Imagine that you are a Federal Bureau of Investigation (FBI) investigator, and you are speaking to new recruits at the FBI training facility in Quantico. Your topic revolves around interview/interrogation techniques and profiling of suspects. You begin discussing a serial killer case that you have just been assigned. Because you have just begun your investigation, you have called on the services of a crime scene profiler to begin narrowing down suspects with whom you want to speak.
This project should cover the following topics:
a brief description of crime scene profiling and its various types and
a description of which type(s) of profiling you feel would be the best to use for your specific case and why you feel that way.
You will also discuss how you might interview/interrogate individuals once you begin narrowing your focus on specific suspects. Be sure to include the following topics:
which type of interview/interrogation technique you will use and how you determined the type and
a description of how you might use the technique that you chose when interviewing a suspect.Kinebuchi et al. BMC Genetics
The genome profiling method can be
applied for species identification of
biological materials collected at crime
Takako Kinebuchi, Nozomi Idota, Hajime Tsuboi, Marin Takaso, Risa Bando and Hiroshi Ikegaya*
Background: Various biological materials unrelated to humans are found at crime scenes and it is often important
to elucidate the origin of these materials. A genetic locus common to several species is conventionally PCRamplified with universal primers to identify species. However, not all species can be identified using a single locus.
In this study, DNA from 13 commonly handled taxa was analyzed to identify species by a genome profiling (GP)
method, which involves random PCR and temperature gradient gel electrophoresis.
Results: In a clustering analysis, we successfully obtained a single cluster for each species.
Conclusion: The GP method is cost-effective and does not require advanced techniques and knowledge in
molecular biology. The random sampling of the whole genome using multiple primers provides substantial
genomic information. Therefore, the method is effective for classifying a wide range of species, including animals,
plants, and insects, and is useful for crime scene investigations.
Keywords: Forensic science, Genome profiling, Species identification, Detection, Classification, Random primers
Several methods to detect human-specific DNA have been
developed to identify victims or suspects, using biological
materials such as blood stains, hair, small tissue particles,
and body fluids found at crime scenes [1–3]. It is important to identify materials derived from humans; however,
for materials that are not of human origin, it is often still
important to determine the source. For instance, this can
be important in cases where feces are placed in front of a
house, when hair or bone fragments are mixed in a food
manufacturing factory, or when wild animals cause agricultural damage. Species identification from biological
materials found at a crime scene can inform subsequent
For non-human biological materials, such as meat
pieces, blood, hair, and bone fragments, the amplification
* Correspondence: email@example.com
Department of Forensic Medicine, Graduate School of Medical Science,
Kyoto Prefectural University of Medicine, 465 Kajiicho, Kamigyo, Kyoto
of a species-specific locus by PCR is commonly performed
[4–8]. However, only expected creatures can be investigated using this approach. There are many methods for
amplifying sequences common to multiple species using
universal primers, such as the amplification of mitochondrial rDNA [9, 10], cytochrome b [11, 12], cytochrome
oxidase I [13, 14], myoglobin , or the D-loop region
[16–20] and subsequent identification using the Basic
Local Alignment Search Tool. Even if sequences for many
species are registered in the database, the location of the
sequence varies. Accordingly, there is a limit to species
identification based on a single locus. In addition, even if
samples belong to the same species, there may be
individual differences in DNA sequences. To address these
issues, analyses of additional loci and molecular phylogenetic approaches are needed. However, these methods are
relatively expensive and require expertise and equipment.
In this study, we focused on the genome profiling (GP)
method. The GP method was developed by Nishigaki et
al. in the bioindustry field in 1971 . In the GP
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kinebuchi et al. BMC Genetics
Page 2 of 7
method, DNA is PCR-amplified using random primers
(random PCR) and temperature gradient gel electrophoresis (TGGE) is performed. Depending on the number of
amplified fragments and the melting temperatures of the
double-stranded DNA, species identification dots (spiddos)
are obtained on the temperature gradient gel. These spiddos
are corrected using internal standards. Then, the spiddos
pattern is compared between samples and references and a
pattern similarity score (PaSS) is obtained. Using this PaSS,
a cluster analysis is performed to identify species.
