BioTech-GO - Advanced level

PTM:

in General Bioinformatics

The content of this PTM is focused on the knowledge about the exploitation of databases and data resources as a critical research skill in bioinformatics. It presents information about the use of boolean logic and formulation of boolean queries for searching purposes. Special attention is paid to the use of the public biological databases with emphasis on understanding and exploiting data annotation and data formats, the 3D molecular structure data, the DNA, RNA, and protein sequence data, genomic data, biochemical pathway data and gene expression data. The use of the specialized interface for deposition of sequence and structure data into the public databases in GenBank and PDB are outlined. Useful clues are provided on how to find proven software for a particular application within the broad bioinformatics field and how to judge the quality of information and software in terms of source, comprehensiveness of offerings, and availability to the casual user.

Next, the PTM presents contemporary knowledge about multiple sequence alignment as versatile tool that can be used to study groups of related genes or proteins, to infer evolutionary relationships between genes, and to discover patterns that are shared among groups of functionally or structurally related sequences. Details about one of the most commonly used programmes for progressive multiple sequence alignment, Clustal Omega, are presented. Another subject discussed in the training material is the phylogenetic analysis as a practical application of the multiple sequence alignment. The principle of profile- or motif-based analysis: use of data derived from multiple alignments to construct and search for sequence patterns, is outlined. Furthermore, the PTM presents information on comparative genomics as a powerful tool for achieving a better understanding of the genomes and, subsequently, of the biology of the respective organisms. The recent advances in genome sequencing are summarized and several General-Purpose Databases for Comparative Genomics, that offer not only convenient access to sequence data but also provide important additional information, such as operon organization, functional predictions, three-dimensional structure, and metabolic reconstructions, are commented. They include PEDANT, COGs, KEGG, and MBGD, as well as the organism-specific databases for Escherichia coli, Mycoplasma genitalium, Bacillus subtilis, and Saccharomyces cerevisiae. It describes the operation of sequence comparison methods that are used for prediction of protein functions, the principles of transfer of functional information from well-characterized genomes to poorly-studied ones, the creation of phylogenetic patterns and their use in differential genome display for identification of gene products that are likely to contribute to specific characteristics of the studied organisms.

Training modules

LO4: Biology, biological databases, and high-throughput data sources

LO5: Alignments and phylogenetic trees

LO6: 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 Introducti on, Springer-Verlag, London
Singh G. B. (2015) Fundamentals of Bioinformatics and omputational Biology, Springer International Publishing, Switzerland

Module authors

Assignments and case-studies

Test questions

Assessment & Certification

PTM General Bioinformatics – advanced level assessment scheme

Part-time Training assessment
Evening training	Learning duration of the PTM: 120 h Proposed time schedule: 3 h/evening Total time 60 days Credit points: 4
Weekend training	Learning duration of the PTM: 120 h Proposed time schedule: 8 h/weekend Total time 15 weekends Credit points: 4
Distance Training assessment
	Learning duration of the PTM: 120 h Proposed time schedule: 8 h/week Total time 15 weeks Credit points: 4

BIOTECH-GO Certificate

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

References

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Module authors

Assignments and case-studies

Test questions

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

BIOTECH-GO Certificate

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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Brul S. Schuren F. Montijn R. Keijser B. J. F. Van Der Spek H. & Oomes S. J. C. M. (2006). The impact of functional genomics on microbiological food quality and safety. International Journal of Food Microbiology 112 195-199.
Carrasco-Castilla J. Hernandez-Alvarez A. J. Jimenez-Martınez C. Gutierrez-Lopez G. F. & Davila-Ortiz G. (2012). Use of proteomics and peptidomics methods in food bioactive peptide science and engineering. Food Engineering Reviews 4 224-243.
Chibuike C. Udenigwe Bioinformatics approaches prospects and challenges of food bioactive peptide research Trends in Food Science & Technology Volume 36 Issue 2 April 2014 Pages 137-143 ISSN 0924-2244.
Desiere F., German B., Watzke H., Pfeifer A., Saguy S. (2001). Bioinformatics and data knowledge: the new frontiers for nutrition and foods. Trends in Food Science & Technology 12 (7): 215-229; ISSN0922244 http://dx.doi.org/10.1016/S09242244(01)00089-9.
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Liu M. Nauta A. Francke C. & Siezen R. J. (2008). Comparative genomics of enzymes in flavor-forming pathways from amino acids in lactic acid bacteria. Applied and Environmental Microbiology 74 4590-4600.
Mari A. Scala E. Palazzo P. Ridolfi S. Zennaro D. & Carabella G. (2006). Bioinformatics applied to allergy: allergen databases from collecting sequence information to data integration. The allergome platform as a model. Cellular Immunology 244 97-100.
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http://www.spss.com/ Clementine
http://www.ifst.org/fst.htm
http://snp.cshl.org
http://datafairport.org
http://www.research.ibm.com/client-programs/foodsafety/

Module authors

Assignments and case-studies

Test questions

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

BIOTECH-GO Certificate

Additional resources

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

References

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Module authors

Assignments and case-studies

Test questions

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

BIOTECH-GO Certificate

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

References

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Module authors

Assignments and case-studies

Test questions

Assessment & Certification

PTM Environmental 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

BIOTECH-GO Certificate

PTM:

in General Bioinformatics

Summary and objectives

Funding

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