Biological Data Management Syllabus for VTU BE 2017 Scheme (Open Elective-2)

Biological Data Management detail syllabus for various departments, 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17BT661), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.

For all other open elective-2 syllabus for vtu be 2017 scheme you can visit Open Elective-2 syllabus for VTU BE 2017 Scheme Subjects. The detail syllabus for biological data management is as follows.

Course Objectives:

This course will enable students

• To understand the types of databases and their data formats.
• To study the importance of various Omics experiments, data generation techniques, data management strategies and their effective utilization
• To comprehend the nature of Clinical Data, its Management and related basic operations

Module 1
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Module 2

IMPORTANCE OF MICROARRAY: DNA Microarray and its importance, Designing a MicroArray Experiment-The Basic steps, Types of MicroArray. NCBI and MicroArray Data Management, GEO (Gene Expression Omnibus), MAML, The benefits of GEO and MAML, The Promise of MicroArray Technology in Treating Disease.MicroArray DataPreprocessing, Data normalization, Measuring Dissimilarity of Expression Pattern-Distance Motifs and Dissimilarity measures, Visualizing MicroArray Data. Principal Component Analysis,MicroArray Data. NCBI and MicroArray Data Management, GEO (Gene Expression Omnibus), MAML, The benefits of GEO and MAML, The Promise of MicroArray Technology in Treating Diseases.Data Mining for specific applications.

Module 3

IMPORTANCE OF OMIC TECHNOLOGIES, NGS DATA COLLECTION AND BIOINFORMATICS PRINCIPLES: Data standards for omic data: the basis of data sharing and reuse. Omic data management and annotation. Data and knowledge management in cross omics research projects. Statistical analysis principles for omic data. Statistical methods and models for bridging Omics data levels. Analysis of time course omic datasets. The use and abuse of Omes. Computational analysis of High Throughput Sequencing Data Analysis of SNP in case control studies. Bioinformatics for RNomics. The ENCODE project consortium. Data Mining for specific applications.

Module 4
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Module 5

CLINICAL DATA MANAGEMENT:
Overview of Clinical Data Management plan, CRF design consideration, Data cleaning issues and Data processing issues, Database design consideration: Making design decisions, Operating procedures for database design, Dealing with problem data, modifying data, Quality control through database audits, Identifying and managing discrepancies, Quality control and assurance, Managing laboratory data, Storing lab data, Creating report and transferring data, Clinical data management systems, Electronic data capture systems, System Validation, Migrating, data integration and archiving data. Data Normalization and Querying Techniques. Data Mining for desired applications.

Course Outcomes:

After studying this course, students will be able to:

• Decipher the differences in the types of databases and their data formats.
• Apply the knowledge of various Omics experiments, data generation techniques, data management concepts, data mining strategies and their effective utilization.
• To comprehend the aspects of Clinical Data, data integration, data Management, data mining for defined applications.

Reference Books:

1. DovStekel, Microarray Bioinformatics, Cambridge University Press, 2003.
2. Draghic S., Chapman, Data Analysis tools for DNA Microarray, Hall/ CRC Press, 2002.
3. Biological Data Mining, Jake Y. Chen, Stefano Lonardi, 2017 by Chapman and Hall/CRC 4.OMICS: Biomedical Perspectives and Applications, DebmalyaBarh, Kenneth Blum, Margaret A. Madigan, CRC, 2017.9
4. Bioinformatics for Omics Data, Methods and Protocols, Editors: Mayer, Bernd (Ed.), Methods in Molecular Biology, 719. Humana Press

Text Books:

1. Bioinformatics Database Systems, Byron et al., CRC Press, 2017, ISBN 978-1-4398-1247-1.
2. Data Mining in Bioinformatics, Wang et al. (eds), Springer, 2005, ISBN 1-85233-671-4.
3. Computational Biology and Genome Informatics, Wang et al. (eds), World Scientific, 2003, ISBN 981-238-257-7.
4. Pattern Discovery in Biomolecular Data: Tools, Techniques and Applications, Wang et al. (eds), Oxford University Press, 1999, ISBN 0-19-511940-1.
5. Microarray Technology and Its Applications, Uwe R. Muller, Dan V. Nicolau, pringer, 2005.

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