
Graves,2
Susan
Hunter,2 and
B.
Swaminathan2
Department of Gastrointestinal Infections,
Statens Seruminstitut, Copenhagen, Denmark,1
and
Foodborne and Diarrheal Diseases Branch, Division of
Bacterial and Mycotic Diseases, Centers for Disease Control and
Prevention, Atlanta, Georgia2
Received 15 July 1997/Returned for modification 14 January
1998/Accepted 13 February 1998
 |
ABSTRACT |
Two computerized restriction fragment length polymorphism pattern
analysis systems, the BioImage system and the GelCompar system
(Molecular Analyst Fingerprinting Plus in the United States), were
compared. The two systems use different approaches to compare patterns
from different gels. In GelCompar, a standard reference pattern in one
gel is used to normalize subsequent gels containing lanes with the same
reference pattern. In BioImage, the molecular sizes of the fragments
are calculated from size standards present in each gel. The molecular
size estimates obtained with the two systems for 12 restriction
fragments of phage 
were between 97 and 101% of their actual sizes,
with a standard deviation of less than 1% of the average estimated
size for most fragments. At the window sizes used for analysis, the
GelCompar system performed somewhat better than BioImage in identifying
visually identical patterns generated by electrophoretic separation of
HhaI-restricted DNA of Listeria monocytogenes.
Both systems require the user to make critical decisions in the
analysis. It is very important to visually verify that the systems are
finding all bands in each lane and that no artifacts are being
detected; both systems allow manual editing. It is also important to
verify results obtained in the pattern matching or clustering portions
of the analysis.
| |
INTRODUCTION |
Electrophoresis-based fingerprinting
methods are now routinely used for epidemiological typing of bacteria
and fungi. The first methods used included analysis of whole-cell or
cell envelope protein patterns and classical DNA fingerprinting methods
such as restriction enzyme analysis (REA), ribotyping, and insertion sequence fingerprinting. Later, macrorestriction analysis of genomic DNA by the use of low-frequency-cutting restriction enzymes and pulsed-field gel electrophoresis was introduced. Most recently, PCR-based methods such as random amplification of polymorphic DNA,
typing by amplification of genomic DNA between repetitive extragenetic
palindromic sequences, and amplified DNA restriction analysis have been
described (6). These electrophoretic methods have mostly
been used to compare results obtained within one experiment on the same
gel. Recent efforts to standardize the methods and the development of
computer-based pattern analysis methods have made it possible to
compare large numbers of patterns generated in the same or different
laboratories.
With these computer-based pattern analysis programs, it is possible to
build up databases of DNA restriction fragment length polymorphism
(RFLP) patterns and perform identity searches of new patterns in these
databases. In addition, the software packages can perform more
sophisticated similarity calculations and cluster analyses of the
patterns in the databases. They are now increasingly being used
(3-5, 7, 8) for epidemiological typing, but to our
knowledge different software packages have not been compared.
In the present study, we compared two such systems, the GelCompar
(Applied Maths, Kortrijk, Belgium; sold as Molecular Analyst Fingerprinting Plus by Bio-Rad Laboratories, Hercules, Calif.) and the
BioImage system (BioImage Corporation, Ann Arbor, Mich.), to determine
if results generated by the two programs were comparable. These two
systems were chosen for the comparison because they use different
approaches to the analysis of the banding patterns. Both systems have
automatic band-finding options as well as molecular size estimation,
similarity calculation, and cluster analysis features.
| |
MATERIALS AND METHODS |
Image analysis systems.
The GelCompar program (version 3.1)
program runs under Microsoft Windows (version 3.1 or higher). The
suggested minimum hardware configuration is a personal computer with an
Intel 386 processor, 4 megabytes of random-access memory, 10 megabytes
of free space on the hard disk, and a color monitor and a videocard
with a minimum resolution of 256 colors. The program runs very slowly
with this configuration, and in the present study it was installed on a Hewlett-Packard Vectra computer equipped with a 66-MHz 486 processor and 12 megabytes of random-access memory. A complete system, consisting of the software program, an image acquisition camera, UV and white light sources, a computer, and a printer, is available from Bio-Rad under the names Gel Doc 1000 and Molecular Analyst Fingerprinting Plus.
