Asia Pacific Bioinformatics Network's 10th InCoB - 1st ISCB Asia Joint Conference 2011

Plenary and keynote speakers in  InCoB/ISCB-Asia Joint Conference 2011 comprises of  experts in the various disciplines of bioinformatics will be invited to present the latest innovations, technologies, ideas and research findings in their respective areas of expertise.

Ancient genomics
The genomes of extinct hominin groups closely related to contemporary humans offer a unique opportunity to identify genetic changes specific to anatomically fully modern humans. Although it is possible to recover mtDNA and occasionally even nuclear DNA sequences from well-preserved remains of organisms that are less than a few hundred thousand years old, determination of ancient hominid sequences is subject to particular difficulties due to DNA degradation, chemical modifications and contamination. Recent advances in large-scale sequencing technologies make it possible to perform direct sequencing of fossil extracts. In the past five years we have generated draft genome sequences for two archaic hominin groups: Neandertals who were known to have lived in Europe and Western Asia until approximately 30 000 years ago, and Denisovans, a group that was newly described based on the genome sequence generated from a bone found in Southern Siberia. I will outline some of the challenges in the generation and analysis of ancient genome sequence data, and discuss the evolutionary insights that have resulted from the sequencing of these genomes.

Dr. Frith's original training was in physics and philosophy (Oxford) and mathematics (Cambridge). He was awarded one of the world's first PhDs in bioinformatics (Boston University, USA). He then held a postdoctoral position jointly at the University of Queensland (Australia) and the RIKEN Yokohama Institute (Japan), before joining the CBRC.

After graduating in Physics and Philosophy at Oxford and the historic Part III Mathematics course at Cambridge, Dr. Frith became one of the world's first Bioinformatics PhDs at Boston University. He has received several awards, including a Howard Hughes Predoctoral Fellowship, and a Harold M. Weintraub Award for outstanding achievement during graduate studies in the biological sciences. His postdoctoral work was jointly at Queensland and RIKEN Yokohama, where he was a core author of the landmark FANTOM3 paper elucidating the transcriptional landscape of the mammalian genome. Dr. Frith is currently at the Computational Biology Research Center, AIST, where he continues to publish fundamental results in biological sequence analysis and develop revolutionary software tools including tantan and LAST.

Underappreciated issues in pairwise sequence comparison
Pairwise sequence comparison is fundamental to much of bioinformatics. I will describe some unsolved problems and recent solutions in this area, that have practical consequences.

• The best method for pairwise alignment, the Smith-Waterman algorithm, has a clear flaw that is not widely known.
• In practice, people use BLAST or similar heuristics. These methods actually avoid the Smith-Waterman flaw, and can produce clearly-better alignments. However, this comes at the cost of a new and little-known imperfection.
• These alignment methods require a choice of score parameters. In practice they are sometimes chosen badly, causing pathological alignments. I will show that some commonly used resources in fact suffer from these problems.
• It is important to distinguish significant alignments from chance alignments. Although significance estimates have been available for decades, they have had limitations, causing them to be used wrongly or not at all. A recent method from the Spouge group at NCBI overcomes these limitations.
• Low-complexity tracts (such as ATATATATATATATATA) confound homology search. This has been obvious for decades, and there are dozens of methods for avoiding spurious low-complexity alignments. Shockingly, none of them actually work. A new method called tantan is the first that does appear to work.
• Because BLAST cannot handle modern multi-gigabase DNA data, a myth has arisen that we need a new class of low-sensitivity "mapping" algorithms. I will describe LAST, a BLAST-like algorithm for giga-scale data. The key insight is that BLAST suffers not from the data's size per se, but from its non-uniform (oligo)nucleotide composition. LAST models both real sequence differences and sequencer errors, and it can accurately find highly-divergent alignments.

Link clustering reveals biological contexts in signed molecular networks

Networks have become a key approach to understanding biological systems. Many biological networks are signed undirected networks, such as gene co-expression networks or genetic interaction networks, which consist of both positive and negative links. However, most of the previous studies either ignore the signs of links or focus on only one type of them. Considering the intrinsic differences between positive and negative links, we speculated that the interconnections among positive and negative links should show distinct features, reflecting their underlying molecular mechanisms. We have defined four different measures of link clustering coefficients to explore the topological structures of signed undirected networks. The results showed that signed molecular networks exhibited distinct structural characteristics with respect to corresponding unsigned networks. Positive links are more adhesive and tend to cluster together, while negative links are more dispersive and usually behave like bridges between positive clusters. Applying these new measures to gene co-expression networks and genetic interaction networks allow us to distinguish links with different biological contexts and identify functional modules with their inter-relationships revealed.

Selected Publications:
Chen CY, Chen ST, Fuh CS, Juan HF, Huang HC, “Coregulation of transcription factors and microRNAs in human transcriptional regulatory network.” BMC Bioinformatics, 12(S1), S41 (2011).

Lin CC, Hsiang JT, Wu CY, Oyang YJ, Juan HF, Huang HC, “Dynamic functional modules in co-expressed protein interaction networks of dilated cardiomyopathy.” BMC Syst. Biol., 4, 138 (2010).

Wu TH, Chang IYF, Chu LCJ, Huang HC, Ng WV, “Modularity of Escherichia coli sRNA regulation revealed by sRNA-targets and protein network analysis.” BMC Bioinformatics, 11 (S7), S11 (2010).

