Displaying publications 1 - 20 of 116 in total

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  1. Zeng C, Guo X, Long J, Kuchenbaecker KB, Droit A, Michailidou K, et al.
    Breast Cancer Res, 2016 06 21;18(1):64.
    PMID: 27459855 DOI: 10.1186/s13058-016-0718-0
    BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.

    METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation.

    RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P 

    Matched MeSH terms: Computational Biology/methods
  2. Shahab M, Aiman S, Alshammari A, Alasmari AF, Alharbi M, Khan A, et al.
    Int J Biol Macromol, 2023 Dec 31;253(Pt 2):126678.
    PMID: 37666399 DOI: 10.1016/j.ijbiomac.2023.126678
    Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.
    Matched MeSH terms: Computational Biology/methods
  3. Shahab M, Iqbal MW, Ahmad A, Alshabrmi FM, Wei DQ, Khan A, et al.
    Comput Biol Med, 2024 Mar;170:108056.
    PMID: 38301512 DOI: 10.1016/j.compbiomed.2024.108056
    The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.
    Matched MeSH terms: Computational Biology/methods
  4. Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
    Matched MeSH terms: Computational Biology/methods*
  5. Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Computational Biology/methods*
  6. Zakaria N, Yusoff NM, Zakaria Z, Lim MN, Baharuddin PJ, Fakiruddin KS, et al.
    BMC Cancer, 2015;15:84.
    PMID: 25881239 DOI: 10.1186/s12885-015-1086-3
    Despite significant advances in staging and therapies, lung cancer remains a major cause of cancer-related lethality due to its high incidence and recurrence. Clearly, a novel approach is required to develop new therapies to treat this devastating disease. Recent evidence indicates that tumours contain a small population of cells known as cancer stem cells (CSCs) that are responsible for tumour maintenance, spreading and resistant to chemotherapy. The genetic composition of CSCs so far is not fully understood, but manipulation of the specific genes that maintain their integrity would be beneficial for developing strategies to combat cancer. Therefore, the goal of this study isto identify the transcriptomic composition and biological functions of CSCs from non-small cell lung cancer (NSCLC).
    Matched MeSH terms: Computational Biology/methods
  7. Chen X, Tan X, Li J, Jin Y, Gong L, Hong M, et al.
    PLoS One, 2013;8(12):e82861.
    PMID: 24340064 DOI: 10.1371/journal.pone.0082861
    Coxsackievirus A16 (CVA16) is responsible for nearly 50% of all the confirmed hand, foot, and mouth disease (HFMD) cases in mainland China, sometimes it could also cause severe complications, and even death. To clarify the genetic characteristics and the epidemic patterns of CVA16 in mainland China, comprehensive bioinfomatics analyses were performed by using 35 CVA16 whole genome sequences from 1998 to 2011, 593 complete CVA16 VP1 sequences from 1981 to 2011, and prototype strains of human enterovirus species A (EV-A). Analysis on complete VP1 sequences revealed that subgenotypes B1a and B1b were prevalent strains and have been co-circulating in many Asian countries since 2000, especially in mainland China for at least 13 years. While the prevalence of subgenotype B1c (totally 20 strains) was much limited, only found in Malaysia from 2005 to 2007 and in France in 2010. Genotype B2 only caused epidemic in Japan and Malaysia from 1981 to 2000. Both subgenotypes B1a and B1b were potential recombinant viruses containing sequences from other EV-A donors in the 5'-untranslated region and P2, P3 non-structural protein encoding regions.
    Matched MeSH terms: Computational Biology/methods*
  8. Choo SW, Ang MY, Dutta A, Tan SY, Siow CC, Heydari H, et al.
    Sci Rep, 2015 Dec 15;5:18227.
    PMID: 26666970 DOI: 10.1038/srep18227
    Mycobacterium spp. are renowned for being the causative agent of diseases like leprosy, Buruli ulcer and tuberculosis in human beings. With more and more mycobacterial genomes being sequenced, any knowledge generated from comparative genomic analysis would provide better insights into the biology, evolution, phylogeny and pathogenicity of this genus, thus helping in better management of diseases caused by Mycobacterium spp.With this motivation, we constructed MycoCAP, a new comparative analysis platform dedicated to the important genus Mycobacterium. This platform currently provides information of 2108 genome sequences of at least 55 Mycobacterium spp. A number of intuitive web-based tools have been integrated in MycoCAP particularly for comparative analysis including the PGC tool for comparison between two genomes, PathoProT for comparing the virulence genes among the Mycobacterium strains and the SuperClassification tool for the phylogenic classification of the Mycobacterium strains and a specialized classification system for strains of Mycobacterium abscessus. We hope the broad range of functions and easy-to-use tools provided in MycoCAP makes it an invaluable analysis platform to speed up the research discovery on mycobacteria for researchers. Database URL: http://mycobacterium.um.edu.my.
    Matched MeSH terms: Computational Biology/methods*
  9. Riveron JM, Ibrahim SS, Mulamba C, Djouaka R, Irving H, Wondji MJ, et al.
    G3 (Bethesda), 2017 06 07;7(6):1819-1832.
    PMID: 28428243 DOI: 10.1534/g3.117.040147
    Pyrethroid resistance in malaria vector, An. funestus is increasingly reported across Africa, threatening the sustainability of pyrethroid-based control interventions, including long lasting insecticidal nets (LLINs). Managing this problem requires understanding of the molecular basis of the resistance from different regions of the continent, to establish whether it is being driven by a single or independent selective events. Here, using a genome-wide transcription profiling of pyrethroid resistant populations from southern (Malawi), East (Uganda), and West Africa (Benin), we investigated the molecular basis of resistance, revealing strong differences between the different African regions. The duplicated cytochrome P450 genes (CYP6P9a and CYP6P9b) which were highly overexpressed in southern Africa are not the most upregulated in other regions, where other genes are more overexpressed, including GSTe2 in West (Benin) and CYP9K1 in East (Uganda). The lack of directional selection on both CYP6P9a and CYP6P9b in Uganda in contrast to southern Africa further supports the limited role of these genes outside southern Africa. However, other genes such as the P450 CYP9J11 are commonly overexpressed in all countries across Africa. Here, CYP9J11 is functionally characterized and shown to confer resistance to pyrethroids and moderate cross-resistance to carbamates (bendiocarb). The consistent overexpression of GSTe2 in Benin is coupled with a role of allelic variation at this gene as GAL4-UAS transgenic expression in Drosophila flies showed that the resistant 119F allele is highly efficient in conferring both DDT and permethrin resistance than the L119. The heterogeneity in the molecular basis of resistance and cross-resistance to insecticides in An. funestus populations throughout sub-Saharan African should be taken into account in designing resistance management strategies.
    Matched MeSH terms: Computational Biology/methods
  10. Axtner J, Crampton-Platt A, Hörig LA, Mohamed A, Xu CCY, Yu DW, et al.
    Gigascience, 2019 Apr 01;8(4).
    PMID: 30997489 DOI: 10.1093/gigascience/giz029
    BACKGROUND: The use of environmental DNA for species detection via metabarcoding is growing rapidly. We present a co-designed lab workflow and bioinformatic pipeline to mitigate the 2 most important risks of environmental DNA use: sample contamination and taxonomic misassignment. These risks arise from the need for polymerase chain reaction (PCR) amplification to detect the trace amounts of DNA combined with the necessity of using short target regions due to DNA degradation.