This method corresponds to random sampling in statistics. It is possible to analyze information for the entire
genome at a very low cost in a short period of time,
without requiring any special knowledge or techniques.
Using this GP method, we have identified human body
fluid [22, 23] and viral species  and differentiated between humans and other mammalian species .
The GP method is highly sensitive and accordingly it
can potentially be used like the Ames test for mutagen
analyses . Additionally, the potential for personal
identification in humans has also been reported . In
this study, we selected a wide range of target organisms,
including common fish, birds, and various mammals,
and examined whether it is possible to identify these organisms using the GP method.
The average numbers of spiddos obtained using SP-1
to − 3 were 27.8 for cattle, 33.0 for pigs, 26.8 for sheep,
36.0 for chickens, 40 for greater amberjack, 32.5 for bigeye tuna, 35.8 for silver salmon, 30.8 for Japanese horse
mackerel, 30.0 for Japanese halibut, 33.0 for dog, 36.8
for cat, 16.3 for rat, and 31.0 for human.
Phylogenetic trees were generated using PaSS values
for each primer. Using the SP-1 primer, the same species
were classified into a single cluster for cattle, sheep,
Japanese halibut, greater amberjack, Japanese horse
mackerel, and rat. However, mixed clusters were obtained for other species (Fig. 1a). Using the SP-2 primer,
the same species were classified into a single cluster for
cattle, pig, chicken, greater amberjack, bigeye tuna, silver
salmon, Japanese horse mackerel, Japanese halibut, rat,
and human. However, mixed clusters were obtained for
sheep (Fig. 1b). Using the SP-3 primer, the same species
were classified into a single cluster only for chicken and
Japanese halibut, and the other species formed mixed
clusters (Fig. 1c). Cluster formation was not related to
the number of spiddos.
When a cluster analysis was performed based on average PaSS values for SP-1, SP-2, and SP-3, samples from
the same species were classified into the same cluster for
all species (Fig. 2).
Four or more spiddos were obtained for all samples, regardless of species (Table 1). The average number of spiddos obtained from each sample was 11.7 ± 3.1 (range
5–21) for SP-1, 12.7 ± 3.8 (range 5–22) for SP-2, and
7.0 ± 3.0 (range 4–16) for SP-3.
In the field of forensic medicine, DNA typing by short
tandem repeat (STR) analysis is generally accepted for
the personal identification of biological materials such as
blood, semen, and saliva stains collected at a crime scene
[28, 29]. However, it is often presumed that blood stains
are human in origin. First, it is important to confirm
whether the material is derived from humans. For nonhuman samples, a universal method for species identification is needed.
Though there is a report that simple sequence repeat
(SSR) is very effective for determining the differences
among species , many reports using a genetic approach, including STR or other methods, determined
the differences in the same species. The GP method can
be used to detect all species, including humans, animals
, insects, plants , bacteria , and fungi ,
following a single protocol. Using the GP method, results can be obtained in a few hours, including random
PCR and 10 min of TGGE. The cost is only a few dollars
For the GP method, the accuracy of species determination depends on the number of spiddos obtained by
random PCR. A spiddos shows not only the cleaved
temperature of the double-stranded DNA fragment
amplified by random PCR, but also specific DNA sequences. Therefore, analyzing spiddos is the same as
performing random sampling for large-scale genome
Table 1 Number of spiddos in various animal species, obtained
using three different random primers
Cattle (n = 4)
9.0 ± 2.0
12.3 ± 3.6 6.5 ± 1.0
27.8 ± 4.6
Pig (n = 4)
10.5 ± 1.3 16.0 ± 1.8 6.5 ± 2.1
33.0 ± 4.2
Sheep (n = 4)
11.3 ± 1.5 9.75 ± 3.4 5.8 ± 1.0