Hwang YC, Lin CC, Chang JY, Mori H, Juan HF, Huang HC, “Predicting essential genes based on network and sequence analysis.” Mol. BioSyst. 5, 1672-1678 (2009).

Lin CC, Juan HF, Hsiang JT, Hwang YC, Mori H, Huang HC, “Essential core of protein-protein interaction network in Escherichia coli.” J. Proteome Res. 8, 1925-1931 (2009).

Hsu CW, Juan HF, Huang HC, “Characterization of microRNA-regulated protein-protein interaction network.” Proteomics, 8, 1975-9 (2008).

Fang YC, Huang HC, Juan HF, “MeInfoText: associated gene methylation and cancer information from text mining.” BMC Bioinformatics, 9, 22 (2008).

Cheng KC, Huang HC, Chen JH, Hsu JW, Cheng HC, Ou CH, Yang WB, Chen ST, Wong CH, Juan HF. “Ganoderma lucidum polysaccharides in human monocytic leukemia cells: from gene expression to network construction.” BMC Genomics, 8, 411 (2007).

Leu JH, Chang CC, Wu JL, Hsu CW, Hirono I, Aoki T, Juan HF, Lo CF, Kou GH, Huang HC. “Comparative analysis of differentially expressed genes in normal and white spot syndrome virus infected Penaeus monodon.” BMC Genomics, 8, 120 (2007).

Juan HF, Wang IH, Huang TC, Li JJ, Chen ST, Huang HC. “Proteomics analysis of a novel compound: cyclic RGD in breast carcinoma cell line MCF-7.” Proteomics 6, 2991-3000 (2006).

Arifuzzaman M, et al. “Large-scale identification of protein-protein interaction of Escherichia coli K-12.” Genome Res. 16, 686-91 (2006).

Wu CC, Huang HC, Juan HF, Chen ST. “GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data.” Bioinformatics, 20, 3691-3 (2004).

Wu CY, et al. “Small molecules targeting severe acute respiratory syndrome human coronavirus.” Proc Natl Acad Sci U S A, 101, 10012-7 (2004).

Mikael Bodén was a senior research fellow at the Institute for Molecular Bioscience 2007-2010, and is currently appointed jointly by the School of Chemistry and Molecular Biosciences and the School of Information Technology and Electrical Engineering. He also has an affiliate appointment at the Institute for Molecular Bioscience at the University of Queensland. Mikael Bodén collaborates widely with structural and cell biologists, protein engineers and genetics researchers.

Probabilistically integrating experimental data to elucidate nuclear sorting of proteins

Major cellular events depend on the integrity of the nuclear space, and the dynamic organization of macromolecules into membrane-less sub-compartments. Despite progress on experimentally characterizing a number of nuclear localization signals, their presence alone remains an unreliable indicator of nuclear translocation of proteins. Further sorting into more specific intra-nuclear locations appears to be largely passive and involve a profusion of case-specific interactions. Support for associating proteins with the nucleus and more specific sub-compartments thus comes from a hodgepodge of data sets, of varying quality and coverage.

Bayesian networks encode expertise by design or from data, integrate heterogeneous information, with missing values, and enable robust and transparent probabilistic inference. This talk will discuss models based on Bayesian networks that explicitly recognize a variety of nuclear localization signals, and integrate relevant amino acid sequence and interaction data for candidate proteins. We also explore determinants of associations between eight intra-nuclear compartments and their proteins in heterogeneous genome-wide data. We develop a Bayesian network that not only predicts locations with better accuracies than comparable predictors but also explains the basis for its decisions. The ability to computationally survey the complete mammalian nuclear proteome enables us to investigate the collective role of transcription factors in each of nuclear sub-compartment.

 BioSharing and ISAcommons - cooperating procedures and standards-driven biocuration in action

BioSharing (www.biosharing.org) works at the global level to build stable linkages in particular between journals, funders, implementing data sharing policies, and well-constituted standardization efforts in the biosciences domain. Its goal is to (i) address overlaps and duplication of efforts that hamper the wider uptake of standards and interfere with the creation of standards-compliant tools, and (ii) expedite the production of an integrated standards-based framework for the capture and sharing of high-throughput genomics and functional genomic bioscience data, in particular.

ISAcommons(www.isacommons.org)is a growing, exemplar ecosystem of international public and internal data curation and sharing solutions built on a common, metadata-focused framework, providing tools and resources to create and manage large, heterogeneous data sets in a coherent manner. The commons is built around the ISA-Tab format and ISA software components and comprise groups from diverse life science scenarios, including toxicogenomics, stem cell genomics, environmental genomics, system biology and metagenomics.

Selected publications

- Sansone, Rocca-Serra, Maguire, Field et al. Towards interoperable bioscience data. Nat Genet (accepted).

- Harland, Larminie, Sansone, Popa et al. Empowering industrial research with shared biomedical vocabularies. Drug Discov Today (2011).

- Gaudet, Bairoch, Field, Sansone et al. Towards BioDBcore. Nucleic Acids Res (2011).

- Rocca-Serra, Brandizi, Maguire, Sklyar et al. ISA software suite. Bioinformatics (2010).

- Field, Sansone, Collis, Booth et al. 'Omics data sharing. Science (2009).

- Taylor, Field, Sansone, Aerts et al. The MIBBI project. Nat Biotechnol(2008).