The BioImage system (version 3.2) runs on a Sun microcomputer equipped
with a UNIX operating system. This system can be purchased complete
with the software, an image acquisition camera, UV and white light
sources, a computer, and a printer.
Experimental setup.
A mixture of adenovirus type 2 BamHI-EcoRI DNA fragments and StuI
fragments of phage was used as a reference standard and molecular
size marker throughout the study. The mixture was prepared as follows.
An adenovirus DNA fragment suspension was made by mixing 2.6 µl of
adenovirus BamHI-EcoRI fragments (ca. 0.9 µg; IBI, New Haven, Conn.) with 22 µl of gel loading buffer (50% sucrose and 0.25% bromophenol blue in 100 mM Tris-90 mM boric acid-1 mM EDTA, pH 8.0 [TBE]) and 75.4 µl of 10 mM Tris-1 mM EDTA buffer, pH
8.0. The phage fragment stock solution was prepared by digesting 1 µg of phage (Boehringer Mannheim, Indianapolis, Ind.) with StuI (Gibco/BRL, Gaithersburg, Md.) in a 20-µl volume in
accordance with the instructions of the manufacturer of the restriction
enzyme before adding 22 µl of gel loading buffer and 58 µl of 10 mM
Tris-1 mM EDTA buffer, pH 8.0. The final adenovirus- fragment
solution was prepared by mixing 70 µl of the adenovirus DNA with 56 µl of the StuI fragment solution. Eighteen microliters
of this mixture was loaded onto the gel in each standard lane.
In the first part of the study,
EcoRI (Gibco/BRL) and
StyI (Gibco/BRL) digests of phage
were placed between
the standards
in two gels (gels I and II) to test the accuracy of the
molecular
size determinations of the systems. The solutions of these
fragments
were prepared by digesting 2-µg amounts of phage
DNA separately
with each enzyme, in accordance with the manufacturer's
instructions,
in a volume of 40 µl. After digestion, 44 µl of gel
loading buffer
and 116 µl of 10 mM Tris-1 mM EDTA buffer, pH 8.0, were added
to each digestion tube. Ten microliters of each solution was
applied
to each lane. In one of the gels (gel II), a heavy load of
HindIII
(Gibco/BRL) phage
fragments was placed in
two lanes instead
of the
EcoRI fragments to create
distortion of the migration of
the fragments in the adjacent tracks in
the gel. This solution
was threefold more concentrated than the
solutions with the
EcoRI
and
StyI fragments. In
the second part of the study, the abilities
of the software programs to
analyze the more complex restriction
patterns of DNA from 18 isolates
of
Listeria monocytogenes were
determined (Table
1). For this REA typing procedure,
genomic
DNA was purified by the method of Graves and Swaminathan
(
2).
It was digested with
HhaI (Gibco/BRL) in
accordance with the manufacturer's
instructions and electrophoresed in
two gels (gels III and IV)
with a reference standard in every third or
fourth lane. The DNA
of five isolates was run in both gels.
View this table:
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TABLE 1.
Information on the clinical isolates of L. monocytogenes whose DNA was used in the pattern recognition
portion of the study
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|
Electrophoresis was done in 0.65% SeaKem agarose (FMC BioProducts,
Rockland, Maine) or ultrapure agarose (Gibco/BRL) in 1×
TBE buffer, pH
8.0, in a Horizon 20.25 electrophoresis chamber
(Gibco/BRL) at
approximately 2 V/cm overnight in TBE at ambient
temperature. In the
experiment with
L. monocytogenes DNA, the
electrophoresis
was done with circulation of the electrophoresis
buffer. After
electrophoresis, the gels were stained with ethidium
bromide (10 mg/liter) and photographed on a UV table, using a
Polaroid camera and
Polaroid negative black and white film 667.
