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2016 update on APBioNet’s annual international conference on bioinformatics (InCoB)


InCoB became since its inception in 2002 one of the largest annual bioinformatics conferences in the Asia-Pacific region with attendance ranging between 150 and 250 delegates depending on the venue location. InCoB 2016 in Singapore was attended by almost 220 delegates. This year, sessions on structural bioinformatics, sequence and sequencing, and next-generation sequencing fielded the highest number of oral presentation. Forty-four out 96 oral presentations were associated with an accepted manuscript in supplemental issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics or BMC Systems Biology. Articles with a genomics focus are reviewed in this editorial. Next year’s InCoB will be held in Shenzen, China from September 20 to 22, 2017.


The International Conference on Bioinformatics (InCoB) was held for the first time at Biopolis, Singapore in 2009 and returned in September 21 to 23, 2016 [1] to attract approximately 220 delegates. Singaporean bioinformatics pioneers and APBioNet founders Subramanian Subbiah and Tin Wee Tan highlighted in a special InCoB session 20 years of history and achievements of bioinformatics in Singapore and particularly at the Bioinformatics Centre at NUS. Four parallel tracks accommodated 19 sessions including 96 oral presentations and two sessions with 79 poster presentations.

Five keynotes addressed the latest state of research and advances in genomics transcriptomics and proteomics. Barak Cohen talked about the analysis of combinatorial cis-regulation. Mihaela Zavolan’s talk focused on predicting small RNA targets to learn about gene expression regulatory networks. Vanessa Hayes presented how next generation mapping provides new insights into complex genomic variation of significance to human health and cancer. Sir Tom Blundell’s provided insight into structural bioinformatics and genomic variation: understanding and combating genetic disease and drug resistance and Shoba Ranganathan updated the audience on accelerating the search for the human proteome’s “missing proteins”.

Three medical genomic-themed satellite workshops were conducted at the end of the first conference day: (1) An introduction to transcriptomics and cancer genomics: tools, databases and workflows, (2) Garuda Platform: re-imagining connectivity in biology and medicine and (3) Decoding causative mutations: finding SNPs in a sea of sequenced data.

At the Annual General Meeting on September 21st Shoba Ranganathan delivered the President’s Report on the state of APBioNet and its activities in the past year. The AGM was concluded with the announcement of election results for the Executive Committee (ExCo), term 2016–2018. The list of elected ExCo members and office bearers is available at APBioNet and ISCB affiliates websites [2, 3].

Manuscript submission and review

Authors had a choice of five journals for submission of original research or software and database articles: BMC Genomics, BMC Bioinformatics, BMC Systems Biology, BMC Medical Genomics, PeerJ, or Journal of Bioinformatics and Computational Biology (JBCB). Of 101 submitted manuscripts 49 (48.5%) were accepted after peer review by at least two members of the Program Committee or external sub-reviewers (Additional file 1) in revised form. Forty-four accepted manuscripts are published in the InCoB2016 supplements of BMC Medical Genomics (6), BMC Systems Biology (5), BMC Genomics (14), BMC Bioinformatics (19) issues, two in PeerJ and three in JBCB. Four manuscripts with top reviewers’ scores were selected for Best Paper Awards (Additional file 2). An overview of the 24 research papers published in the supplement issues of BMC Bioinformatics and BMC Systems Biology is available as introduction in the InCoB2016 supplement of BMC Bioinformatics [4]. The articles included in the BMC Genomics and BMC Medical Genomics supplements are briefly reviewed here.

NGS informatics

Biomarker discovery is a significant area of biomedical research, where immense transcriptome datasets are analysed and compared. To facilitate this intensive process, Moon and Nakai [5] propose an efficient and accurate ensemble L 1 -norm support vector machine approach for multi-dimensional data, tested on renal cancer patient data. On the other hand, Hu et al. [6] have used support vector machine-based recursive feature elimination approach to zoom in on key single cell brain transcripts for studying neurological diseases.

To rapidly estimate the variation in expression levels of novel transcripts across multiple human and mouse tissues and cell types, Hou et al. [7] have developed LocExpress as a freely available webserver, while Ponyared et al. [8] have developed the EASP Plus server for mining simple sequence repeat (SSR) markers for plant genetic trait characterization. For targeted gene editing, zinc finger proteins can be engineered to bind to specific DNA sequences. Dutta et al. [9] have analysed zinc finger-DNA complex structures and developed a method to predict zinc finger recognition sites for a given DNA nonamer, also implemented as a webserver, Zifpredict_ihbe. Furthermore, Dutta et al. [10] have used an ensemble micro neural network approach to decipher DNA-zinc finger interactions and developed a webserver, ZifNN, for designing zinc finger proteins that will preferentially bind to a given DNA sequence.

