10.1186/1471-2105-11-S7-S1BMC Bioinformatics11SUPPL. 7-BBMI

Christian Schönbach

DMM Tay

G Kumar

G Ostlund

H Gong

K Okamura

Kenta Nakai

M Pennisi

M Wu

S Nagarajan

S Ranganathan

S Teraguchi

Shoba Ranganathan

SI Fukuyama

TH Wu

Tin Wee Tan

TS Ghosh

TW Chen

USF Tambunan

WH Wu

Y Saito

BMC Bioinformatics

PubMed

Crossref

InCoB2010 - 9th International Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010

Schönbach, C.

Nakai, K.

Tan, T.W.

Ranganathan, S.

ScholarBank@NUS

INTRODUCTION Open AccessInCoB2010 - 9th International Conferenceon Bioinformatics at Tokyo, Japan,September 26-28, 2010Christian Schönbach1*, Kenta Nakai2, Tin Wee Tan3, Shoba Ranganathan4,3From Asia Pacific Bioinformatics Network (APBioNet) Ninth International Conference on Bioinformatics(InCoB2010)Tokyo, Japan. 26-28 September 2010AbstractThe International Conference on Bioinformatics (InCoB), the annual conference of the Asia-Pacific BioinformaticsNetwork (APBioNet), is hosted in one of countries of the Asia-Pacific region. The 2010 conference was awarded toJapan and has attracted more than one hundred high-quality research paper submissions. Thorough peer review-ing resulted in 47 (43.5%) accepted papers out of 108 submissions. Submissions from Japan, R.O. Korea, P.R. China,Australia, Singapore and U.S.A totaled 43.8% and contributed to 57.4% of accepted papers. Manuscripts originatingfrom Taiwan and India added up to 42.8% of submissions and 28.3% of acceptances. The fifteen articles publishedin this BMC Bioinformatics supplement cover disease informatics, structural bioinformatics and drug design, biologi-cal databases and software tools, signaling pathways, gene regulatory and biochemical networks, evolution andsequence analysis.IntroductionInCoB (International Conference on Bioinformatics) isthe official conference of the Asia-Pacific BioinformaticsNetwork (APBioNet) http://www.apbionet.org/. Sincethe inaugural conference in Bangkok in 2002, InCoBdeveloped into one of the largest bioinformatics confer-ences in the Asia-Pacific region [1]. We attribute thegrowth in part to publishing submissions as researcharticles in the conference supplement of a PubMed-indexed open-access journal with a reasonable impactfactor. Since 2006 InCoB has published 82 articles inBMC Bioinformatics that were cited 436 times (as ofMay 2010). Three years later (InCoB2009 Singapore),the increasing number of submissions necessitated theaddition of a second InCoB supplement in BMC Geno-mics[1].InCoB annual conferences showcase the latestresearch and technologies in all areas of bioinformatics.This year’s collaboration with Chem-Bio InformaticsSociety (CBI) http://www.cbi.or.jp of Japan and Interna-tional Immunomics Society (IIMMS) http://www.iimms.org/ and support of the Japanese Society for Bioinfor-matics http://www.jsbi.org attracted a diverse spectrumof submission including papers on synthetic biology,biocomputing, systems biology, computational immunol-ogy/vaccinology and disease informatics. Some of thetopics will be covered in depth on the third conferenceday when IIMMS and CBI hold their 3rd Conference ofBasic and Clinical Immunogenomics and Immunomicsand CBI Workshop on Synthetic Biology, MolecularRobotics and Translational Bioinformatics.Submissions and reviewOf the 108 submissions received, we accepted 15 articlesfor BMC Bioinformatics, 25 for BMC Genomics[2] andsix for Immunome Research[3], an independent BMCjournal that publishes bioinformatics-driven immunol-ogy research, and one for IPSJ Transactions on Bioinfor-matics. The submitted articles originated from 19countries with Taiwan and India contributing almost* Correspondence: schoen@bio.kyutech.ac.jp1Department of Bioscience and Bioinformatics, Kyushu Institute ofTechnology, Fukuoka 820-8502, JapanFull list of author information is available at the end of the articleSchönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1http://www.biomedcentral.com/1471-2105/11/S7/S1© 2010 Schönbach et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.43% of