Úvod: Nemocnicní informacní systémy jsou ve fakultních nemocnicích vysoce rozšírené, ale nejsou dostacující jak pro využití behem lécby pacienta, tak i pro dlouhodobé lékarské výzkumy. Lékari si casto vedou své vlastní tabulky nebo databáze bez spolupráce s oddelením informatiky a casto také dochází k porušování zákonu Ceské republiky. Cíl: Tento clánek popisuje príklad využití vlastní lékarské aplikace, která umí spolupracovat s nemocnicním informacním systémem a je zároven vhodná pro použití v dlouhodobých lékarských výzkumech. Metody: Aplikace je vyvinula v jazyce Visual Basic. Je popsán farmakoekonomický výzkum lécby Radiojódem I135 u pacientu trpících onemocnením štítné žlázy. Výsledky: Financní náklady na lécbu jednoho pacienta ambulantne jsou 2850 Kc. Což je 16% ve srovnání s lécbou s hospitalizací a 25% ve srovnání s chirurgickou lécbou. Diskuze: Ambulantní lécba muže být pro zdravotnický systém vysoce motivující po financní stránce a zvýšit tak její podíl v metodách ve svuj prospech.
Background: Hospital Information Systems widely used in departments of university hospitals are not sufficient for both storing data about patients treatment and long-term research. Clinicians often use custom-developed applications which are maintained without any cooperation with the management of the hospitals and mostly break law in the Czech Republic. Objectives: This article describes using such an application in cooperation with the Hospital Information System. It also describes an example of a research of cost effectiveness thyroid gland diseases treatment using Radioiodine 131I in outpatient regime compared to hospitalization or an alternative surgery. Methods: The database application is developed in Visual Basic. The research studies the treatment by the Radioiodine 131I in 45 patients. We evaluated the financial cost of radioiodine therapy in the outpatient regime and hospitalization compared with a surgery. Results: The financial costs for 1 patient is 114 EUR, it means 16% if compared with the same treatment in a hospital and only 25% of the possible alternative operation. Conclusion: This study describes that the treatment by outpatient regime can be a motivating alternative compared to the treatment by 131I at a hospital or even the surgery.
- Keywords
- finanční nákladovost, radiojód, nemocniční informační systém,
- MeSH
- Ambulatory Care economics MeSH
- Medical Records Systems, Computerized standards utilization MeSH
- Drug Therapy methods MeSH
- Financing, Organized MeSH
- Inpatients statistics & numerical data MeSH
- Humans MeSH
- Health Care Costs statistics & numerical data MeSH
- Thyroid Diseases diagnosis ultrasonography MeSH
- Computer Systems utilization MeSH
- Thyroid Gland MeSH
- Records standards statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Database MeSH
UNLABELLED: Nowadays the most used pipeline for protein identification consists in the comparison of the MS/MS spectra to reference databases. Search algorithms compare obtained spectra to an in silico digestion of a sequence database to find exact matches. In this context, the database has a paramount importance and will determine in a great deal the number of identifications and its quality, being this especially relevant for non-model plant species. Using a single Viridiplantae database (NCBI, UniProt) and TAIR is not the best choice for non-model species since they are underrepresented in databases resulting in poor identification rates. We demonstrate how it is possible to improve the rate and quality of identifications in two orphan species, Quercus ilex and Pinus radiata, by using SEQUEST and a combination of public (Viridiplantae NCBI, UniProt) and a custom-built specific database which contained 593,294 and 455,096 peptide sequences (Quercus and Pinus, respectively). These databases were built after gathering and processing (trimming, contiging, 6-frame translation) publicly available RNA sequences, mostly ESTs and NGS reads. A total of 149 and 1533 proteins were identified from Quercus seeds and Pinus needles, representing a 3.1- or 1.5-fold increase in the number of protein identifications and scores compared to the use of a single database. Since this approach greatly improves the identification rate, and is not significantly more complicated or time consuming than other approaches, we recommend its routine use when working with non-model species. BIOLOGICAL SIGNIFICANCE: In this work we demonstrate how the construction of a custom database (DB) gathering all available RNA sequences and its use in combination with Viridiplantae public DBs (NCBI, UniProt) significantly improve protein identification when working with non-model species. Protein identification rate and quality is higher to those obtained in routine procedures based on using only one database (commonly Viridiplantae from NCBI), as we demonstrated analyzing Quercus seeds and Pine needles. The proposed approach based on the building of a custom database is not difficult or time consuming, so we recommend its routine use when working with non-model species. This article is part of a Special Issue entitled: Proteomics of non-model organisms.