    FINDINGS: Our high-throughput workflow minimizes these risks via a 4-step strategy: (i) technical replication with 2 PCR replicates and 2 extraction replicates; (ii) using multi-markers (12S,16S,CytB); (iii) a "twin-tagging," 2-step PCR protocol; and (iv) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases. Because annotation errors in the reference sequences can result in taxonomic misassignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in >50% of sequences being deleted from public reference databases, owing to (i) limited overlap between our target amplicon and reference sequences, (ii) mislabelling of reference sequences, and (iii) redundancy. Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an invertebrate-derived DNA dataset from 1,532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment (16S) amplified most frequently for all samples but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criterion we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples.

    CONCLUSIONS: Our metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider use of environmental and invertebrate-derived DNA, which is turning into a valuable source of ecological and conservation information on tetrapods.

    Matched MeSH terms: Computational Biology/methods
  11. Cheah BH, Nadarajah K, Divate MD, Wickneswari R
    BMC Genomics, 2015;16:692.
    PMID: 26369665 DOI: 10.1186/s12864-015-1851-3
    Developing drought-tolerant rice varieties with higher yield under water stressed conditions provides a viable solution to serious yield-reduction impact of drought. Understanding the molecular regulation of this polygenic trait is crucial for the eventual success of rice molecular breeding programmes. microRNAs have received tremendous attention recently due to its importance in negative regulation. In plants, apart from regulating developmental and physiological processes, microRNAs have also been associated with different biotic and abiotic stresses. Hence here we chose to analyze the differential expression profiles of microRNAs in three drought treated rice varieties: Vandana (drought-tolerant), Aday Sel (drought-tolerant) and IR64 (drought-susceptible) in greenhouse conditions via high-throughput sequencing.
    Matched MeSH terms: Computational Biology/methods
  12. Ling KH, Loo SS, Rosli R, Shamsudin MN, Mohamed R, Wan KL
    In Silico Biol. (Gedrukt), 2007;7(1):115-21.
    PMID: 17688436
    Phosphatidylinositol 4-phosphate 5-kinases (PIP5Ks) play diverse roles in the cellular biology of many organisms, including signal transduction, secretion and vesicular trafficking, and regulation of cytoskeleton assembly. Discovery of the PIP5K gene in Eimeria tenella may shed light on its role in the biology of this avian protozoan, and afford further understanding of the cell-host interaction, particularly during the invasion process. In this study, we report the identification of the PIP5K coding region in the genome sequence of Eimeria tenella using in silico gene prediction approaches. Prediction of the PIP5K coding sequence was confirmed by mapping the full-length cDNA sequence, generated via the Rapid Amplification of cDNA Ends (RACE) method, to the genomic sequence. The putative PIP5K gene of Eimeria tenella is located on the complementary strand of the E1080B12.b1 contig, and comprises 12 exons. Further analysis showed that the coding region spans from exon 1 to exon 7, with all exons obeying the adopted 'gt...ag' splicing rule of intronic sequences. Consensus of the hexameric 5' donor-splice site was deduced as GTRDBB... and the consensus for the 3' acceptor-splice sites as ...BHDYAG. The gene encodes a 252-amino acid residue protein. Domain search and protein fold recognition analyses provide compelling evidences that the deduced protein is a PIP5K.
    Matched MeSH terms: Computational Biology/methods*
  13. Formenti G, Rhie A, Balacco J, Haase B, Mountcastle J, Fedrigo O, et al.
    Genome Biol, 2021 04 29;22(1):120.
    PMID: 33910595 DOI: 10.1186/s13059-021-02336-9
    BACKGROUND: Modern sequencing technologies should make the assembly of the relatively small mitochondrial genomes an easy undertaking. However, few tools exist that address mitochondrial assembly directly.

