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.
... , 171 -- 8 Predictive Methods Using Protein Sequences, 197 -- 9 Protein Structure Prediction and Analysis ... ... Wolfsberg -- PART TWO -- ANALYSIS AT THE NUCLEOTIDE LEVEL -- 5 Predictive Methods Using DNA Sequences ... ... Mullikin and Stephen T Sherry -- PART THREE -- ANALYSIS AT THE PROTEIN LEVEL -- 8 Predictive Methods ... ... Baxevanis -- I 2 Creation and Analysis of Protein Multiple Sequence Alignments, 325 -- Geoffrey J. ... ... Bouffard -- I 4 Phylogenetic Analysis, 365 -- Fiona S. ...
3rd ed. xviii, 540 s. : il. ; 29 cm
- MeSH
- Base Sequence methods MeSH
- Sequence Analysis, Protein methods MeSH
- Computational Biology methods MeSH
- Conspectus
- Biochemie. Molekulární biologie. Biofyzika
- NML Fields
- biochemie
Methods of biochemical analysis ; v. 39
370 s. : il.
... -- Alignment of Pairs of Sequences, 65 Chapter 4 -- Introduction to Probability and Statistical Analysis ... ... Regulation, 361 Chapter 10 -- Protein Classification and Structure Prediction, 409 -- Chapter 11 Genome Analysis ... ... , 495 -- Chapter 1 2 -- Bioinformatics Programming Using Perl and Perl Modules, 549 -- Chapter 1 3 -- ... ... Analysis of Microarrays, 611 -- Index, 667 vii ...
2nd ed. xii, 692 s. : il.
Metal-related genes (afe_0654, afe_0671, afe_0674, afe_1143, afe_1144, and afe_2126) were cloned to identify whether those genes existed in Acidithiobacillus ferrooxidans strain DC (A. ferrooxidans DC). The deduced amino acid sequences of those genes were analyzed by bioinformatics. The tolerance levels of A. ferrooxidans DC to Mn(2+), Zn(2+), and Cd(2+) were determined, which were 0.52, 0.42, and 0.16 mol/L for ferrous iron-grown cells and 0.38, 0.18, and 0.08 mol/L for sulfur-grown cells, respectively. Real-time quantitative PCR was employed to analyze the transcriptional levels of the metal-related genes when ferrous iron- and sulfur-grown cells of A. ferrooxidans DC, respectively, exposed to Mn(2+), Zn(2+), and Cd(2+). The metal-related genes were up-regulated when A. ferrooxidans DC exposed to Mn(2+). When A. ferrooxidans DC exposed to Zn(2+), the metal-related genes were up-regulated in sulfur-grown cells; afe_0654 and afe_0674 were down-regulated, and the others were up-regulated in ferrous iron-grown cells. Afe_2126 was down-regulated, and the others were up-regulated when A. ferrooxidans DC exposed to Cd(2+). According to experimental results and bioinformatics analysis, the proteins encoded by afe_0654 and afe_0674 may relate with Mn(2+) and Cd(2+) efflux. It needed further study whether they relate with Zn(2+) transport. Proteins encoded by afe_0671, afe_1143, and afe_1144 may relate with the efflux of Mn(2+), Zn(2+), and Cd(2+). The protein encoded by afe_2126 may relate with Mn(2+) and Zn(2+) efflux and Cd(2+) uptake.
- MeSH
- Acidithiobacillus drug effects genetics growth & development MeSH
- Genes, Bacterial MeSH
- Bacterial Proteins genetics metabolism MeSH
- Biological Transport MeSH
- Magnesium metabolism toxicity MeSH
- Cloning, Molecular MeSH
- Metals metabolism toxicity MeSH
- Real-Time Polymerase Chain Reaction MeSH
- Manganese metabolism toxicity MeSH
- Microbial Sensitivity Tests MeSH
- Gene Expression Regulation, Bacterial drug effects MeSH
- Sequence Analysis, DNA MeSH
- Sulfur metabolism MeSH
- Gene Expression Profiling MeSH
- Computational Biology MeSH
- Zinc metabolism toxicity MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Methods of biochemical analysis ; Vol. 43
2nd ed. xviii, 470 s. : il.