26.8 ± 4.3
Chicken (n = 4)
14.0 ± 4.9 13.0 ± 1.4 9.0 ± 1.2
36.0 ± 5.6
Greater amberjack (n = 4) 14.8 ± 3.6 19.0 ± 2.1 6.3 ± 1.9
40.0 ± 2.9
Bigeye tuna (n = 4)
14.8 ± 2.5 12.5 ± 1.9 5.3 ± 1.0
32.5 ± 2.4
Silver salmon (n = 4)
14.8 ± 2.2 14.8 ± 1.3 6.3 ± 1.3
35.8 ± 3.3
Horse mackerel (n = 4)
10.8 ± 0.5 14.8 ± 1.5 5.3 ± 1.9
30.8 ± 1.9
Halibut (n = 4)
11.8 ± 2.9 13.0 ± 3.3 5.3 ± 1.0
30.0 ± 4.4
Dog (n = 3)
11.0 ± 1.4 12.3 ± 1.7 9.8 ± 2.1
33.0 ± 4.1
Cat (n = 3)
10.5 ± 1.3 13.3 ± 3.3 13.0 ± 2.5 36.8 ± 5.5
Rat (n = 4)
5. 3 ± 1.0
6.5 ± 1.3
16.3 ± 1.5
Human (n = 3)
13.3 ± 2.1 11.3 ± 2.5 6.3 ± 1.5
31.0 ± 2.6
11.7 ± 3.1 12.7 ± 3.8 7.0 ± 3.0
Numbers are shown as averages ± S.D
4,5 ± 1.0
Kinebuchi et al. BMC Genetics
Page 3 of 7
Fig. 1 Cluster analysis of 12 animal species and human. Analysis was based on PaSS values calculated from the GP method using the random
primer a SP-1, b SP-2, and c SP − 3
information. For this reason, primers that can amplify a
larger number of spiddos are useful to maximize the
genome information that is obtained.
However, there is a limit to the number of spiddos that
can be obtained using a single primer. Accordingly, the
number of spiddos can be increased by increasing the
number of random primers, resulting in a more accurate
cluster analysis . In our study, we were able to classify species more accurately using the average value of
the results obtained using three primers (Fig. 2) than
using each of the three primers separately (Fig. 1a, b,
and c). By using multiple random primers, even closely
related species, such as the cattle and sheep groups or
the greater amberjack and horse mackerel groups, can
be classified correctly. Our results indicate that species
identification by the GP method using multiple random
primers is very accurate.
Classification based on a very small part of the whole
genome is sometimes difficult for closely related species.
Moreover, the reliability of the results may be insufficient when the whole genome is not analyzed. The GP
method, in which the whole genome is analyzed by random sampling, addresses these limitations and is sufficient for species classification. However, the GP method
has various limitations. First, it is difficult to apply to
mixed samples. Although mixed samples were not examined in this report, both genomes are expected to be
randomly amplified, and it may be difficult to determine
their species. In previous studies of virus detection using
body fluids, we found species-specific spiddos [23, 24].
Kinebuchi et al. BMC Genetics
Page 4 of 7
There may be specific spiddos in various species. Further
studies are required to evaluate this species specificity. A
second problem is related to the reference samples.
Although there are international databases for DNA
sequences, a database of TGGE images does not exist.
Therefore, it is necessary to prepare DNA for the suspected species once to obtain images for comparison.
Despite the issues described above, the GP method can
save time, labor, and costs, and although it is a random
sampling approach, it can be used to obtain whole genome information. Therefore, the GP method is also an
effective approach for classifying species and can be used
for criminal investigations.
Human and 12 species that are often found in the house
or household kitchens were used in this study, i.e., cattle
(Bos taurus), pig (Sus scrofa domesticus), sheep (Ovis
aries), chicken (Gallus Gallus domesticus), greater amberjack (Seriola dumerili), bigeye tuna (Thunnus obesus),
silver salmon (Oncorhynchus kisutch), Japanese horse
mackerel (Trachurus japonicus), Japanese halibut (Paralichthys olivaceus), dog (Canis lupus familiaris), cat
(Felis catus), and rat (Rattus norvegicus). The meats of
cattle, pig, sheep, chicken, greater amberjack, bigeye tuna,
silver salmon, Japanese horse mackerel, and Japanese halibut were purchased at a grocery store in the city market.
