Basic level (see Enrolment Level)
PTM:
- in General Bioinformatics
- for VET in Healthcare
- for VET in Food science
- for VET in Agriculture
- for VET in Environmental science
- in General Bioinformatics
- for VET in Healthcare
- for VET in Food science
- for VET in Agriculture
- for VET in Environmental science
in General Bioinformatics
Summary and objectives
Bioinformatics is the science combining utilization of computer and biological data. It's the instrument we can use to understand biological processes and to answer of numerous others questions. Entirely, bioinformatics is a subset of the bigger field of computational science, the use of quantitative scientific strategies in modelling biological systems. The field of bioinformatics depends vigorously on work by specialists with statistical methods and pattern recognition. A lot of what we currently consider as a major aspect of bioinformatics — sequence comparison, sequence database searching, sequence analysis — is more complicated than simply outlining and setting public databases. Bioinformaticians (or computational scientists) go beyond simply downloading, managing, and introducing information, drawing motivation from a wide variety of quantitative fields, including statistics, physics, material science, software engineering. The questions that drive bioinformatics development are similar that people have at in applied biology for the last couple of hundred years. How might we cure disease? How might we prevent infection? How might we produce enough food to sustain all of mankind? Organizations working in the field of drugs development, agricultural chemicals, hybrid plants, plastics and other petroleum derivatives, and biological approaches to environmental remediation, among others, are creating bioinformatics divisions and looking to bioinformatics to give new targets and to help replace scarce natural resources.
There are several approaches that are integrated in modern bioinformatics. They are such as:
- Single sequence analysis and sequence characterization
- Pairwise alignment and DNA / protein sequence searching
- Multiple sequence alignment
- Sequence motif discovery in multiple alignments
- Phylogenetic analysis
Applying these methods can help in understanding the function of a particular gene or the mechanism of action of a particular protein. Furthermore, the experimental strategies for analyzing one gene or one protein are progressively replaced by parallel approaches in which many genes are examined simultaneously. Using bioinformatics algorithms information from multiple sources can be integrated to form a complete picture of genomic function and its expression, as well as to allow comparison between the genomes of different organisms.
Training modules
LO1: Biology, biological databases, and high-throughput data sources
LO2: Alignments and phylogenetic trees
LO3: Omics and system biology
References
- Baxevanis A.D., Ouellette B. F. F. (2004) Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 3rd Edition, John Wiley & Son, New York
- Elloumi M., Zomaya A. Y. (2011) Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications, John Wiley a& Son, New York
- Liu L., Agren R., Bordel S., Nielsen J. (2010) Use of genome-scale metabolic models for understanding microbial physiology. FEBS Letters 584: 2556–2564.
- Milne C.B., Kim P.J., Eddy J.A., Price N.D. (2009) Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology. Biotechnol J. 4(12):1653-70
- Pevzner P., Shamir R. (2011) Bioinformatics for Biologists, 1st Edition, Cambrage University Press
- Ramsden J. (2015) Bioinformatics: An Introduction, Springer-Verlag, London
- Singh G. B. (2015) Fundamentals of Bioinformatics and Computational Biology, Springer International Publishing, Switzerland
Assessment & Certification
PTM General Bioinformatics – basic level assessment scheme
Part-time Training assessment | |
---|---|
Evening training | Learning duration of the PTM: 90 h Proposed time schedule: 3 h/evening Total time 30 days Credit points: 3 |
Weekend training | Learning duration of the PTM: 90 h Proposed time schedule: 9 h/weekend Total time 10 weekends Credit points: 3 |
Distance Training assessment | |
Learning duration of the PTM: 90 h Proposed time schedule: 6 h/week Total time 15 weeks Credit points: 3 |
Additional resources
Useful links
- Online Courses in Bioinformatics
- Bioinformatics Training
- EMBL-EBI Train online
- MAGPIE: Multipurpose Automated Genome Project Investigation Environment
- Microbial Genome Databases (MBGD)
- Comparative Genome Analysis in P.Brok Laboratory
- TIGR:The Comprehensive Microbial Resource
- U.S Dept. of Energy Joint Genome Initiative
- Genome Databases at NCBI (index)
- Genome Databases at NCBI (Entrez)
- Genome Databases at NCBI (PMGif) Genome List in NIH