    RESULTS: As part of the Vertebrate Genomes Project (VGP) we develop mitoVGP, a fully automated pipeline for similarity-based identification of mitochondrial reads and de novo assembly of mitochondrial genomes that incorporates both long (> 10 kbp, PacBio or Nanopore) and short (100-300 bp, Illumina) reads. Our pipeline leads to successful complete mitogenome assemblies of 100 vertebrate species of the VGP. We observe that tissue type and library size selection have considerable impact on mitogenome sequencing and assembly. Comparing our assemblies to purportedly complete reference mitogenomes based on short-read sequencing, we identify errors, missing sequences, and incomplete genes in those references, particularly in repetitive regions. Our assemblies also identify novel gene region duplications. The presence of repeats and duplications in over half of the species herein assembled indicates that their occurrence is a principle of mitochondrial structure rather than an exception, shedding new light on mitochondrial genome evolution and organization.

    CONCLUSIONS: Our results indicate that even in the "simple" case of vertebrate mitogenomes the completeness of many currently available reference sequences can be further improved, and caution should be exercised before claiming the complete assembly of a mitogenome, particularly from short reads alone.

    Matched MeSH terms: Computational Biology/methods
  14. Kumar S, Fazil MHUT, Ahmad K, Tripathy M, Rajapakse JC, Verma NK
    Methods Mol Biol, 2019;1930:149-156.
    PMID: 30610609 DOI: 10.1007/978-1-4939-9036-8_18
    Analysis of protein-protein interactions is important for better understanding of molecular mechanisms involved in immune regulation and has potential for elaborating avenues for drug discovery targeting T-cell motility. Currently, only a small fraction of protein-protein interactions have been characterized in T-lymphocytes although there are several detection methods available. In this regard, computational approaches garner importance, with the continued explosion of genomic and proteomic data, for handling protein modeling and protein-protein interactions in large scale. Here, we describe a computational method to identify protein-protein interactions based on in silico protein design.
    Matched MeSH terms: Computational Biology/methods*
  15. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, et al.
    F1000Res, 2018;7.
    PMID: 30026930 DOI: 10.12688/f1000research.14509.2
    Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a "One Health" approach.
    Matched MeSH terms: Computational Biology/methods*
  16. Packiam KAR, Ramanan RN, Ooi CW, Krishnaswamy L, Tey BT
    Appl Microbiol Biotechnol, 2020 Apr;104(8):3253-3266.
    PMID: 32076772 DOI: 10.1007/s00253-020-10454-w
    Over the past few decades, Escherichia coli (E. coli) remains the most favorable host among the microbial cell factories for the production of soluble recombinant proteins. Recombinant protein production (RPP) via E. coli is optimized at the level of gene expression (expression level) and the process condition of fermentation (process level). Presently, the reported studies do not give a clear view on the selection of methods employed in the optimization of RPP. Here, we have reviewed various optimization methods and their preferences with respect to the factors at expression and process levels to achieve the optimal levels of soluble RPP. With a greater understanding of these optimization methods, we proposed a stepwise methodology linking the factors from both levels for optimizing the production of soluble recombinant protein in E. coli. The proposed methodology is further explained through five sets of examples demonstrating the optimization of RPP at both expression and process levels.Key Points• Stepwise methodology of optimizing recombinant protein production is proposed.• In silico tools can facilitate the optimization of gene- and protein-based factors.• Optimization of gene- and protein-based factors aids host-vector selection.• Statistical optimization is preferred for achieving optimal levels of process factors.
    Matched MeSH terms: Computational Biology/methods*
  17. Teo CY, Shave S, Chor AL, Salleh AB, Rahman MB, Walkinshaw MD, et al.
    BMC Bioinformatics, 2012;13 Suppl 17:S4.
    PMID: 23282142 DOI: 10.1186/1471-2105-13-S17-S4
    BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disease with unknown etiology. Anticitrullinated protein autoantibody has been documented as a highly specific autoantibody associated with RA. Protein arginine deiminase type 4 (PAD4) is the enzyme responsible for catalyzing the conversion of peptidylarginine into peptidylcitrulline. PAD4 is a new therapeutic target for RA treatment. In order to search for inhibitors of PAD4, structure-based virtual screening was performed using LIDAEUS (Ligand discovery at Edinburgh university). Potential inhibitors were screened experimentally by inhibition assays.