Samples were collected by scrubbing the surface of each
raw meat with a cotton swab. The dog, cat, and rat samples were obtained at pet shops by scrubbing the buccal
mucosa of each species with a cotton swab. Human samples were obtained from healthy adult volunteers who
provided written informed consent. The buccal mucosa of
each volunteer was scrubbed with a cotton swab. A total
of 49 samples (3–4 samples for each animal species and 3
samples for human) were collected.
Cotton swabs were digested with Proteinase K overnight at 56 °C. DNA was extracted using QIAamp® DNA
Mini Kits (Qiagen, Tokyo, Japan). The DNA concentration was adjusted to 5 ng/μL.
Fig. 2 Cluster analysis of 12 animal species and human. Analysis was
based on PaSS values calculated by the GP method using the
random primers SP-1, − 2, and – 3. Different samples from the same
individuals were classified into the same clade
Three random primers, SP-1 (pfm12) (5′-AGAACGCGC
CTG-3′), SP-2 (pfm19) (5′-CAGGGCGCGCGTAC-3′),
and SP-3 (hunt) (5′-TGCTGCTGCTGC-3′) were used
. PCR amplification was performed using a 50-μL reaction solution containing 4.0 μL of dNTP (2.5 mM each),
5.0 μL of Buffer, 3.5 μL of Ex Taq Polymerase (Takara Bio
Inc., Shiga, Japan), 10 mM each primer, and 1.0 μL of
extracted DNA. PCR was performed using the PC-320
Thermal Cycler (ASTEC, Fukuoka, Japan) as follows: 30
Kinebuchi et al. BMC Genetics
cycles of 94 °C for 30 s, 26 °C for 1 min, and 47 °C for 1
min, and a final extension at 47 °C for 5 min.
Two types of reference DNA were prepared as TGGE internal standards . For reference 1 (Ref1), PCR amplification was performed using a 50-μL reaction solution
containing 2.0 μL of M13 phage DNA (TaKaRa Bio, Inc.),
3.0 μL each of 10 μM MA1 (5′-TGCTACGTCTCTTCCGATGCTGTCTTTC-3′) and MA2 (5′-CCTTGAATTCTATCGGTTTATCA-3′), 4.0 μL of dNTP (2.5 mM each),
5.0 μL of 10× Buffer, 0.15 μL of Ex Taq Polymerase
(Takara Bio Inc.), 10 mM primers, and 1.0 μL of extracted DNA. PCR conditions were 30 cycles of 94 °C
for 30 s, 63 °C for 1 min, and 72 °C for 30 s, and a
final extension at 72 °C for 5 min.
For reference 2 (Ref2), PCR amplification was performed
using a 50-μL reaction solution containing 2.0 μL of M13
Page 5 of 7
phage DNA (TaKaRa Bio, Inc.), 3.5 μL each of 10 μM
GTTG-3′) and Ref6R (5′-TAGCGAGGTGCCGCCGG
CTTCCATTCAGGTC-3′), 4.0 μL of dNTP (2.5 mM
each), 5.0 μL of 10× Buffer, 0.25 μL of Ex Taq Polymerase (Takara Bio Inc.), 10 mM primers, and 1.0 μL of extracted DNA. PCR conditions were 30 cycles of 94 °C
for 15 s, 44 °C for 30 s, and 72 °C for 1 min, and a final
extension at 72 °C for 30 s.
Temperature gradient gel electrophoresis (TGGE)
A total of 1.0 μL of reference DNA solution was obtained by mixing the reaction solutions of Ref1 and Ref2
at a ratio of 1:1 and 1.0 μL of the PCR solution, followed
by electrophoresis. The mixed sample was applied to a
6% polyacrylamide gel and electrophoresed at 100 V
for 10 min with a temperature gradient of 15 °C to
65 °C [36, 37]. After electrophoresis, the gel was
Fig. 3 Representative images of electrophoresed gels. Human, horse mackerel, and cat sample images are shown. The electrophoresed gel image
is shown on the left side, and the corrected figure is shown in the right side
Kinebuchi et al. BMC Genetics
stained with 0.05% GelRed (Biotium Inc., Fremont, CA,
USA) and images were obtained using the LAS 4000 Mini
(FUJIFILM, Tokyo, Japan).