Proteomics and proteogenomics

Complementary to genome and transcriptome analysis, proteomics and proteogenomics are increasingly becoming complementary analytical approaches. For comparative proteomic analysis, Goh [11] have developed Fuzzy-FishNET, for robust selection of relevant protein complexes, across patient samples. For identifying novel proteins, especially in disease samples, proteogenomic peptides are mapped directly to genomes. Li et al. [12] have thoroughly investigated two popular software tools, X!Tamdem and Comet, for this approach, with several variations and recommend at least two methods for search result validation with separate filtering of known and novel peptides, for improved proteogenomic search sensitivity.

Genome, epigenome and Gut microbiome analyses

In the era of genome-wide transcriptome studies from different tissues and cell-types, Yarmishyn et al. [13] have focussed on the subcellular compartment, the peroxisome, to study mRNA enrichment in a pioneering study, and report a critical cholesterol biosynthesis pathway enzyme as the most enriched transcript. Transcript concentrations vary enormously in a cell, especially as a result of external metabolic stimuli. Kumagai et al. [14] report a strong association between RNA degradation patterns and expression dynamics in dendritic cells. Additionally, their comprehensive analysis of patterns of RNA degradation analysis has resulted in a methodology to predict RNA motifs. For scientists working with the model organism, Arabidopsis thaliana, Su et al. [16] have a develop TEA as an integrated methylome platform, for analysing, annotating and visualizing epigenomic data from large-scale bisulfite-sequencing approaches.

Adaptations to climatic changes and environmental pollutants have a quantifiable effect of the genomes of organisms facing these challenges. Suryavanshi et al. [15] have studied the extremophile, Arabidopsis halleri growing in heavy-metal contaminated soils and identified a link between evolutionary adaptation and gene copy numbers, permitting the accumulation of toxic metals with a lowering of immune defense response. Weng et al. [17] have studied the evolutionary adaptation of hibernation in non-hibernating frogs by tracking their gut microbiome and concluded that artificial hibernation could result in exposure to pathogenic bacteria, reducing the fitness of this species.

Disease informatics

Saini et al. [18] propose a new genetic algorithm-based gene masking approach to accurately classify cancers, towards discrimination between subtypes and for early detection. A new gene sub-network-based feature selection approach by Doungpan et al. was applied to four lung cancer data sets [19]. In this case study the authors’ proposed algorithm improved the classification of sub-networks and agreement between gene and gene-set levels compared to greedy search algorithm.

Lee et al. presented a network approach [20] applied to a data set on schizophrenia DNA methylation in the human frontal cortex [21]. The differential methylation analysis revealed both hyper and hypomethylated promoters that may have complementary roles in the development of schizophrenia. Subramanian et al. [22] analysed palindromic DNA sequences in breast cancer genomes and noted that a significant number occurring near oncogenes are differentiated, suggesting tumour progression and/or poor prognosis.

Resistance to chemotherapeutic agents has been a serious issue in treating cancer patients. Koh et al. [23] have characterized the role of the DNA polymerase alpha subunit B (POLA2) gene in human lung cancer cells. Their results show that the POLA2 knock down is indeed involved in gemcitabine resistance, and suggest that POLA2 may be used as a prognostic biomarker for lung cancer patient outcome. Srinivasulu et al. estimated the survival time of 247 patients with glioblastoma multiforme (GBM) in context of miRNA expression profiles [24]. They identified 24 miRNA signatures that are associated with survival time which might have utility in improving GBM therapy. Chen et al. applied association rule mining to the immunoinformatics problem of characterizing human influenza virus antigenic evolution [25]. Rules of co-occuring mutations revealed interactions of multiple site mutations in influenza A/H3N2, A/H1N1 and B viruses that contribute to better understanding of antigenic evolution and potential prediction of mutations in HA1.


With InCoB maturing and a growing interest of having broadly themed bioinformatics conferences for practitioners and developers, next year’s conference will be held in Shenzhen from Sept. 20–22, 2017 [26]. Easychair is expected to open for paper submissions in January 2017.


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  2. APBioNet. Organization. Accessed 18 Nov 2016.

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  4. Schönbach C, Verma C, Wee LWK, Bond PJ, Ranganathan S. Bioinformatics and systems biology update from the 15th International Conference on Bioinformatics (InCoB 2016). BMC Bioinformatics. 2016. doi:10.1186/s12859-016-1369-y.

  5. Moon M, Nakai K. Stable Feature Selection Based on the Ensemble L1-norm Support Vector Machine for Biomarker Discovery. BMC Genomics. 2016. doi:10.1186/s12864-016-3320-z.

  6. Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, et al. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC Genomics. 2016. doi:10.1186/s12864-016-3317-7.

  7. Hou M, Tian F, Jiang S, Kong L, Yang D, Gao G. LocExpress: a web server for efficiently estimating expression of novel transcripts. BMC Genomics. 2016. doi:10.1186/s12864-016-3329-3.

  8. Ponyared P, Ponsawat J, Tongsima S, Seresangtakul P, Akkasaeng C, Tantisuwichwong N. ESAP Plus: a web-based server for EST-SSR marker development. BMC Genomics. 2016. doi:10.1186/s12864-016-3328-4.