- Mitochondrial DNA Database MitBASE
- Alfresco:Visualization Tool for Genome Comparison
- Allegens.org:A Comparative gene Index(catalog) derived from EST and Predicted Genes
- CGAP:Cancer Genome Anatomy Project
- COG:Cluster of Orthologous group A Gene Classification System
- DOGS:Databases of Genome sizes
- E-CELL A modelling and Simulation Environment for Biochemical and Genetic Processes
- FAST_PAN for automatic searches of online EST Database to Identify new Family Members
- GeneCensus Genome Comparison by Encoded Protein Structures
- GeneQuiz:An Integrated System for large Scale Biological Sequence Analysis and Data Management
- Gene and Disease:Map Location on Human Chromosomes
- Genome Channel at Oak Ridge National Laboratories
- GOLD: Genome Online Database
- IMGT: ImMunoGeneTics Database
- Specializing in Immunoglobulin,T-Cell Receptor,and Major Histocompatibility Complex(MHC)of all Vertibrate Species
- KEGG:Kyto Encyclopedia of Gene and Genomes
- MIA:Molecular Information Agent
- Orthologous Gene Alignment at TIGR
- PEDANT: A Protein Extraction, Description and Analysis Tool
- SEQUEST for Identification of Proteins Following Mass Spectrometry
- STRING:Search Tool for Recurring Instances of Neighboring Genes
- Taxonomy Browser at NCBI arranges genomes taxonomically for sequence retrieval
- 2DGel Analysis of Protein: List oF Organism
- MetaCus metabolic Encyclopedia
- Microarray Guide
- Microarray Project at NCBI
- Microarray Software
- Microarrays.org: A new Public source for Microarraying information,tools,and Protocols
- SMART: for the Study of Genetically mobile protein Domaines
- SWISS-2DPAGE:Two Dimentional Polyacrylamide Gel Electrophoresis Database
- PDB: Protein Data Bank
- Molecular Modelling Database(MMDB)
- Structural classification of protein at Cambridge University(SCOP)
- Biomolecular structure and modelling group at the University college ,London
- Europian Bioinformatics institute Hinxton,Cambridge
- Swiss Institute of Bioinformatics
- D-Ali
- 3D-PSSM
- BLOCKS
- COGS: Clusters of Orthologous Group Database and Search site
- DIP:Database of Interacting Protein
- eMOTIF
- HOMSTRAD
- HSSP:Sequence similar to proteins of known structure
- INTERPRO: Integrated resource of protein domain and functional sites
- LPFC
- Pfam
- PIR
- PRINTS
- PROCLASS
- Prosite
Scientific Journals
- Bioinformatics
- Research Journal of Life Sciences, Bioinformatics, Pharmaceutical and Chemical Sciences
- Advances in Bioinformatics
- Comparative and Functional Genomics
- BMC Bioinformatics
- BMC Genomics
- BMC Systems Biology
- Computational Molecular Biology
- EURASIP Journal on Bioinformatics and Systems Biology
- Genomics and Computational Biology
- International Journal of Data Mining and Bioinformatics
- Molecular & Cellular Proteomics
- Proteins: Structure, Function, and Bioinformatics
- Statistical Applications in Genetics and Molecular Biology
for VET in Healthcare
Summary and objectives
Bioinformatics is a rapidly growing field. It is defined as the application of tools of computation and analysis to the conception and understanding of biological data. It is an interdisciplinary field, which utilizes computer science, mathematics, physics, and biology. Bioinformatics is essential for management of data in modern biology and medicine. Translational bioinformatics is an emerging field in the study of health informatics that focuses on the convergence of molecular bioinformatics, biostatistics, statistical genetics and clinical informatics. Genomics is fundamental to precision medicine which, through its four components of predictive, preventive, personalized, and participatory medicine, (P4 medicine) aims to promote wellness specific to the individual as well as to treat disease more precisely. Recent efforts have focused on the use of the omics data, especially genomics, to discover new drug targets and search for new uses for existing drugs, referred to as drug repositioning. Pharmacogenomics can be defined as the study of how genetic factors affect a person’s response to drugs. This relatively new field combines pharmacology (the science of drugs) and genomics (the study of genes and their functions) to develop effective, safe medications and doses tailored to a person’s genetic makeup. Personalized medicine has the potential to help patients receive the best possible outcomes while reducing adverse effects and high direct medical costs if a treatment will not benefit the patient. Genetic and genomic tests each have a place in personalized medicine. Access to an individual’s genomic sequence and other ‘omics’ data can enable a more detailed understanding of health and disease risks, and inform a more precise approach to patient care, a strategy now commonly called ‘precision medicine’.