    RESULTS: Twenty two of the top-ranked water-soluble compounds were selected for inhibitory screening against PAD4. Three compounds showed significant inhibition of PAD4 and their IC50 values were investigated. The structures of the three compounds show no resemblance with previously discovered PAD4 inhibitors, nor with existing drugs for RA treatment.

    CONCLUSION: Three compounds were discovered as potential inhibitors of PAD4 by virtual screening. The compounds are commercially available and can be used as scaffolds to design more potent inhibitors against PAD4.

    Matched MeSH terms: Computational Biology/methods*
  18. Ranganathan S, Schönbach C, Nakai K, Tan TW
    BMC Genomics, 2010;11 Suppl 4:S1.
    PMID: 21143792 DOI: 10.1186/1471-2164-11-S4-S1
    The 2010 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation formed in 1998, was organized as the 9th International Conference on Bioinformatics (InCoB), Sept. 26-28, 2010 in Tokyo, Japan. Initially, APBioNet created InCoB as forum to foster bioinformatics in the Asia Pacific region. Given the growing importance of interdisciplinary research, InCoB2010 included topics targeting scientists in the fields of genomic medicine, immunology and chemoinformatics, supporting translational research. Peer-reviewed manuscripts that were accepted for publication in this supplement, represent key areas of research interests that have emerged in our region. We also highlight some of the current challenges bioinformatics is facing in the Asia Pacific region and conclude our report with the announcement of APBioNet's 100 BioDatabases (BioDB100) initiative. BioDB100 will comply with the database criteria set out earlier in our proposal for Minimum Information about a Bioinformatics and Investigation (MIABi), setting the standards for biocuration and bioinformatics research, on which we will report at the next InCoB, Nov. 27 - Dec. 2, 2011 at Kuala Lumpur, Malaysia.
    Matched MeSH terms: Computational Biology/methods
  19. Ranganathan S, Hsu WL, Yang UC, Tan TW
    BMC Bioinformatics, 2008;9 Suppl 12:S1.
    PMID: 19091008 DOI: 10.1186/1471-2105-9-S12-S1
    The 2008 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998, was organized as the 7th International Conference on Bioinformatics (InCoB), jointly with the Bioinformatics and Systems Biology in Taiwan (BIT 2008) Conference, Oct. 20-23, 2008 at Taipei, Taiwan. Besides bringing together scientists from the field of bioinformatics in this region, InCoB is actively involving researchers from the area of systems biology, to facilitate greater synergy between these two groups. Marking the 10th Anniversary of APBioNet, this InCoB 2008 meeting followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India) and Hong Kong. Additionally, tutorials and the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) immediately prior to the 20th Federation of Asian and Oceanian Biochemists and Molecular Biologists (FAOBMB) Taipei Conference provided ample opportunity for inducting mainstream biochemists and molecular biologists from the region into a greater level of awareness of the importance of bioinformatics in their craft. In this editorial, we provide a brief overview of the peer-reviewed manuscripts accepted for publication herein, grouped into thematic areas. As the regional research expertise in bioinformatics matures, the papers fall into thematic areas, illustrating the specific contributions made by APBioNet to global bioinformatics efforts.
    Matched MeSH terms: Computational Biology/methods*
  20. Zeti AM, Shamsir MS, Tajul-Arifin K, Merican AF, Mohamed R, Nathan S, et al.
    PLoS Comput Biol, 2009 Aug;5(8):e1000457.
    PMID: 19714208 DOI: 10.1371/journal.pcbi.1000457
    Matched MeSH terms: Computational Biology/methods*
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