V současné době prudce narůstá množství identifikovaných genů, které jsou-li poškozeny nebo je-li změněna jejich funkce či regulační vztahy způsobují dědičnou predispozici k určitému onemocnění. Molekulárně genetická diagnostika je nyní dostupnou součástí vyšetření u řady geneticky podmíněných chorob. Laboratorní metody umožňují detekci široké škály mutací, které lze obecně definovat jako odchylky od specifické DNA sekvence ve srovnání s referenční sekvencí zveřejněnou v genové databázi. V některých případech je však obtížné rozlišit, zda je detekovaná sekvenční varianta hledanou onemocnění způsobující mutací nebo zda se jedná o neutrální (polymorfní) variantu nemající vztah k onemocnění jedince. Dědičné formy komplexních onemocnění, jako jsou například hereditární formy nádorových onemocnění, jsou z hlediska interpretace závažnosti mutace velmi problematickou skupinou. Další analýzy na DNA a na proteinové úrovni s využitím bioinformatiky však mohou míru patogenity sekvenčních variant nejasného významu odhalit. Určení konkrétní příčiny genetické predispozice k onemocnění a míra patogenity za onemocnění odpovědné mutace má význam pro včasný záchyt jedinců ve velkém riziku onemocnění, pro cílená preventivní a léčebná opatření a umožňuje v závažných případech prenatální nebo případně také preimplantační diagnostiku.

Molecular genetic diagnostics is available for increasing number of genetically determined diseases. Awide spectrum of mutations can be detected by laboratory methods. A mutation can be defined as a change in a specific DNA sequence when compared with the reference sequence published in the gene database. However, in some cases it is difficult to distinguish if the detected sequence variant is a causal mutation or a neutral (polymorphic) variation without any effect on phenotype. The interpretation of rare sequence variants of unknown significance detected in disease-causing genes becomes an increasingly important problem. Further analysis on DNA and on protein levels with the use of bioinformatics are needed to reveal the effect of rare sequence variants. Inherited complex disorders, for example rare hereditary forms of cancer diseases, represent a challenge tomolecular geneticists. The identification of exact causal mutation directly responsible for the development of the disease and for the assessment of disease risk resulting from this genetic variation has further implications. Predictive genetic diagnostics allows identify relatives at high risk of genetically determined disease and use of targeted preventive and therapeutic approaches. In severe cases it allows also prenatal or pre-implantation diagnostics.

• MeSH
• Genetic Predisposition to Disease genetics   prevention & control   MeSH
• Genetic Testing MeSH
• Humans MeSH
• Molecular Biology MeSH
• Mutation MeSH
• DNA Mutational Analysis MeSH
• Polymorphism, Genetic MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Publication type
• Database MeSH
• Review MeSH

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

• MeSH
• Humans MeSH
• Pandemics MeSH
• Virus Diseases * drug therapy   genetics   MeSH
• Computational Biology * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

BACKGROUND: Recent advances in genomics indicate functional significance of a majority of genome sequences and their long range interactions. As a detailed examination of genome organization and function requires very high quality genome sequence, the objective of this study was to improve reference genome assembly of banana (Musa acuminata). RESULTS: We have developed a modular bioinformatics pipeline to improve genome sequence assemblies, which can handle various types of data. The pipeline comprises several semi-automated tools. However, unlike classical automated tools that are based on global parameters, the semi-automated tools proposed an expert mode for a user who can decide on suggested improvements through local compromises. The pipeline was used to improve the draft genome sequence of Musa acuminata. Genotyping by sequencing (GBS) of a segregating population and paired-end sequencing were used to detect and correct scaffold misassemblies. Long insert size paired-end reads identified scaffold junctions and fusions missed by automated assembly methods. GBS markers were used to anchor scaffolds to pseudo-molecules with a new bioinformatics approach that avoids the tedious step of marker ordering during genetic map construction. Furthermore, a genome map was constructed and used to assemble scaffolds into super scaffolds. Finally, a consensus gene annotation was projected on the new assembly from two pre-existing annotations. This approach reduced the total Musa scaffold number from 7513 to 1532 (i.e. by 80%), with an N50 that increased from 1.3 Mb (65 scaffolds) to 3.0 Mb (26 scaffolds). 89.5% of the assembly was anchored to the 11 Musa chromosomes compared to the previous 70%. Unknown sites (N) were reduced from 17.3 to 10.0%. CONCLUSION: The release of the Musa acuminata reference genome version 2 provides a platform for detailed analysis of banana genome variation, function and evolution. Bioinformatics tools developed in this work can be used to improve genome sequence assemblies in other species.

