Genetic Characteristics of Polycythemia Vera and Essential Thrombocythemia in Korean Patients.

BACKGROUND: Despite recent advances in the investigation of myeloproliferative neoplasms (MPN), the impact of genetic heterogeneity on its molecular pathogenesis has not been fully elucidated. Thus, in this study, we aim to characterize the genetic complexity in Korean patients with polycythemia vera (PV) and essential thrombocythemia (ET). METHODS: We conducted association studies using 84 single-nucleotide polymorphisms (SNPs) in 229 patients (96 with PV and 133 with ET) and 170 controls. Further, whole-genome sequencing was performed in six patients (two with JAK2 V617F and four with wild-type JAK2), and putative somatic mutations were validated in a further 69 ET patients. Clinical and laboratory characteristics were also analyzed. RESULTS: Several germline SNPs and the 46 haplotype were significantly associated with PV and ET. Three somatic mutations in MPDZ, IQCH, and CALR genes were selected and validated. The frequency of the CALR mutation was 58.0% (40/69) in ET patients, who did not carry JAK2/MPL mutations. Moreover, compared with JAK2 V617F-positive patients, those with CALR mutations showed lower hemoglobin and hematocrit levels (P = 0.004 and P = 0.002, respectively), higher platelet counts (P =0.008), and a lower frequency of cytoreductive therapy (P = 0.014). CONCLUSION: This study was the first comprehensive investigation of the genetic characteristics of Korean patients with PV and ET. We found that somatic mutations and the 46 haplotype contribute to PV and ET pathogenesis in Korean patients.

Draft Genome Sequence of Entomopathogenic Bacterium Photorhabdus temperata Strain M1021, Isolated from Nematodes.

Photorhabdus temperata strain M1021 is an entomopathogenic bacterium belonging to the family Enterobacteriaceae and is symbiotically associated with nematodes. The draft genome sequence of P. temperata strain M1021 consists of 5,598,253 bp with a G+C content of 43.7%, and it has 6,120 protein-coding genes.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies.

BACKGROUND: BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. RESULTS: The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. CONCLUSION: We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

The 2nd DBCLS BioHackathon: interoperable bioinformatics Web services for integrated applications.

BACKGROUND: The interaction between biological researchers and the bioinformatics tools they use is still hampered by incomplete interoperability between such tools. To ensure interoperability initiatives are effectively deployed, end-user applications need to be aware of, and support, best practices and standards. Here, we report on an initiative in which software developers and genome biologists came together to explore and raise awareness of these issues: BioHackathon 2009. RESULTS: Developers in attendance came from diverse backgrounds, with experts in Web services, workflow tools, text mining and visualization. Genome biologists provided expertise and exemplar data from the domains of sequence and pathway analysis and glyco-informatics. One goal of the meeting was to evaluate the ability to address real world use cases in these domains using the tools that the developers represented. This resulted in i) a workflow to annotate 100,000 sequences from an invertebrate species; ii) an integrated system for analysis of the transcription factor binding sites (TFBSs) enriched based on differential gene expression data obtained from a microarray experiment; iii) a workflow to enumerate putative physical protein interactions among enzymes in a metabolic pathway using protein structure data; iv) a workflow to analyze glyco-gene-related diseases by searching for human homologs of glyco-genes in other species, such as fruit flies, and retrieving their phenotype-annotated SNPs. CONCLUSIONS: Beyond deriving prototype solutions for each use-case, a second major purpose of the BioHackathon was to highlight areas of insufficiency. We discuss the issues raised by our exploration of the problem/solution space, concluding that there are still problems with the way Web services are modeled and annotated, including: i) the absence of several useful data or analysis functions in the Web service "space"; ii) the lack of documentation of methods; iii) lack of compliance with the SOAP/WSDL specification among and between various programming-language libraries; and iv) incompatibility between various bioinformatics data formats. Although it was still difficult to solve real world problems posed to the developers by the biological researchers in attendance because of these problems, we note the promise of addressing these issues within a semantic framework.

Identification of type 2 diabetes-associated combination of SNPs using support vector machine.

BACKGROUND: Type 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways. RESULTS: We analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method. CONCLUSIONS: Support Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.

Genome-wide analysis to predict protein sequence variations that change phosphorylation sites or their corresponding kinases.

We define phosphovariants as genetic variations that change phosphorylation sites or their interacting kinases. Considering the essential role of phosphorylation in protein functions, it is highly likely that phosphovariants change protein functions. Therefore, a comparison of phosphovariants between individuals or between species can give clues about phenotypic differences. We categorized phosphovariants into three subtypes and developed a system that predicts them. Our method can be used to screen important polymorphisms and help to identify the mechanisms of genetic diseases.

WoLF PSORT: protein localization predictor.

WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org.

DBTGR: a database of tunicate promoters and their regulatory elements.

The high similarity of tunicates and vertebrates during their development coupled with the transparency of tunicate larvae, their well-studied cell lineages and the availability of simple and efficient transgenesis methods makes of this subphylum an ideal system for the investigation of vertebrate physiological and developmental processes. Recently, the sequencing of two different Ciona genomes has lead to the identification of numerous genes. In order to better understand the regulation of these genes, a database was created containing information on regulation of tunicate genes collected from literature. It includes for instance information regarding the minimal promoter length, the transcription factors involved and their binding sites, as well as the localization of the gene expression. Additionally, binding sites for characterized transcription factors were predicted based on published in vitro recognition sites. Comparison of the promoters of homologous genes in different species is also provided to allow identification of conserved cis elements. At the time of writing, information about 184 promoters, containing 73 identified binding sites and >2000 newly predicted binding sites is available. This database is accessible at http://dbtgr.hgc.jp.

Discrimination of outer membrane proteins using support vector machines.

MOTIVATION: Discriminating outer membrane proteins from other folding types of globular and membrane proteins is an important task both for dissecting outer membrane proteins (OMPs) from genomic sequences and for the successful prediction of their secondary and tertiary structures. RESULTS: We have developed a method based on support vector machines using amino acid composition and residue pair information. Our approach with amino acid composition has correctly predicted the OMPs with a cross-validated accuracy of 94% in a set of 208 proteins. Further, this method has successfully excluded 633 of 673 globular proteins and 191 of 206 alpha-helical membrane proteins. We obtained an overall accuracy of 92% for correctly picking up the OMPs from a dataset of 1087 proteins belonging to all different types of globular and membrane proteins. Furthermore, residue pair information improved the accuracy from 92 to 94%. This accuracy of discriminating OMPs is higher than that of other methods in the literature, which could be used for dissecting OMPs from genomic sequences. AVAILABILITY: Discrimination results are available at http://tmbeta-svm.cbrc.jp.

Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs.

MOTIVATION: The subcellular location of a protein is closely correlated to its function. Thus, computational prediction of subcellular locations from the amino acid sequence information would help annotation and functional prediction of protein coding genes in complete genomes. We have developed a method based on support vector machines (SVMs). RESULTS: We considered 12 subcellular locations in eukaryotic cells: chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular medium, Golgi apparatus, lysosome, mitochondrion, nucleus, peroxisome, plasma membrane, and vacuole. We constructed a data set of proteins with known locations from the SWISS-PROT database. A set of SVMs was trained to predict the subcellular location of a given protein based on its amino acid, amino acid pair, and gapped amino acid pair compositions. The predictors based on these different compositions were then combined using a voting scheme. Results obtained through 5-fold cross-validation tests showed an improvement in prediction accuracy over the algorithm based on the amino acid composition only. This prediction method is available via the Internet.