submissions and 28% of accepted articles (Fig. 1).Taiwan’s proximity to Japan and the success ofIncoB2008 [4] in Taipei may explain this very high sub-mission rate. The lower acceptance rate of manuscriptsfrom India compared to that of Taiwan is the result ofseveral submissions that were outside the scope of thethree concerned journals. Each submission was peer-reviewed by three PC members and/or sub-reviewers,excepting two, both of which had excellent scores fromthe reviewers, verified by the editors. In an attempt toraise the quality and impact of manuscripts, we intro-duced a second round of reviews for revised submissionsthat were originally judged as borderline papers. Twosubmissions underwent three rounds of revisions andreviewing before being accepted. We wish to thank the83 Program Committee members and 56 sub-reviewers(Additional File 1) for lending their precious time, pro-viding objective comments and responding promptly toour multiple review requests.The challenges and returns of applying bioinformaticsresearch to the areas of disease informatics, structuralbioinformatics and drug design, biological databases andtools, systems biology focussing on biological networksand signaling pathways, and sequence analysis are high-lighted in this issue.Disease informaticsHomozygosity mapping is a standard method to identifyrecessive disease-causing genes if the data size is suffi-ciently large. Huqun et al. [5] developed a homozygositymapping tool (qHomozygosityMapping) that can identifyrecessive disease-causing genes using high-density singlenucleotide polymorphism arrays from very small samplesizes. The success of this new approach was confirmedthe power of this algorithm using data from six patientswith a rare autosomal recessive disease in Japan, theSiiyama-type a1-antitrypsin deficiency. Pennisi andco-workers [6] introduce hybrid agent-based modelsthat simulate the vaccine-elicited immune systemresponse against murine lung metastases. The modelsaim to maximize the reduction of metastases whileminimizing the number of vaccinations.Structural bioinformatics and inhibitory drugdesignIn an analysis of intrinsically disordered domains (IDDs)in innate-immune-related proteins in mouse and generichuman proteins Teraguchi and co-workers [7] clusteredIDDs into sub-groups and associated the clusters withvarious ordered domains. Ligand- and structure-basedvirtual screening approaches are typical methods inidentifying inhibitor drug candidates. Nagarajan et al.[8] applied 3D-QSAR, recursive partitioning, and dock-ing filters to identify hit compounds that inhibit NIKKb,an important target in anti-inflammatory drug research.Usman and Wulandari [9] report the docking results oftwo histone deacetylase family inhibitors. Pharmacologi-cal properties gleaned from the result might be usefulfor designing new inhibitors targeting cervical cancer.Biological databases and toolsChen et al. [11] have developed DoDo and efficientmethod for detecting orthologous genes by usingdomain information. DoDo outperforms InParanoidFigure 1 Submitted vs. accepted articles by country.Schönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1http://www.biomedcentral.com/1471-2105/11/S7/S1Page 2 of 4[11]. Effectors of Type III Secretion System database(T3SSdb) contains annotations that permit the effectivedesign of prediction models to discover new effectorcandidates [12].Systems biology: biological networks andsignaling pathwaysProtein-protein interactions (PPI) are essential featuresof many cellular processes. In a large-scale topologicalanalysis of Escherichia coli non-coding small RNA tar-gets in transcription regulatory and PPI networks, Wuet al. [13] identified general modular relationshipsamong the regulatory networks. The findings of experi-mental and predicted OxyS targets in response to oxida-tive stress are discussed as proof of concept. Wu andcolleagues [14] present a new two-step method thatintegrates various biological and computational sourcesusing Bayesian statistics or support vector machinemethods to construct