• MeSH
• Molecular Sequence Annotation MeSH
• Musa genetics   MeSH
• Genetic Markers MeSH
• Genome, Plant * MeSH
• Contig Mapping MeSH
• Sequence Analysis, DNA MeSH
• Computational Biology methods   MeSH
• High-Throughput Nucleotide Sequencing MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Computational methods that allow predicting the effects of nonsynonymous substitutions are an integral part of exome studies. Here, we validated and improved their specificity by performing a comprehensive bioinformatics analysis combined with experimental and clinical data on a model of glucokinase (GCK): 8835 putative variations, including 515 disease-associated variations from 1596 families with diagnoses of monogenic diabetes (GCK-MODY) or persistent hyperinsulinemic hypoglycemia of infancy (PHHI), and 126 variations with available or newly reported (19 variations) data on enzyme kinetics. We also proved that high frequency of disease-associated variations found in patients is closely related to their evolutionary conservation. The default set prediction methods predicted correctly the effects of only a part of the GCK-MODY-associated variations and completely failed to predict the normoglycemic or PHHI-associated variations. Therefore, we calculated evidence-based thresholds that improved significantly the specificity of predictions (≤75%). The combined prediction analysis even allowed to distinguish activating from inactivating variations and identified a group of putatively highly pathogenic variations (EVmutation score <-7.5 and SNAP2 score >70), which were surprisingly underrepresented among MODY patients and thus under negative selection during molecular evolution. We suggested and validated the first robust evidence-based thresholds, which allow improved, highly specific predictions of disease-associated GCK variations.

• MeSH
• Enzyme Activation MeSH
• Diabetes Mellitus, Type 2 genetics   metabolism   MeSH
• Glucokinase chemistry   genetics   MeSH
• Kinetics MeSH
• Humans MeSH
• Evolution, Molecular MeSH
• Disease Susceptibility MeSH
• Amino Acid Substitution * MeSH
• Computational Biology * methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Informatics MeSH
• Molecular Biology MeSH

• Conspectus
• Knihovnictví. Informatika
• NML Fields
• biologie
• knihovnictví, informační věda a muzeologie

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

• MeSH
• Biological Science Disciplines * MeSH
• Genomics MeSH
• Humans MeSH
• Proteins MeSH
• Computational Biology organization & administration   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

• MeSH
• Algorithms MeSH
• Biomedical Research MeSH
• Databases, Factual MeSH
• Program Evaluation MeSH
• Education, Continuing MeSH
• Curriculum MeSH
• Reproducibility of Results MeSH
• Quality Control * MeSH
• Data Collection MeSH
• Software MeSH
• User-Computer Interface MeSH
• Computational Biology education   standards   MeSH
• Research Personnel MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH

• MeSH
• Biomedical Engineering MeSH
• Biomedical Technology MeSH
• Telemedicine MeSH
• Computational Biology MeSH
• Publication type
• Congress MeSH
• Collected Work MeSH
• News MeSH

• Conspectus
• Biotechnologie. Genetické inženýrství
• NML Fields
• biomedicínské inženýrství
• lékařská informatika

The review provides a brief overview of techniques used for interpretation of mass spectrometric data acquired in proteomic experiments. Processing of the data by bioinformatics is an important part of protein identification in proteomics. Analyzed samples can be measured using different mass spectrometric techniques and the obtained data require different methods of processing. Genomic databases rapidly grow and a suitable way of manipulation with data becomes a crucial step for their correct interpretation. Basic methods of interpretation of mass spectrometric data are summarized and some examples show how the corresponding programs work.

• MeSH
• Peptide Mapping MeSH
• Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods   instrumentation   MeSH

• MeSH
• Base Sequence methods   MeSH
• Sequence Analysis, Protein methods   MeSH
• Computational Biology methods   MeSH

• Conspectus
• Biochemie. Molekulární biologie. Biofyzika
• NML Fields
• biochemie