Page 6 of 7
The authors declare that they have no competing interests.
Received: 11 September 2018 Accepted: 29 May 2019
From the images of the electrophoresed gels, the melting
points of amplified double-stranded DNA (species identification dots: spiddos) were manually plotted. The spiddos were corrected by two reference DNA spiddos.
Representative images of the electrophoresed gels and
corrected figures are shown in Fig. 3.
The spiddo patterns of the samples were compared,
and PaSS was calculated according to the following formula using micro-TGGE Analyzer [38–42]:
” !! !!”
” ð1Þ ð2Þ ”
“P −P ”
” !!” ” !!”
PaSS ¼ 1‐
n i¼1 “” ð1Þ “” “” ð2Þ “”
“P i ” þ “P i ”
PaSS takes a value of 0 to 1, where 1 indicates a perfect match. This PaSS value was analyzed using the
Ward method to create a phylogenetic tree .
Additional file 1: PaSS values (SP-1). (CSV 18 kb)
Additional file 2: PaSS values (SP-2). (CSV 16 kb)
Additional file 3: PaSS values (SP-3). (CSV 18 kb)
Additional file 4: PaSS values (average of SP-1-3). (CSV 25 kb)
GP: Genome profiling; PaSS: Pattern similarity score; Ref1: Reference 1;
Ref2: Reference 2; STR: Short tandem repeat; TGGE: Temperature gradient gel
We thank Prof. Koichi Nishigaki for helpful discussion.
TK and NI performed random PCR and temperature gradient gel
electrophoresis. HT, MT, and RB analyzed the data obtained by
electrophoresis. HI was a major contributor in writing the manuscript. All
authors read and approved the final manuscript.
This research, including the design of the study; collection, analysis, and
interpretation of data; and writing of the manuscript, was funded by Kyoto
Prefectural University of Medicine.
Availability of data and materials
All data generated or analyzed during this study are included in this
published articles and its Additional files 1, 2, 3 and 4.
Ethics approval and consent to participate
This research was a part of the research project approved by an institutional
review board of Kyoto Prefectural University of Medicine (G-98).
Human samples were collected from healthy adult volunteers who provided
Consent for publication
1. Waye JS, Presley LA, Budowle B, Shutler GG, Fourney RM. A simple and
sensitive method for quantifying human genomic DNA in forensic
specimen extracts. Biotechniques. 1989;7:852–5.
2. Waye JS, Michaud D, Bowen JH, Fourney RM. Sensitive and specific
quantification of human genomic deoxyribonucleic acid (DNA) in forensic
science specimens: casework examples. J Forensic Sci. 1991;36:1198–203.
3. Soejima M, Hiroshige K, Yoshimoto J, Koda Y. Selective quantification of
human DNA by real-time PCR of FOXP2. Forensic Sci Int Genet. 2012;6:447–51.
4. Ng J, Satkoski J, Premasuthan A, Kanthaswamy S. A nuclear DNA-based
species determination and DNA quantification assay for common poultry
species. J Food Sci Technol. 2014;51:4060–5.
5. Karabasanavar NS, Singh SP, Kumar D, Shebannavar SN. Detection of pork
adulteration by highly-specific PCR assay of mitochondrial D-loop. Food
6. Wan QH, Fang SG. Application of species-specific polymerase chain reaction
in the forensic identification of tiger species. Forensic Sci Int. 2003;131:75–8.
7. Angleby H, Savolainen P. Forensic informativity of domestic dog mtDNA
control region sequences. Forensic Sci Int. 2005;154:99–110.
8. Kyle CJ, Wilson CC. Mitochondrial DNA identification of game and harvested
freshwater fish species. Forensic Sci Int. 2007;166:68–76 Erratum in: Forensic
Sci Int. 2008;180:59.