reliable yeast PPI networks. Someinteresting differences in the subcellular locations ofproteins between human PPI and metabolic networkswere reported by Kumar and Ranganathan [15]. Theauthors analyzed metabolic pathways, from the view-point of subcellular localization of proteins in the net-works. In a detailed study of high-mobility group box-1protein (HMGB1) signaling pathway Gong et al. [16]introduced a computational model that simulates thecross-talk between HMGB1 and p53, NFB, TNF andother signaling pathways that are implicated in cancer.The last paper in the network category takes us throughthe sensitivity analysis of large biological reaction usingtime-delayed differential equations (DDEs) [17]. Theauthors demonstrated the efficacy of their DDEs innumerical interacellular kinetic simulations of the cardi-ovascular control system and the TNFa signal transduc-tion network.Sequence analysisThree papers show the power of computational biologyin addressing questions of general biological interest.Okamura et al. [18] present a new method of analyzingmethylation patterns. Their analysis of 26 deuterosomegenomes revealed a directional transition of methylationpatterns from cephalochordates to the urochordate-vertebrate split. The results imply that the mammalianglobal DNA methylation pattern arose gradually fromfractional patterns. Saito et al. [19] extended a pre-viously published base-pairing profile local alignment(BPLA) kernel method for single sequences to predictnon-coding RNAs from structure-based alignedsequences of large datasets. BPLA method showedsuperior prediction accuracy and robustness againsterrors. Metagenomic sequence analysis requires thetaxonomic binning of sequenced reads. Ghosh et al. [20]have improved their earlier algorithm by addressing thebinning quality and speed associated.ConclusionConference attendance and networking can provide arich source of inspirations and excellent opportunities toexchange information on a broad spectrum of cutting-edge biological and computational topics. To advancebioinformatics research, it is important to continuouslyengage in information exchange. Towards this end, weprovided to one author of each article a complimentaryone-year membership in the International Society ofComputational Biology (ISCB), and hope to see themagain at 10th InCoB in Kuala Lumpur in 2011 or at oneof the ISCB conferences.Additional file 1: List of Program Committee members andsubreviewers.AcknowledgementsWe thank the Local Organizing Committee of InCoB2010 for their persistentefforts. Special thanks go to Ms. Yuki Saito for running the InCoB2010Secretariat. We are grateful to BMC Bioinformatics for their support andencouragement. We also thank Japanese Society for Bioinformatics, Chem-Bio Informatics Society, International Immunomics Society, InternationalSociety for Computational Biology, IPSJ Transactions on Bioinformatics, andBioinformation for support. CS and KN acknowledge the support of KyushuInstitute of Technology and Human Genome Center, respectively.This article has been published as part of BMC Bioinformatics Volume 11Supplement 7, 2010: Ninth International Conference on Bioinformatics(InCoB2010): Bioinformatics. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2105/11?issue=S7.Author details1Department of Bioscience and Bioinformatics, Kyushu Institute ofTechnology, Fukuoka 820-8502, Japan. 2Laboratory of Functional Analysis insilico, Human Genome Center, The Institute of Medical Science, TheUniversity of Tokyo, Tokyo 108-8639, Japan. 3Department of Biochemistry,Yong Loo Lin School of Medicine, National University of Singapore, 8Medical Drive, Singapore 117597. 