  9. Dutta S, Madan S, Sundar D. Exploiting the recognition code for elucidating the mechanism of zinc finger protein-DNA interactions. BMC Genomics. 2016. doi:10.1186/s12864-016-3324-8.

  10. Dutta S, Madan S, Parikh H, Sundar D. An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA. BMC Genomics. 2016. doi:10.1186/s12864-016-3323-9.

  11. Goh WWB. Fuzzy-FishNET: A highly reproducible protein complex-based approach for feature selection in comparative proteomics. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0228-z.

  12. Li H, Joh YS, Kim H, Paek E, Lee SW, Hwang KB. Evaluating the effect of database inflation in proteogenomic search on sensitive and reliable peptide identification. BMC Genomics. 2016. doi:10.1186/s12864-016-3327-5.

  13. Yarmishyn AA, Kremenskoy M, Batagov AO, Preuss A, Wong JH, Kurochkin IV. Genome-wide analysis of mRNAs associated with mouse peroxisomes. BMC Genomics. 2016. doi:10.1186/s12864-016-3330-x.

  14. Kumagai Y, Vandenbon A, Teraguchi S, Shizuo Akira S, Suzuki Y. Genome-wide map of RNA degradation kinetics patterns in dendritic cells after LPS stimulation facilitates identification of primary sequence and secondary structure motifs in mRNAs. BMC Genomics. 2016. doi:12864-17-10.1186/s12864-016-3325-7.

  15. Suryawanshi V, Talke IN, Weber M, Eils R, Brors B, Clemens S, et al. Between-species differences in gene copy number are enriched among functions critical for adaptive evolution in Arabidopsis halleri. BMC Genomics. 2016. doi:10.1186/s12864-016-3319-5.

  16. Su SY, Chen SH, Lu IH, Chiang YS, Wang YB, Chen PY, et al. TEA: The epigenome platform for Arabidopsis methylome study. BMC Genomics. 2016. doi:10.1186/s12864-016-3326-6.

  17. Weng FCH, Yang YJ, Wang D. Functional analysis for gut microbes of the brown tree frog (Polypedates megacephalus) in artificial hibernation. BMC Genomics. 2016. doi:10.1186/s12864-016-3318-6.

  18. Saini H, Lal SP, Naidu VV, Pickering VW, Singh G, Tsunoda T, et al. Gene Masking - A technique to improve accuracy for cancer classification with high dimensionality in microarray data. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0233-2.

  19. Doungpan N, Engchuan W, Meechai A, Chan JH. GSNFS: Gene subnetwork biomarker identification of lung cancer expression data. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0231-4.

  20. Lee SA, Huang KC. Epigenetic profiling of human brain differential DNA methylation networks in schizophrenia. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0229-y.

  21. Subramanian S, Chaparala S, Avali V, Ganapathiraju M. A pilot study on the prevalence of DNA palindromes in breast cancer genomes. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0232-3.

  22. Koh V, Kwan HY, Tan WL, Mah TL, Peng W, Yong WP. Knockdown of POLA2 increases gemcitabine resistance in lung cancer cells. BMC Genomics. 2016. doi:10.1186/s12864-016-3322-x.

  23. Srinivasulu YS, Huang HL, Ho SY. Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures. BMC Genomics. 2016. doi:10.1186/s12864-016-3321-y.

  24. Jaffe AE, Gao Y, Deep-Soboslay A, Tao R, Hyde TM, Weinberger DR, et al. Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci. 2016;19(1):40–7.

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  25. Chen H, Zhou X, Zheng J, Kwoh CK. Rules of co-occurring mutations characterize the antigenic evolution of human influenza A/H3N2, A/H1N1 and B viruses. BMC Medical Genomics. 2016. doi:10.1186/s12920-016-0230-5.

  26. The 16th International Conference on Bioinformatics (InCoB 2017), 2016. Accessed 28 Nov 2016.

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We thank all reviewers for their time and effort. We also thank Duke-NUS Graduate Medical School, NUS Centre for Bioimaging Sciences, National Supercomputing Centre Singapore, Biomedical Research Council, Experimental Therapeutics Centre, Genome Institute of Singapore, Biomedical Sciences Institute P53 Laboratory and Institute for Medical Biology supporting InCoB2016 through sponsorships. We are grateful the excellent support by staff of the APBioNet Secretariat and Bioinformatics Institute.


This article has been published as part of BMC Genomics Volume 17 Supplement 3, 2016: 15th International Conference On Bioinformatics (INCOB 2016): genomics. The full contents of the supplement are available online at

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Correspondence to Christian Schönbach or Shoba Ranganathan.

Additional files

Additional file 1:

List of InCoB2016 Reviewers (PDF 109 kb)

Additional file 2:

InCoB2016 Best Paper Awards (PDF 86 kb)

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Schönbach, C., Verma, C., Wee, L.J.K. et al. 2016 update on APBioNet’s annual international conference on bioinformatics (InCoB). BMC Genomics 17 (Suppl 13), 1036 (2016).

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