9. Kitano T, Umetsu K, Tian W, Osawa M. Two universal primer sets for species
identification among vertebrates. Int J Legal Med. 2007;121:423–7.
10. Cawthorn DM, Steinman HA, Witthuhn RC. Evaluation of the 16S and 12S
rRNA genes as universal markers for the identification of commercial fish
species in South Africa. Gene. 2012;491:40–8.
11. Parson W, Pegoraro K, Niederstätter H, Föger M, Steinlechner M. Species
identification by means of the cytochrome b gene. Int J Legal Med. 2000;
12. Thanakiatkrai P, Kitpipit T. Meat species identification by two direct-triplex realtime PCR assays using low resolution melting. Food Chem. 2017;233:144–50.
13. Wells JD, Wall R, Stevens JR. Phylogenetic analysis of forensically important
Lucilia flies based on cytochrome oxidase I sequence: a cautionary tale for
forensic species determination. Int J Legal Med. 2007;121:229–33.
14. Tobe SS, Kitchener AC, Linacre AM. Reconstructing mammalian phylogenies:
a detailed comparison of the cytochrome b and cytochrome oxidase
subunit I mitochondrial genes. PLoS One. 2010;5:e14156.
15. Ono T, Miyaishi S, Yamamoto Y, Yoshitome K, Ishikawa T, Ishizu H. Human
identification from forensic materials by amplification of a human-specific
sequence in the myoglobin gene. Acta Med Okayama. 2001;55:175–84.
16. Sullivan KM, Hopgood R, Gill P. Identification of human remains by
amplification and automated sequencing of mitochondrial DNA. Int J Legal
17. Wilson MR, DiZinno JA, Polanskey D, Replogle J, Budowle B. Validation of
mitochondrial DNA sequencing for forensic casework analysis. Int J Legal
18. Parson W, Parsons TJ, Scheithauer R, Holland MM. Population data for
101 Austrian Caucasian mitochondrial DNA d-loop sequences:
application of mtDNA sequence analysis to a forensic case. Int J Legal
19. Wittig H, Augustin C, Baasner A, Bulnheim U, Dimo-Simonin N, Edelmann J,
et al. Mitochondrial DNA in the Central European population. Human
identification with the help of the forensic mt-DNA D-loop-base database.
Forensic Sci Int. 2000;113:113–8.
20. Nakamura H, Muro T, Imamura S, Yuasa I. Forensic species identification
based on size variation of mitochondrial DNA hypervariable regions. Int J
Legal Med. 2009;123:177–84.
21. Nishigaki K, Amano N. Takasaka T. DNA profiling. An approach of systemic
characterization, classification, and comparison of genomic DNAs. Chem
22. Takasaka T, Sakurada K, Akutsu T, Nishigaki K, Ikegaya H. Trials of the
detection of semen and vaginal fluid RNA using the genome profiling
method. Leg Med (Tokyo). 2011;13:265–7.
Kinebuchi et al. BMC Genetics
23. Hirata R, Takasaka T, Miyamori D, Ahmed S, Sakurada K, Nishigaki K, et al.
Use of the genome profiling method for the identification of saliva and
sweat samples. Jpn J Forensic Sci Technol. 2013;18:79–83.
24. Tanaka Y, Hirata R, Mashita K, Mclean S, Ikegaya H. Detection of human
polyomavirus DNA using the genome profiling method. Open Virol J.
25. Suwa N, Ikegaya H, Takasaka T, Nishigaki K, Sakurada K. Human blood
identification using the genome profiling method. Leg Med (Tokyo). 2012;
26. Futakami M, Salimullah M, Miura T, Tokita S, Nishigaki K. Novel mutation assay
with high sensitivity based on direct measurement of genomic DNA
alterations: comparable results to the Ames test. J Biochem. 2007;141:675–86.