4Department of Chemistry andBiomolecular Sciences and ARC Centre of Excellence in Bioinformatics,Macquarie University, Sydney NSW 2109, Australia.Authors’ contributionsCS and KN are the Conference Co-chairs and contributed equally the overallorganization of InCoB 2010. TWT (APBioNet Secretariat) and SR (President,APBioNet and Publication Committee Chair) provided advice in organizingInCoB2010 and supported post-acceptance manuscript processing.Competing interestsThe authors declare that they have no competing interests.Published: 15 October 2010References1. Ranganathan S, Eisenhaber F, Tong JC, Tan TW: Extending Asia Pacificbioinformatics into new realms in the “-omics” era. BMC Genomics 2009,10(Suppl 3):S1.2. Ranganathan S, Schönbach C, Nakai K, Tan TW: Asia Pacific bioinformatics:a 2010 update. BMC Genomics 2010, 11(Suppl 4):S1.3. Immunome Research 6(Suppl 1)[http://www.biomedcentral.com/1745-7580/6?issue=S1].Schönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1http://www.biomedcentral.com/1471-2105/11/S7/S1Page 3 of 44. Ranganathan S, Hsu WL, Yang UC, Tan TW: Emerging strengths in AsiaPacific bioinformatics. BMC Bioinformatics 2008, 9(Suppl 12):S1.5. Fukuyama SI, Morino H, Miyazawa H, Tanaka T, Suzuki T, Kohda M,Kawakami H, Okazaki Y, Seyama K, Hagiwara K: A quantitatively-modeledhomozygosity mapping algorithm, qHomozygosityMapping, utilizingwhole genome single nucleotide polymorphism genotyping data. BMCBioinformatics 2010, 11(Suppl 7):S5.6. Pennisi M, Pappalardo F, Palladini A, Nicoletti G, Nanni P, Lollini PL, Motta S:Modeling the competition between lung metastases and the immunesystem using agents. BMC Bioinformatics 2010, 11(Suppl 7):S13.7. Teraguchi S, Patil A, Standley DM: Intrinsically disordered domains deviatesignificantly from random sequences in mammalian proteins. BMCBioinformatics 2010, 11(Suppl 7):S7.8. Nagarajan S, Choo H, Cho YS, Oh KS, Lee BH, Pae AN: IKKb inhibitoridentification: a multi-filter driven novel scaffold. BMC Bioinformatics 2010,11(Suppl 7):S15.9. Tambunan USF, Wulandari EK: Identification of a better Homo sapiensClass II HDACs inhibitor through binding energy calculations anddescriptor analysis. BMC Bioinformatics 2010, 11(Suppl 7):S16.10. Chen TW, Wu TH, Ng WV, Lin WC: DODO: an efficient orthologous genesassignment tool based on domain architectures. BMC Bioinformatics 2010,11(Suppl 7):S6.11. Ostlund G, Schmitt T, Forslund K, Köstler T, Messina DN, Roopra S, Frings O,Sonnhammer EL: InParanoid 7: new algorithms and tools for eukaryoticorthology analysis. Nucleic Acids Res 2010, 38:D196-D203.12. Tay DMM, Govindarajan KR, Khan AM, Ong TYR, Samad HM, Soh WW,Tong M, Zhang F, Tan TW: T3SEdb: Data warehousing of virulenceeffectors secreted by the bacterial Type III Secretion System. BMCBioinformatics 2010, 11(Suppl 7):S4.13. Wu TH, Chang IY, Chu LCJ, Huang HC, Ng WV: Modularity of Escherichiacoli sRNA regulation revealed by sRNA-target and protein networkanalysis. BMC Bioinformatics 2010, 11(Suppl 7):S11.14. Wu M, Li X, Chua HN, Kwoh CK, Ng SK: Integrating diverse biological andcomputational sources for reliable protein-protein interactions. BMCBioinformatics 2010, 11(Suppl 7):S8.15. Kumar G, Ranganathan S: Network analysis of human protein location.BMC Bioinformatics 2010, 11(Suppl 7):S9.16. Gong H, Zuliani P, Komuravelli A, Faeder JR, Clarke EM: Analysis andverification of the HMGB1 signaling pathway. BMC Bioinformatics 2010,11(Suppl 7):S10.17. Wu WH, Wang FS, Chang MS: Sensitivity analysis of dynamic biologicalsystems with time-delays. BMC Bioinformatics 2010, 11(Suppl 7):S12.18. Okamura K, Matsumoto KA, Nakai K: Gradual transition from mosaic toglobal DNA methylation patterns during deuterostome evolution. BMCBioinformatics 2010, 11(Suppl 7):S2.19. Saito Y, Sato K, Sakakibara Y: Robust and accurate prediction ofnoncoding RNAs from aligned sequences. BMC Bioinformatics 2010,11(Suppl 7):S3.20. Ghosh TS, Monzoorul Haque M, Mande SS: DiScRIBinATE: A rapid methodfor accurate taxonomic classification of metagenomic sequences. BMCBioinformatics 2010, 11(Suppl 7):S14.doi:10.1186/1471-2105-11-S7-S1Cite this article as: Schönbach et al.: InCoB2010 - 9th InternationalConference on Bioinformatics at Tokyo, Japan, September 26-28,2010. BMC Bioinformatics 2010 11(Suppl 7):S1.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitSchönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1http://www.biomedcentral.com/1471-2105/11/S7/S1Page 4 of 4