For computer analysis, all
images were scanned on a light table,
using a BioImage camera and an
earlier version of BioImage software
(3.0). In contrast to BioImage
version 3.2, the older version
generates uncompressed TIFF files that
may be read by the GelCompar
software. The images in TIFF format were
transferred to an MS-DOS-formatted
diskette for analysis by the
GelCompar program. A resolution of
1,024 by 1,024 pixels was used for
image acquisition. The normalization
settings in GelCompar were as
follows: a resolution of 400 points,
a smoothing factor of 3 (each data
point was averaged with one
point on each side), and background
subtraction by the rolling
disc method with a setting of 12 as
recommended by the manufacturer
of RFLP gels. Bands were identified by
the band search features
of both systems. The sensitivity of the band
search feature was
set so that all bands present were identified by
both systems.
With these settings, some artifacts on the images were
identified
as bands; these were manually deleted. In the pattern
recognition
part of the study, only bands in the size range between 3.5 and
14.3 kb were analyzed. No strain's DNA contained a band larger
than 8.4 kb. The positions of bands smaller than 3.5 kb could
not be
reliably ascertained because the bands in this region were
not
completely resolved. All molecular size marker bands in the
reference
lanes were included in the calculation of the sizes
of the bands in the
test lanes. This was done to determine the
sizes of the bands in the
test lanes by interpolation from the
reference lane data. The
"robust" method in BioImage and the "spline
fitting" method in
GelCompar were used for interpolation in the
molecular size
determination procedure. Neither of these methods
produces reliable
results if the sizes of bands in the test lanes
are outside the range
of the bands in the reference lanes; i.e.,
the methods do not work well
with extrapolation. For pattern recognition,
the optimization feature,
a track-to-track alignment feature that
recognizes small global shifts
(up to 4% migration differences)
in similar normalized patterns that
are not perfectly aligned,
was enabled in GelCompar. A similar feature
is not present in
BioImage. Similarities between patterns were
determined by generating
dendrograms. The Dice similarity coefficient
(similarity coefficient
2 in BioImage) (
1) was used, and the
patterns were clustered
by the unweighted pair group method using
arithmetic averages
(UPGMA). The influence of using different
numbers of lanes containing
molecular size markers on a gel was
ascertained by comparing the
gels on which all marker lanes were used
with the same gels on
which the two outermost marker lanes only were
used.
| |
RESULTS |
A gel with reference molecular size standards as well as
EcoRI, StyI, and HindIII phage fragments is shown in Fig. 1. The average
molecular sizes (both the observed values and their percentages of the
expected values) and the associated standard deviations (both the
determined values and their percentages of the observed average
molecular sizes) for the StyI and EcoRI fragments are shown in Table 2. The two
programs estimated the molecular sizes with nearly the same precision.
The average size calculated for each fragment was within 2% of the
actual size of the fragment, except for the largest StyI
fragment, which by BioImage was estimated to have a size 2.9% smaller
than expected. The standard deviations for fragments of less than 7,743 bp were less than 1% of the calculated average sizes, except for the
two smallest StyI fragments, which by BioImage were 1.1 and
2.5%, respectively, of the calculated average molecular sizes. By both
systems, the standard deviations for the two largest fragments were
between 1.7 and 3.2% of their calculated average sizes. Because of
this, the position tolerance (the maximum positional deviation between
two identical fragments run in different lanes) was set to 0.5% with
an increase of 1.75% in GelCompar, corresponding to a 3% deviation in
molecular weight throughout the gel for the pattern comparison portion
of the study. Likewise, the deviation in BioImage was set at 3%
throughout the gel.
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FIG. 1.