Computational health informatics (CHI) is an emerging and multidisciplinary field involving various sciences such as biomedical, medical, nursing, information technology, computer science, and statistics. CHI addresses how computational methods relate to providing health care. Using Information and Communication Technologies (ICTs), health informatics collects and analyzes the information from all healthcare domains to predict patients’ health status. The major goal of health informatics research is to improve the quality of care provided to patients or Health Care Output (HCO). Big data analytics, a popular term given to datasets which are large and complex, play a vital role in managing the huge healthcare data and improving the quality of healthcare offered to patients. In addition, it promises a bright prospect for decreasing the cost of care, improving treatments, reaching more personalized medicine, and helping doctors and physicians to make personalized decisions.
Training modules
LO7: Health bioinformatics
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Assessment & Certification
PTM Health Bioinformatics assessment scheme
Part-time Training assessment | |
---|---|
Evening training | Learning duration of the PTM: 45 h Proposed time schedule: 3 h/evening Total time 15 days Credit points: 1,5 |
Weekend training | Learning duration of the PTM: 45 h Proposed time schedule: 5 h/weekend Total time 9 weekends Credit points: 1,5 |
Distance Training assessment | |
Learning duration of the PTM: 45 h Proposed time schedule: 9 h/week Total time 5 weeks Credit points: 1,5 |
for VET in Food science
Summary and objectives
Bioinformatics approach for successful development of food production and engineering is a smart decision of the sophisticated interpretation of the great biological data sources accumulated with the introduction of advanced “omics” techniques. The module Bioinformatics in food production and engineering presents information and provides useful examples on the current developments of bioinformatics that are providing simple and convenient ways for improving the food research and technologies. Special emphasis in the training material is given to the benefits for the food production and nutrition offered by the implementation of the applied bioinformatics approach. The contribution of bioinformatics in reconstruction of metabolic pathways, in the view point of genetics and molecular biology is discussed. The role of bioinformatics in improving the production of biomass and metabolites, enhancing crop production and food processing, and making better the food texture and flavour is also revealed.
The module discusses as well the contribution of bioinformatics in solving the major problems of food quality and safety. Details about the specific food characteristics effecting its quality and the bioinformatics approach to manipulate them are given. The application of bioinformatics in food risk assessment; tracing and detection of food microorganisms; and the impact of toxicogenomics on the food quality assurance are discussed. Finally, the training material outlines the major trends in bioinformatics implementation in food production, engineering and safety.
Training modules
LO8: Bioinformatics in food production and engineering
References
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Assessment & Certification
PTM Food science assessment scheme
Part-time Training assessment | |
---|---|
Evening training | Learning duration of the PTM: 45 h Proposed time schedule: 3 h/evening Total time 15 days Credit points: 1,5 |
Weekend training | Learning duration of the PTM: 45 h Proposed time schedule: 5 h/weekend Total time 9 weekends Credit points: 1,5 |
Distance Training assessment | |
Learning duration of the PTM: 45 h Proposed time schedule: 9 h/week Total time 5 weeks Credit points: 1,5 |
Additional resources
Trainig resources
- Bioinformatics Approaches in Food Sciences
- Microbial bioinformatics for food safety and liroduction
- Regulatory bioinformatics for food safety
- Bioinformatics, a necessary step to advance food research
- Food security and bioinformatics
- Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access
- The Course Source Bioinformatics Learning Framework
for VET in Agriculture
Summary and objectives
The last decade were considered to be a new era of bioinformatics and computational biology which boosts the scientific invention in life science. Involvement of computer science in the area of plant biology has changed the way we usually do research related to plants. Rapid ground breaking progress of sequencing technology during the few last years made this approach so cost-effective that nowadays it is common for any experimental lab and is used to study the genome of interest.