- MeSH
- Pinus genetics metabolism MeSH
- Databases, Protein * MeSH
- Quercus genetics metabolism MeSH
- Proteome genetics metabolism MeSH
- Proteomics methods MeSH
- Plant Proteins genetics metabolism MeSH
- Amino Acid Sequence MeSH
- Base Sequence MeSH
- Sequence Analysis, Protein methods MeSH
- Sequence Analysis, RNA methods MeSH
- Seeds genetics metabolism MeSH
- Publication type
- Journal Article MeSH
- Dataset MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: The majority of infectious diseases of cultured fish is caused by bacteria. Rapid identification of bacterial pathogens is necessary for immediate management. The present study developed a custom Main Spectra Profile (MSP) database and validate the method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid identification of fish bacterial pathogens. Streptococcus agalactiae, Streptococcus iniae, Aeromonas hydrophila, Aeromonas veronii, and Edwardsiella tarda obtained from diseased fish were used as representative bacterial pathogens in this study. Bacterial peptides were extracted to create a Main Spectra Profile (MSP), and the MSPs of each bacterial species was added into the MALDI Biotyper database. Fifteen additional isolates of each bacterial species were tested to validate the utilized technique. RESULTS: The MSPs of all field isolates were clearly distinguishable, and the MSPs of the same species were clustered together. The identification methodology was validated with 75 bacterial isolates. The reliability and specificity of the method were determined with MALDI Biotyper log score values and matching results with 16 s rDNA sequencing. The species identification using the public MALDI Biotyper library (Bruker MALDI Biotyper) showed unreliable results (log score < 2.000) with 42.67% matching result with the reference method. In contrast, accurate identification was obtained when using the custom-made database, giving log score > 2.115, and a 100% matching result. CONCLUSION: This study demonstrates an effective identification of fish bacterial pathogens when a complete custom-made MSP database is applied. Further applications require a broad, well-established database to accommodate prudent identification of many fish bacterial pathogens by MALDI-TOF MS.
- MeSH
- Bacterial Infections microbiology veterinary MeSH
- Bacterial Proteins chemistry MeSH
- Databases, Protein * MeSH
- Fish Diseases microbiology MeSH
- Fishes MeSH
- Cluster Analysis MeSH
- Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization methods veterinary MeSH
- Aquaculture MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.
- MeSH
- Molecular Sequence Annotation MeSH
- Point Mutation * MeSH
- Databases, Protein * MeSH
- Datasets as Topic MeSH
- Internet MeSH
- Models, Molecular MeSH
- Proteins chemistry genetics MeSH
- Software MeSH
- Protein Stability MeSH
- Machine Learning statistics & numerical data MeSH
- Computational Biology methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.
- MeSH
- Bacterial Proteins * genetics metabolism MeSH
- Databases, Protein MeSH
- Virulence Factors * genetics metabolism MeSH
- Genome, Bacterial * genetics MeSH
- Paenibacillus larvae * genetics pathogenicity metabolism MeSH
- Proteogenomics * methods MeSH
- Proteomics methods MeSH
- Bees microbiology MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.
With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.
- MeSH
- Algorithms MeSH
- Databases, Factual MeSH
- Databases, Genetic MeSH
- Genomics MeSH
- Internet MeSH
- Medical Oncology MeSH
- Humans MeSH
- Neoplasms genetics MeSH
- Computer Graphics MeSH
- Proteomics MeSH
- Workflow MeSH
- Software MeSH
- Transcriptome MeSH
- User-Computer Interface MeSH
- Computational Biology methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Deregulation of microRNA (miRNA) expression plays a critical role in the transition from a physiological to a pathological state. The accurate miRNA promoter identification in multiple cell types is a fundamental endeavor towards understanding and characterizing the underlying mechanisms of both physiological as well as pathological conditions. DIANA-miRGen v4 (www.microrna.gr/mirgenv4) provides cell type specific miRNA transcription start sites (TSSs) for over 1500 miRNAs retrieved from the analysis of >1000 cap analysis of gene expression (CAGE) samples corresponding to 133 tissues, cell lines and primary cells available in FANTOM repository. MiRNA TSS locations were associated with transcription factor binding site (TFBSs) annotation, for >280 TFs, derived from analyzing the majority of ENCODE ChIP-Seq datasets. For the first time, clusters of cell types having common miRNA TSSs are characterized and provided through a user friendly interface with multiple layers of customization. DIANA-miRGen v4 significantly improves our understanding of miRNA biogenesis regulation at the transcriptional level by providing a unique integration of high-quality annotations for hundreds of cell specific miRNA promoters with experimentally derived TFBSs.
- MeSH
- Molecular Sequence Annotation MeSH
- Cell Line MeSH
- Databases, Nucleic Acid * MeSH
- Transcription, Genetic MeSH
- Genome * MeSH
- Internet MeSH
- Humans MeSH
- MicroRNAs genetics metabolism MeSH
- Transcription Initiation Site MeSH
- Primary Cell Culture MeSH
- Promoter Regions, Genetic * MeSH
- Base Sequence MeSH
- Software * MeSH
- Transcription Factors genetics metabolism MeSH
- Protein Binding MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Searching for similar sequences in a database via BLAST or a similar tool is one of the most common bioinformatics tasks applied in general, and to non-coding RNAs in particular. However, the results of the search might be difficult to interpret due to the presence of partial matches to the database subject sequences. Here, we present rboAnalyzer - a tool that helps with interpreting sequence search result by (1) extending partial matches into plausible full-length subject sequences, (2) predicting homology of RNAs represented by full-length subject sequences to the query RNA, (3) pooling information across homologous RNAs found in the search results and public databases such as Rfam to predict more reliable secondary structures for all matches, and (4) contextualizing the matches by providing the prediction results and other relevant information in a rich graphical output. Using predicted full-length matches improves secondary structure prediction and makes rboAnalyzer robust with regards to identification of homology. The output of the tool should help the user to reliably characterize non-coding RNAs in BLAST output. The usefulness of the rboAnalyzer and its ability to correctly extend partial matches to full-length is demonstrated on known homologous RNAs. To allow the user to use custom databases and search options, rboAnalyzer accepts any search results as a text file in the BLAST format. The main output is an interactive HTML page displaying the computed characteristics and other context of the matches. The output can also be exported in an appropriate sequence and/or secondary structure formats.
- Publication type
- Journal Article MeSH