A distorted gel (gel II) containing molecular size
markers (M) and EcoRI, StyI, and
HindIII fragments of phage . The distortion was
created by overloading the lanes with the HindIII phage
fragments and by electrophoresing without recirculation of the
buffer. The molecular size standard is a mixture of adenovirus type 2 BamHI-EcoRI fragments and StuI phage
fragments. From the top, the sizes of the fragments are 21.4, 19.0, 14.3, 12.4, 10.7, 7.9, 7.0, 6.2, 5.9, 4.7, 4.3, 4.0, 3.7, and 2.7 kb.
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|
The results of the pattern recognition study are shown in Fig. 2 to
5.
Figure 2 shows HhaI-digested genomic DNA of strains of
L. monocytogenes. The effect of circulation of the
electrophoresis buffer can be judged by comparing this figure with Fig.
1. There is no "smiling" effect in the gel run with circulation of
the buffer (Fig. 2), while there is a pronounced smiling in the gel run
without buffer circulation (Fig. 1). The molecular size
standards also show better separation in Fig. 2 than in Fig. 1. In Fig. 3, the clustering of the RFLP patterns of the bacterial isolates, the
fragments, and the molecular size marker fragments by GelCompar is
shown. All marker lanes were used for normalization in this experiment.
One of the marker lanes in one of the gels with the clinical-strain DNA
was used as a reference standard in the normalization procedure. All
patterns were correctly identified in this figure. The corresponding
dendrogram generated by BioImage for the same patterns is shown in Fig.
4. The clustering of patterns by BioImage was less satisfactory than
that obtained with GelCompar. The BioImage software matched the
-fragment patterns with the 3% deviation window setting. When rerun
on gel IV, isolates 6 and 2 were not clustered with 100% similarity to
their run on gel III. The patterns of isolates 15 and 16 were
falsely judged to be identical; they differ in the position of a single
band in the region between 3.6 and 4 kb (Fig. 3). However, the overall
clustering was similar in BioImage and GelCompar. Figure 5
shows the clustering of the RFLP patterns of all isolates studied, all
fragments, and the molecular weight marker fragments by GelCompar
after normalization had been performed with only the outermost
molecular size marker lanes. Compared to Fig. 3, for which
normalization was optimal, the dendrogram in Fig. 5 is much more
branched. This effect is seen only in the patterns in the gels with the
most pronounced smiling effect, i.e., the two gels with the
EcoRI, StyI, and HindIII fragments. Inclusion of a third molecular weight marker lane in
the middle of the smiling gels improved the results substantially but
without reaching the level of perfection attained when all marker lanes
were included in the calculation (data not shown). Similar results were
found when only the two outermost standards in each gel were used for
analysis of the data by BioImage (data not shown).
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FIG. 2.
HhaI restriction fragments of DNA from
isolates of L. monocytogenes (gel IV). The molecular size
standards are as described in the legend to Fig. 1. The identification
numbers of the L. monocytogenes isolates are indicated at
the top with their REA pattern designations in the parentheses. The gel
was electrophoresed with recirculation of the running buffer.
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FIG. 3.
A GelCompar-generated UPGMA clustering dendrogram with
error flags and corresponding normalized restriction profiles. Only
visually unique patterns and patterns identified as being different by
BioImage (Fig. 4) are shown. Normalization was done using all molecular
standards in all gels. The clustering was based on the bands enhanced
in the figure. The Dice similarity coefficient was used, and the
optimization feature was enabled. On the right, the gel numbers and the
sources of the profiles are indicated. The pattern designations of the
Listeria restriction profiles are indicated in the
parentheses. The scale at the top of the figure shows percent
similarity. M, molecular size markers.
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FIG. 4.
The BioImage-generated UPGMA clustering dendrogram
corresponding to the GelCompar dendrogram shown in Fig. 3. The scale at
the bottom of the figure shows percent similarity. M, molecular size
markers.
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FIG. 5.