InCoB2010 - 9thInternational Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010

Schonbach, Christian

Nakai, Kenta

Tan, Tin Wee

Ranganathan, Shoba

Macquarie University ResearchOnline

Springer - Publisher Connector

INTRODUCTION Open Access
InCoB2010 - 9th International Conference
on Bioinformatics at Tokyo, Japan,
September 26-28, 2010
Christian Schönbach1*, Kenta Nakai2, Tin Wee Tan3, Shoba Ranganathan4,3
From Asia Pacific Bioinformatics Network (APBioNet) Ninth International Conference on Bioinformatics
(InCoB2010)
Tokyo, Japan. 26-28 September 2010
Abstract
The International Conference on Bioinformatics (InCoB), the annual conference of the Asia-Pacific Bioinformatics
Network (APBioNet), is hosted in one of countries of the Asia-Pacific region. The 2010 conference was awarded to
Japan and has attracted more than one hundred high-quality research paper submissions. Thorough peer review-
ing resulted in 47 (43.5%) accepted papers out of 108 submissions. Submissions from Japan, R.O. Korea, P.R. China,
Australia, Singapore and U.S.A totaled 43.8% and contributed to 57.4% of accepted papers. Manuscripts originating
from Taiwan and India added up to 42.8% of submissions and 28.3% of acceptances. The fifteen articles published
in this BMC Bioinformatics supplement cover disease informatics, structural bioinformatics and drug design, biologi-
cal databases and software tools, signaling pathways, gene regulatory and biochemical networks, evolution and
sequence analysis.
Introduction
InCoB (International Conference on Bioinformatics) is
the official conference of the Asia-Pacific Bioinformatics
Network (APBioNet) http://www.apbionet.org/. Since
the inaugural conference in Bangkok in 2002, InCoB
developed into one of the largest bioinformatics confer-
ences in the Asia-Pacific region [1]. We attribute the
growth in part to publishing submissions as research
articles in the conference supplement of a PubMed-
indexed open-access journal with a reasonable impact
factor. Since 2006 InCoB has published 82 articles in
BMC Bioinformatics that were cited 436 times (as of
May 2010). Three years later (InCoB2009 Singapore),
the increasing number of submissions necessitated the
addition of a second InCoB supplement in BMC Geno-
mics[1].
InCoB annual conferences showcase the latest
research and technologies in all areas of bioinformatics.
This year’s collaboration with Chem-Bio Informatics
Society (CBI) http://www.cbi.or.jp of Japan and Interna-
tional Immunomics Society (IIMMS) http://www.iimms.
org/ and support of the Japanese Society for Bioinfor-
matics http://www.jsbi.org attracted a diverse spectrum
of submission including papers on synthetic biology,
biocomputing, systems biology, computational immunol-
ogy/vaccinology and disease informatics. Some of the
topics will be covered in depth on the third conference
day when IIMMS and CBI hold their 3rd Conference of
Basic and Clinical Immunogenomics and Immunomics
and CBI Workshop on Synthetic Biology, Molecular
Robotics and Translational Bioinformatics.
Submissions and review
Of the 108 submissions received, we accepted 15 articles
for BMC Bioinformatics, 25 for BMC Genomics[2] and
six for Immunome Research[3], an independent BMC
journal that publishes bioinformatics-driven immunol-
ogy research, and one for IPSJ Transactions on Bioinfor-
matics. The submitted articles originated from 19
countries with Taiwan and India contributing almost
* Correspondence: schoen@bio.kyutech.ac.jp
1Department of Bioscience and Bioinformatics, Kyushu Institute of
Technology, Fukuoka 820-8502, Japan
Full list of author information is available at the end of the article
Schönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1
http://www.biomedcentral.com/1471-2105/11/S7/S1
© 2010 Schönbach et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
43% of submissions and 28% of accepted articles (Fig. 1).
Taiwan’s proximity to Japan and the success of