Inclusion of modern biotechnology progress in agriculture provided huge dividends to the bioenergy sector, agro-based industries, agricultural by-products utilization, plant improvement and better management of the environment. Latest plant genomics and transcriptomics studies give the opportunity to reveal the genetic architecture of many plant species, the existing differences within and outside population, the genes and mutations which are essential for improving the particular wanted complex traits. Furthermore, collection and storage of plant genetic resource can be used to manufacture stronger, disease and insect resistant crops. Comparative genetics of the model and non-model plant species provide an information on organization of their genes which are used after that for transferring information from the model crop systems to other food crops.
At the most essential level, the progress in bioinformatics and genomics research will further considerably speed up the acquisition of knowledge and that, in turn, will directly effect on many aspects of the sustainable agricultural development.
Training modules
LO9: The role of bioinformatics in agriculture
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Assessment & Certification
PTM Agriculture assessment scheme
Part-time Training assessment | |
---|---|
Evening training | Learning duration of the PTM: 45 h Proposed time schedule: 3 h/evening Total time 15 days Credit points: 1,5 |
Weekend training | Learning duration of the PTM: 45 h Proposed time schedule: 5 h/weekend Total time 9 weekends Credit points: 1,5 |
Distance Training assessment | |
Learning duration of the PTM: 45 h Proposed time schedule: 9 h/week Total time 5 weeks Credit points: 1,5 |
Additional resources
Trainig resources
- Application of Bioinformatics in Agriculture
- Bioinformatics for Crop Improvement
- Agrigenomics
- Bioinformatics in Agricultural Science
- liroteomics and Bioinformatics in Agriculture Research and Crop Improvement
- Integrating Genomics and Bioinformatics in the lilant Breeding
- The Role of Bioinformatics in Agriculture
for VET in Environmental science
Summary and objectives
Bioremediation, a process mediated by microorganisms, is a sustainable way to degrade and detoxify environmental contaminants. It uses microorganisms to repair ecosystems and substances, through the removal of pollution or the release of beneficial compounds. Biological systems already play an important role cleaning up damage caused by environmental disasters such as oil-spills, chemical pollution, and nuclear leakage. The objective of the present education material is to summarize the applications of system biology in bioremediation and environmental applications of high-throughput technologies from the genomics, metagenomics, proteomics and metabolomics with case studies.
Training modules
LO10: Application of system biology in bioremediation
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- Zhou AF, He ZL, Qin YJ, Lu ZM, Deng Y, Tu QC, Hemme CL, Van Nostrand JD, Wu LY, Hazen TC, Arkin AP, Zhou JZ (2013) StressChip as a high-throughput tool for assessing microbial community responses to environmental stresses. Environ Sci Technol 47:9841–9849. doi:10.1021/es4018656
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Assessment & Certification
PTM Environmental science assessment scheme
Part-time Training assessment | |
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Evening training | Learning duration of the PTM: 45 h Proposed time schedule: 3 h/evening Total time 15 days Credit points: 1,5 |
Weekend training | Learning duration of the PTM: 45 h Proposed time schedule: 5 h/weekend Total time 9 weekends Credit points: 1,5 |
Distance Training assessment | |
Learning duration of the PTM: 45 h Proposed time schedule: 9 h/week Total time 5 weeks Credit points: 1,5 |
Additional resources
Trainig resources
- Learn bioremediation
- Bioremediation in the times of systemic biology
- Systems Biology Approach to Bioremediation
- Back to environment through bioinformatics
- Microbial remediation and bioinformatics
- Microbial Bioremediation in Omics era: Opportunities and Challenges
- Systems biology and bioremediation breakthroughs
- Integration of bioinformatics to biodegradation