A GelCompar-generated UPGMA clustering dendrogram with
error flags and corresponding normalized restriction profiles.
Normalization was done with only the outermost standards in each gel,
and only profiles identified by the software as being different are
shown. All parameters and designations used are as described in the
legend to Fig. 3. The scale at the top of the figure shows percent
similarity. M, molecular size markers.
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| |
DISCUSSION |
RFLP pattern analysis software programs enable investigators to
compare large numbers of complex patterns in a short period of time.
The programs in this study use different approaches to compensate for
differences in run length and smiling of the gels. Before analysis can
be performed with GelCompar, the patterns have to be normalized. In
this process, a lane containing a standard profile is selected as a
reference. This standard should also be present in all other gels. The
standards are then compressed or stretched to match the profile in the
reference lane as closely as possible. The test profiles in between are
normalized by interpolation to the nearest standard lanes. The
normalized profiles are saved and used for future analyses; information
on the sizes of the bands in the standards is not required. With
BioImage, all patterns are compared as molecular sizes based on
molecular size markers in each gel. A process like the normalization
procedure in GelCompar is not needed with BioImage. Thus, GelCompar
compares differences in positions, i.e., run lengths, on the gels
rather than differences in molecular sizes, the approach used by
BioImage.
Both software programs allow storage of the patterns in one or more
databases in a computer so that a new pattern can be compared with
existing patterns in the database(s). This is particularly useful for
tracking specific subtypes of bacteria in epidemiological studies. The
two software programs evaluated in this study have several features
(e.g., band finding, lane comparison, etc.) that allow many of the
tedious and time-consuming steps in pattern analysis to be
automatically performed with one or a few keystrokes. Also, they have
powerful built-in data analysis features and can generate reports in
several formats. Both programs have lane-matching features that will
report the total number of bands as well as the number of matched and
unmatched bands. This feature is useful for discriminating between
unrelated strains; however, the analyst should visually confirm the
band-matching results from the programs. Both programs require the
analyst to make critical decisions during the various steps in pattern
normalization and in selecting the parameters used for analysis and
matching of patterns. The analyst needs to become familiar with various
features of each program before he or she can obtain the best results.
The GelCompar program has several methods of aligning positions on the
reference patterns (identified by the operator) within a gel with each
other and with a previously chosen reference standard. In our
experience, the automatic association methods often produce incorrect
alignments and require operator input to make corrections and to align
positions appropriately. The BioImage system also has an automatic
alignment feature to align identical fragments of reference standards
in different lanes on a gel. This alignment feature may not perform satisfactorily if the rates of migration of fragments in different lanes are significantly different (smiling effect). The program draws a
horizontal line through identical bands in the reference lanes. These
lines can be adjusted manually by the operator as needed to improve the
alignments. These adjustments must be made correctly or molecular sizes
will not be determined accurately. The BioImage software pattern
matches are determined on the basis of molecular size values. The
GelCompar program does not use computed molecular sizes of bands for
matching but rather uses normalized positions. The normalization
process in GelCompar is quite time-consuming for the newcomer, whereas
the molecular size estimation by BioImage is straightforward.
With both of these software programs, the molecular size estimates for
the majority of the test fragments were within 98% of their actual
sizes. The standard deviations for the molecular weights of the large
fragments were larger than those for the smaller fragments. This was to
be expected because the bands from the larger fragments were broader
than the bands from the smaller fragments. Both software programs
choose the most intense point in each band as the position of the band.
Many scientists routinely place molecular weight standards in only the
outermost lanes in their gels. Based on the results of the present
study, this practice needs to be reevaluated since it is very difficult
to avoid smiling or other types of distortion in every gel. More highly
branched dendrograms were obtained with both software programs when
only outer-lane standards were used. Somewhat less branching was seen
when three lanes were used for standards. Smiling may be partly avoided
if the buffer is recirculated during electrophoresis. However, it is
very difficult to avoid gel distortion caused by small differences in
the amount of DNA loaded in each lane. The normalization process may be
improved by placing the same strain ("standard" strain) in
different positions on each gel. An ideal fragment analysis system
generates variable as well as invariant bands. Invariant bands may
serve as internal controls and help correct for gel distortions.