27. Suwa N, Ishikawa N, Miyamori D, Ikegaya H. The GP method can be
effective as a screening test before STR typing. Int J Hum Genet. 2018;
28. Dumache R, Ciocan V, Muresan C, Enache A. Molecular DNA analysis in
forensic identification. Clin Lab. 2016;62:245–8.
29. Butler JM, Buel E, Crivellente F, McCord BR. Forensic DNA typing by capillary
electrophoresis using the ABI Prism 310 and 3100 genetic analyzers for STR
analysis. Electrophoresis. 2004;25:1397–412.
30. Srivastava S, Avvaru AK, Sowpati DT, Mishra RK. Patterns of microsatellite
distribution across eukaryotic genomes. BMC Genomics. 2019;20:153.
31. Kouduka M, Sato D, Komori M, Kikuchi M, Miyamoto K, Kosaku A, et al. A
solution for universal classification of species based on genomic DNA. Int J
Plant Genomics. 2007;2007:27894.
32. Yamamoto M, Ishii A, Nogi Y, Inoue A, Ito M. Isolation and characterization
of novel denitrifying alkalithermophiles, AT-1 and AT-2. Extremophiles. 2006;
33. Hamano K, Ueno-Tsuji S, Tanaka R, Suzuki M, Nishimura K, Nishigaki K.
Genome profiling (GP) as an effective tool for monitoring culture
collections: a case study with Trichosporon. J Microbiol Methods. 2012;89:
34. Sakuma Y, Nishigaki K. Computer prediction of general PCR products based
on dynamical solution structures of DNA. J Biochem. 1994;116:736–41.
35. Nishigaki K, Naimuddin M, Hamano K. Genome profiling: a realistic solution
for genotype-based identification of species. J Biochem. 2000;128:107–12.
36. Nishigaki K, Miura T, Tsubota M, Sutoh A, Amano N, Husimi Y. Structural
analysis of nucleic acids by precise denaturing gradient gel electrophoresis:
II. Applications to the analysis of subtle and drastic mobility changes of
oligo- and polynucleotides. J Biochem. 1992;111:151–6.
37. Biyani M, Nishigaki K. Sequence-specific and nonspecific mobilities of singlestranded oligonucleotides observed by changing the borate buffer
concentration. Electrophoresis. 2003;24:628–33.
38. Nishigaki K, Husimi Y, Masuda M, Kaneko K, Tanaka T. Strand dissociation
and cooperative melting of double-stranded DNAs detected by denaturant
gradient gel electrophoresis. J Biochem. 1984;95:627–35.
39. Naimuddin M, Kurazono T, Nishigaki K. Commonly conserved genetic
fragments revealed by genome profiling can serve as tracers of evolution.
Nucleic Acids Res. 2002;30:e42.
40. Biyani M, Nishigaki K. Structural characterization of ultra-stable higherordered aggregates generated by novel guanine-rich DNA sequences.
41. Naimuddin M, Kurazono T, Zhang Y, Watanabe T, Yamaguchi M, Nishigaki K.
Species-identification dots: a potent tool for developing genome
microbiology. Gene. 2000;261:243–50.
42. Kouduka M, Matuoka A, Nishigaki K. Acquisition of genome information
from single-celled unculturable organisms (radiolaria) by exploiting genome
profiling (GP). BMC Genomics. 2006;7:135.
43. Ward JH Jr. Hierarchical grouping to optimize an objective function. J Am
Stat Assoc. 1963;58:23–8.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Page 7 of 7
BioMed Central publishes under the Creative Commons Attribution License (CCAL). Under
the CCAL, authors retain copyright to the article but users are allowed to download, reprint,
distribute and /or copy articles in BioMed Central journals, as long as the original work is
Purchase answer to see full
Why Choose Us
- 100% non-plagiarized Papers
- 24/7 /365 Service Available
- Affordable Prices
- Any Paper, Urgency, and Subject
- Will complete your papers in 6 hours
- On-time Delivery
- Money-back and Privacy guarantees
- Unlimited Amendments upon request
- Satisfaction guarantee
How it Works
- Click on the “Place Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
- Fill in your paper’s requirements in the "PAPER DETAILS" section.
- Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
- Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
- From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.