BACKGROUND: The successful sequencing of SARS-CoV-2 cleared the way for the use of omics technologies and integrative biology research for combating the COVID-19 pandemic. Currently, many research groups have slowed down their respective projects to concentrate efforts in the study of the biology of SARS-CoV-2. In this bibliometric analysis and mini-review, we aimed to describe how computational methods or omics approaches were used during the first months of the COVID-19 pandemic. METHODS: We analyzed bibliometric data from Scopus, BioRxiv, and MedRxiv (dated June 19th, 2020) using quantitative and knowledge mapping approaches. We complemented our analysis with a manual process of carefully reading the selected articles to identify either the omics or bioinformatic tools used and their purpose. RESULTS: From a total of 184 articles, we found that metagenomics and transcriptomics were the main sources of data to perform phylogenetic analysis aimed at corroborating zoonotic transmission, identifying the animal origin and taxonomic allocation of SARS-CoV-2. Protein sequence analysis, immunoinformatics and molecular docking were used to give insights about SARS-CoV-2 targets for drug and vaccine development. Most of the publications were from China and USA. However, China, Italy and India covered the top 10 most cited papers on this topic. CONCLUSION: We found an abundance of publications using omics and bioinformatics approaches to establish the taxonomy and animal origin of SARS-CoV-2. We encourage the growing community of researchers to explore other lesser-known aspects of COVID-19 such as virus-host interactions and host response.
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
INTRODUCTION: The presence of microbial invasion of the amniotic cavity (MIAC) and histological chorioamnionitis (HCA) is associated with adverse neonatal outcomes in pregnancies complicated by preterm prelabor rupture of membranes (pPROM). Therefore, there is an urgent need to identify new biomarkers revealing these conditions. The objective of this study is to identify possible biomarkers and their underlying biofunctions in pPROM pregnancies with and without MIAC and HCA. METHODS: A total of 72 women with pPROM were recruited. Only women with both MIAC and HCA (n = 19) and all women without these complications (n = 19) having the same range of gestational ages at sampling were included in the study. Samples of amniotic fluid were obtained by transabdominal amniocentesis, processed and analyzed using quantitative shotgun proteomics. Ingenuity pathway analysis was used to identify molecular networks that involve altered proteins. RESULTS: Network interaction identified by ingenuity pathway analysis revealed immunological disease and the inflammatory response as the top functions and disease associated with pPROM in the presence of MIAC and HCA. The proteins involved in these pathways were significantly altered between the groups with and without the presence of both MIAC and HCA. Proteins involved included histones H3, H4, H2B, cathelicidin antimicrobial peptide, myeloperoxidase, neutrophil gelatinase-associated lipocalin, matrix metalloproteinase-9, peptidoglycan recognition protein-1 and neutrophil defensin 1, all of which were found to be up-regulated in the presence of MIAC and HCA. CONCLUSION: Bioinformatic analysis of proteomics data allowed us to project likely biomolecular pathology resulting in pPROM complicated by MIAC and HCA. As inflammation is not a homogeneous phenomenon, we provide evidence for oxidative-stress-associated DNA damage and biomarkers of reactive oxygen species generation as factors associated with inflammation and proteolysis.
- MeSH
- Biomarkers metabolism MeSH
- Chorioamnionitis immunology metabolism MeSH
- Adult MeSH
- Histones metabolism MeSH
- Cohort Studies MeSH
- Humans MeSH
- Inflammation Mediators metabolism MeSH
- Metabolic Networks and Pathways MeSH
- Young Adult MeSH
- Infant, Newborn MeSH
- Oxidative Stress MeSH
- Fetal Membranes, Premature Rupture immunology metabolism MeSH
- Proteomics MeSH
- Case-Control Studies MeSH
- Pregnancy MeSH
- Computational Biology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Infant, Newborn MeSH
- Pregnancy MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
A never-before-seen wealth of body function of living things data has been created by the human gene project and putting in correct order projects in other living things. The huge demand for analysis and understanding of these data is being managed by the changing and getting better science of bioinformatics. Bioinformatics is defined as the use of tools of computation and analysis to the act of recording by a computer and understanding of function of living things data. It is a combined field, which captures and controls computer science, mathematics, physics, and qualities of living things. Bioinformatics is extremely important for management of data in modern qualities of living things and medicine. So the need of bioinformatics is more important now days.