In this study, with the deviation percentage used, the BioImage
software did not cluster isolates 6 and 2 with 100% similarity when
they were run on two different gels. Two isolates, 15 and 16, which
differed from each other in the position of a single band in the 3.6- to 4.0-kb range, were falsely judged to be identical. BioImage does
some proofreading of the band matching if the bands compared are within
the size deviation set by the user. This did not work with the size
deviation chosen in this study (3%). Deviations of 2.5, 3.5, and 4%
gave similar results (data not shown). If the deviation was set to 2%,
BioImage correctly judged the profiles of strains 15 and 16 to be
different. However, the profiles of the aforementioned two strains (6 and 2) run on different gels were still misinterpreted as being
different when this setting was used (data not shown). These problems
were not seen with GelCompar when the optimization feature was enabled.
If this feature was disabled, a dendrogram similar to the BioImage
dendrogram was obtained (data not shown). The overall relationships of
the patterns analyzed were identical for the two programs and
similar to the one expected from visual inspection of the gels. In
GelCompar, dendrograms with error flags at the branchings (standard
deviations with respect to the similarity matrix for each branch) may
be produced (Fig. 3 and 5). This feature is not present in BioImage. The two RFLP pattern analysis software programs performed almost equally well in determining molecular weights from standards present in
each gel and in recognizing relationships between banding patterns.
The development of computer programs for automated analysis of DNA RFLP
patterns has made it possible to perform sophisticated comparisons of a
large number of complex patterns. However, the programs are not
completely automatic but require the user to make critical decisions
that affect the way in which the analysis is done and the final
results. Similarly, the results of a very distorted gel cannot be
corrected by any computer program. It is extremely important to verify
the results of all computerized RFLP pattern analyses. This
includes visual comparisons of the number of bands in a lane with the
bands found automatically by the software programs. It may be useful to
include DNA from at least two identical strains on each gel to verify
the ability of the software to recognize identical patterns at the
chosen settings. It should be stressed that computer programs may be used as an aid in the analysis of complex banding patterns; they do not
provide an undisputably correct analysis.
In conclusion, the two programs evaluated in this study performed well.
Of the two programs evaluated, BioImage was the easier to use. The
version of BioImage evaluated in this study was written for UNIX-based
computers; since this study was undertaken, BioImage Corporation has
released a version of its software that is designed for Microsoft
Windows (3.1 and later versions). This version is reported to have most
of the features of the UNIX-based software. The GelCompar program
software is designed for Microsoft Windows (3.1 and later versions) and
has some useful features, like optimization of profiles and error flags
on the similarity dendrograms, not present in BioImage. GelCompar is
not as easy to work with as BioImage, but it performed slightly better
than the latter program in the present study.
In the present paper, we have considered a few important features that
are common to most image analysis systems. When considering the
purchase of such a program, additional features may be important. These
include statistical, combining, printing, exporting, and program
linkage capabilities. The two image analysis programs we have tested
will not fulfill the requirements of all laboratories. These software
packages are usually priced at approximately $6,000 (U.S.) for a
single-user version. Thus, before you decide on a purchase, insist on a
free trial period to test the system thoroughly before you buy it.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department of
Gastrointestinal Infections, Statens Seruminstitut, Artillerivej 5, DK-2300 Copenhagen S, Denmark. Phone: (45) 32 68 37 98. Fax: (45) 32 68 38 73. E-mail: PGS{at}SSI.DK.
| |
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Journal of Clinical Microbiology, May 1998, p. 1318-1323, Vol. 36, No. 5
0095-1137/98/$04.00+0
Copyright © 1998, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