IncoB2008 [4] in Taipei may explain this very high sub-
mission rate. The lower acceptance rate of manuscripts
from India compared to that of Taiwan is the result of
several submissions that were outside the scope of the
three concerned journals. Each submission was peer-
reviewed by three PC members and/or sub-reviewers,
excepting two, both of which had excellent scores from
the reviewers, verified by the editors. In an attempt to
raise the quality and impact of manuscripts, we intro-
duced a second round of reviews for revised submissions
that were originally judged as borderline papers. Two
submissions underwent three rounds of revisions and
reviewing before being accepted. We wish to thank the
83 Program Committee members and 56 sub-reviewers
(Additional File 1) for lending their precious time, pro-
viding objective comments and responding promptly to
our multiple review requests.
The challenges and returns of applying bioinformatics
research to the areas of disease informatics, structural
bioinformatics and drug design, biological databases and
tools, systems biology focussing on biological networks
and signaling pathways, and sequence analysis are high-
lighted in this issue.
Disease informatics
Homozygosity mapping is a standard method to identify
recessive disease-causing genes if the data size is suffi-
ciently large. Huqun et al. [5] developed a homozygosity
mapping tool (qHomozygosityMapping) that can identify
recessive disease-causing genes using high-density single
nucleotide polymorphism arrays from very small sample
sizes. The success of this new approach was confirmed
the power of this algorithm using data from six patients
with a rare autosomal recessive disease in Japan, the
Siiyama-type a1-antitrypsin deficiency. Pennisi and
co-workers [6] introduce hybrid agent-based models
that simulate the vaccine-elicited immune system
response against murine lung metastases. The models
aim to maximize the reduction of metastases while
minimizing the number of vaccinations.
Structural bioinformatics and inhibitory drug
design
In an analysis of intrinsically disordered domains (IDDs)
in innate-immune-related proteins in mouse and generic
human proteins Teraguchi and co-workers [7] clustered
IDDs into sub-groups and associated the clusters with
various ordered domains. Ligand- and structure-based
virtual screening approaches are typical methods in
identifying inhibitor drug candidates. Nagarajan et al.
[8] applied 3D-QSAR, recursive partitioning, and dock-
ing filters to identify hit compounds that inhibit NIKKb,
an important target in anti-inflammatory drug research.
Usman and Wulandari [9] report the docking results of
two histone deacetylase family inhibitors. Pharmacologi-
cal properties gleaned from the result might be useful
for designing new inhibitors targeting cervical cancer.
Biological databases and tools
Chen et al. [11] have developed DoDo and efficient
method for detecting orthologous genes by using
domain information. DoDo outperforms InParanoid
Figure 1 Submitted vs. accepted articles by country.
Schönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1
http://www.biomedcentral.com/1471-2105/11/S7/S1
Page 2 of 4
[11]. Effectors of Type III Secretion System database
(T3SSdb) contains annotations that permit the effective
design of prediction models to discover new effector
candidates [12].
Systems biology: biological networks and
signaling pathways
Protein-protein interactions (PPI) are essential features
of many cellular processes. In a large-scale topological
analysis of Escherichia coli non-coding small RNA tar-
gets in transcription regulatory and PPI networks, Wu
et al. [13] identified general modular relationships
among the regulatory networks. The findings of experi-
mental and predicted OxyS targets in response to oxida-
tive stress are discussed as proof of concept. Wu and
colleagues [14] present a new two-step method that
