Inherited haemoglobinopathies are the most common monogenic diseases, with millions of carriers and patients worldwide. At present, we know several hundred disease-causing mutations on the globin gene clusters, in addition to numerous clinically important trans-acting disease modifiers encoded elsewhere and a multitude of polymorphisms with relevance for advanced diagnostic approaches. Moreover, new disease-linked variations are discovered every year that are not included in traditional and often functionally limited locus-specific databases. This paper presents IthaGenes, a new interactive database of haemoglobin variations, which stores information about genes and variations affecting haemoglobin disorders. In addition, IthaGenes organises phenotype, relevant publications and external links, while embedding the NCBI Sequence Viewer for graphical representation of each variation. Finally, IthaGenes is integrated with the companion tool IthaMaps for the display of corresponding epidemiological data on distribution maps. IthaGenes is incorporated in the ITHANET community portal and is free and publicly available at http://www.ithanet.eu/db/ithagenes.
20Haemoglobinopathies are the commonest monogenic diseases, with millions of carriers and 21 patients worldwide. Online resources for haemoglobinopathies are largely divided into 22 specialised sites catering for patients, researchers and clinicians separately. However, the 23 severity, ubiquity and surprising genetic complexity of the haemoglobinopathies call for an 24 integrated website to serve as a free and comprehensive repository and tool for patients, 25scientists and health professionals alike. This paper presents the ITHANET community portal, 33world and is freely available for the public at http://www.ithanet.eu.
BackgroundThe prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.ResultsWe predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.ConclusionsWe have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.
Backgroundβ-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.ResultsWe have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.ConclusionsWe have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.
b-Thalassaemia is one of the most common autosomal recessive single-gene disorder worldwide, with a carrier frequency of 12% in Cyprus. Prenatal tests for at risk pregnancies use invasive methods and development of a non-invasive prenatal diagnostic (NIPD) method is of paramount importance to prevent unnecessary risks inherent to invasive methods. Here, we describe such a method by assessing a modified version of next generation sequencing (NGS) using the Illumina platform, called 'targeted sequencing', based on the detection of paternally inherited fetal alleles in maternal plasma. We selected four single-nucleotide polymorphisms (SNPs) located in the b-globin locus with a high degree of heterozygosity in the Cypriot population. Spiked genomic samples were used to determine the specificity of the platform. We could detect the minor alleles in the expected ratio, showing the specificity of the platform. We then developed a multiplexed format for the selected SNPs and analysed ten maternal plasma samples from pregnancies at risk. The presence or absence of the paternal mutant allele was correctly determined in 27 out of 34 samples analysed. With haplotype analysis, NIPD was possible on eight out of ten families. This is the first study carried out for the NIPD of b-thalassaemia using targeted NGS and haplotype analysis. Preliminary results show that NGS is effective in detecting paternally inherited alleles in the maternal plasma.
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