integrates various biological and computational sources
using Bayesian statistics or support vector machine
methods to construct reliable yeast PPI networks. Some
interesting differences in the subcellular locations of
proteins between human PPI and metabolic networks
were reported by Kumar and Ranganathan [15]. The
authors analyzed metabolic pathways, from the view-
point of subcellular localization of proteins in the net-
works. In a detailed study of high-mobility group box-1
protein (HMGB1) signaling pathway Gong et al. [16]
introduced a computational model that simulates the
cross-talk between HMGB1 and p53, NFB, TNF and
other signaling pathways that are implicated in cancer.
The last paper in the network category takes us through
the sensitivity analysis of large biological reaction using
time-delayed differential equations (DDEs) [17]. The
authors demonstrated the efficacy of their DDEs in
numerical interacellular kinetic simulations of the cardi-
ovascular control system and the TNFa signal transduc-
tion network.
Sequence analysis
Three papers show the power of computational biology
in addressing questions of general biological interest.
Okamura et al. [18] present a new method of analyzing
methylation patterns. Their analysis of 26 deuterosome
genomes revealed a directional transition of methylation
patterns from cephalochordates to the urochordate-
vertebrate split. The results imply that the mammalian
global DNA methylation pattern arose gradually from
fractional patterns. Saito et al. [19] extended a pre-
viously published base-pairing profile local alignment
(BPLA) kernel method for single sequences to predict
non-coding RNAs from structure-based aligned
sequences of large datasets. BPLA method showed
superior prediction accuracy and robustness against
errors. Metagenomic sequence analysis requires the
taxonomic binning of sequenced reads. Ghosh et al. [20]
have improved their earlier algorithm by addressing the
binning quality and speed associated.
Conclusion
Conference attendance and networking can provide a
rich source of inspirations and excellent opportunities to
exchange information on a broad spectrum of cutting-
edge biological and computational topics. To advance
bioinformatics research, it is important to continuously
engage in information exchange. Towards this end, we
provided to one author of each article a complimentary
one-year membership in the International Society of
Computational Biology (ISCB), and hope to see them
again at 10th InCoB in Kuala Lumpur in 2011 or at one
of the ISCB conferences.
Additional file 1: List of Program Committee members and
subreviewers.
Acknowledgements
We thank the Local Organizing Committee of InCoB2010 for their persistent
efforts. Special thanks go to Ms. Yuki Saito for running the InCoB2010
Secretariat. We are grateful to BMC Bioinformatics for their support and
encouragement. We also thank Japanese Society for Bioinformatics, Chem-
Bio Informatics Society, International Immunomics Society, International
Society for Computational Biology, IPSJ Transactions on Bioinformatics, and
Bioinformation for support. CS and KN acknowledge the support of Kyushu
Institute of Technology and Human Genome Center, respectively.
This article has been published as part of BMC Bioinformatics Volume 11
Supplement 7, 2010: Ninth International Conference on Bioinformatics
(InCoB2010): Bioinformatics. The full contents of the supplement are
available online at http://www.biomedcentral.com/1471-2105/11?issue=S7.
Author details
1Department of Bioscience and Bioinformatics, Kyushu Institute of
Technology, Fukuoka 820-8502, Japan. 2Laboratory of Functional Analysis in
silico, Human Genome Center, The Institute of Medical Science, The
University of Tokyo, Tokyo 108-8639, Japan. 3Department of Biochemistry,
Yong Loo Lin School of Medicine, National University of Singapore, 8
Medical Drive, Singapore 117597. 4Department of Chemistry and
Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics,
Macquarie University, Sydney NSW 2109, Australia.
Authors’ contributions
CS and KN are the Conference Co-chairs and contributed equally the overall
organization of InCoB 2010. TWT (APBioNet Secretariat) and SR (President,
APBioNet and Publication Committee Chair) provided advice in organizing
InCoB2010 and supported post-acceptance manuscript processing.
Competing interests
The authors declare that they have no competing interests.
Published: 15 October 2010
References
1. Ranganathan S, Eisenhaber F, Tong JC, Tan TW: Extending Asia Pacific
bioinformatics into new realms in the “-omics” era. BMC Genomics 2009,
10(Suppl 3):S1.
2. Ranganathan S, Schönbach C, Nakai K, Tan TW: Asia Pacific bioinformatics:
a 2010 update. BMC Genomics 2010, 11(Suppl 4):S1.
3. Immunome Research 6(Suppl 1)[http://www.biomedcentral.com/1745-7580/
6?issue=S1].
Schönbach et al. BMC Bioinformatics 2010, 11(Suppl 7):S1
http://www.biomedcentral.com/1471-2105/11/S7/S1
Page 3 of 4
4. Ranganathan S, Hsu WL, Yang UC, Tan TW: Emerging strengths in Asia
Pacific bioinformatics. BMC Bioinformatics 2008, 9(Suppl 12):S1.
5. Fukuyama SI, Morino H, Miyazawa H, Tanaka T, Suzuki T, Kohda M,
Kawakami H, Okazaki Y, Seyama K, Hagiwara K: A quantitatively-modeled
homozygosity mapping algorithm, qHomozygosityMapping, utilizing
whole genome single nucleotide polymorphism genotyping data. BMC
Bioinformatics 2010, 11(Suppl 7):S5.
6. Pennisi M, Pappalardo F, Palladini A, Nicoletti G, Nanni P, Lollini PL, Motta S:
Modeling the competition between lung metastases and the immune
system using agents. BMC Bioinformatics 2010, 11(Suppl 7):S13.
7. Teraguchi S, Patil A, Standley DM: Intrinsically disordered domains deviate
significantly from random sequences in mammalian proteins. BMC
Bioinformatics 2010, 11(Suppl 7):S7.
8. Nagarajan S, Choo H, Cho YS, Oh KS, Lee BH, Pae AN: IKKb inhibitor
identification: a multi-filter driven novel scaffold. BMC Bioinformatics 2010,
11(Suppl 7):S15.
9. Tambunan USF, Wulandari EK: Identification of a better Homo sapiens
Class II HDACs inhibitor through binding energy calculations and
descriptor analysis. BMC Bioinformatics 2010, 11(Suppl 7):S16.
10. Chen TW, Wu TH, Ng WV, Lin WC: DODO: an efficient orthologous genes
assignment tool based on domain architectures. BMC Bioinformatics 2010,
11(Suppl 7):S6.
11. Ostlund G, Schmitt T, Forslund K, Köstler T, Messina DN, Roopra S, Frings O,
Sonnhammer EL: InParanoid 7: new algorithms and tools for eukaryotic
orthology analysis. Nucleic Acids Res 2010, 38:D196-D203.
12. Tay DMM, Govindarajan KR, Khan AM, Ong TYR, Samad HM, Soh WW,
Tong M, Zhang F, Tan TW: T3SEdb: Data warehousing of virulence
effectors secreted by the bacterial Type III Secretion System. BMC
Bioinformatics 2010, 11(Suppl 7):S4.
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doi:10.1186/1471-2105-11-S7-S1
Cite this article as: Schönbach et al.: InCoB2010 - 9th International
Conference on Bioinformatics at Tokyo, Japan, September 26-28,
2010. BMC Bioinformatics 2010 11(Suppl 7):S1.
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InCoB2010 - 9 International Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010

11(Suppl 7):S1. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance

Analysis and verification of the HMGB1 signaling pathway. BMC Bioinformatics 2010, 11(Suppl 7):S10.

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Modeling the competition between lung metastases and the immune system using agents. BMC Bioinformatics 2010, 11(Suppl 7):S13.

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Wulandari EK: Identification of a better Homo sapiens Class II HDACs inhibitor through binding energy calculations and descriptor analysis.

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InCoB2010 - 9thInternational Conference on Bioinformatics at Tokyo, Japan, September 26-28, 2010

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