Biological Big Data Management - Types, Analysis, Integration

This presentation covers Biological Big Data Management, its types, analysis, and integration. It explains the importance of data management in bioinformatics and the impact of big data on the field. The types of biological data covered include genomics, transcriptomics, proteomics, metabolomics, cytometry, and system biology. The presentation also covers the challenges of big data management and the need for data integration approaches.

BIOLOGICAL BIG DATA
MANAGEMENT

BIOLOGICAL DATA
 Biological data is the data which comes from every living organisms.
 Biological data have a important information about a particular organism.
 Biological data can be from human, animals, plants etc.
 Biological data retrieves with help of biotechnology, bioinformatics and branches
of science.
 Biological data can be a sequence, image, pattern, models, hypothesis or any
evidence etc.

TYPES OF BIOLOGICAL DATA
Genomics
 Nucleotide sequences
 Study of gene, gene finding, mutations prediction, sequence alignment
Transcriptomics
 RNA expression, transcription factor
Proteomics
 Protein structure, interaction, identification
 Protein prediction, structure, function prediction, structure comparison, molecular dynamics, simulation, docking.
Metabolomics
 Small molecules study/ metabolite
 Network analysis, pathway analysis
Cytometry
 Cell level study, cell population
 Population clustering, cell biomarker finding
System Biology
 Study all these above
 Simulation, modeling, networking study,
target prediction for drugs

BIOLOGICAL DATA
ANALYSIS
 Now days biological data is generating on large scale and
it decreases its quality
 This data comes from different ways like metagenomic
data analysis or big data analysis
 Having noise and more dimensions
 Need to remove all noise
 Make data in understandable format
 Prepare pipelines for the analysis of biological data

 Biological data analysis starts with the development of
sequence algorithm
 It generate multiple copies side by side
 Needs its statistical analysis
 Select the specific algorithm after setting the specific
parameter
 Development and its testing needs a huge data storage
and its retrieving system
 Handling of such big data like genomics and proteomics
needs good data management ways

BIG DATA

BIG DATA MANAGEMENT
 Bioinformatics tools still depend on the files stored locally
 Statistical analysis method is also complex
 Nowadays, handling of big data is big issue
 Example: META-pipe pipeline which is from metagenomic
 Works when dataset is of small size
 Now datasets are very large and data is stored in a global
file system
 There is also a distributed data storage system such as
HDFS, GPFS and the Google file system
 Others system also there like RDBM System, NoSQL cluster
 Rational databases(MySQL) are connected to distributed
databases
 Example: Neo4j is graph data base used in bioinformatics
for protein interaction.

INTEGRATION OF
BIOLOGICAL DATA
 Everyone can store, access and analyze the biological
data which is available one the private side or web.
 Integration of all biological data into a single source make
easy to access the data
 Bioinformatics and I.T. gives solution:
- NCBI Entrez
- Multi- databases(TAMBIS)
- Data warehousing
- Distributed data system

CONCLUSION
 Big data has main impact on the bioinformatics field.
 Needs many public databases and data management
projects
 Classification analysis system
 Data integration approaches
 Biological data approaches
 System should be bio friendly

ACKNOWLEDGEMENT
 Wooley JC, Lin HS(2005).Catalyzing Inquiry at the Interface of Computing and Biology.
Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK25464/
 E.Weitschek, Genomic Big Data Management Integration and Mining. Retrieved from
https://www.slideshare.net/DataDrivenInnovation/genomic-big-data-management-
integration-and-mining-emanuel-weitschek
 Transcriptomics. Retrieved from https://www.nature.com/subjects/transcriptomics
 Metabolomics. Retrieved from
https://www.ebi.ac.uk/training/online/course/introduction-metabolomics/what-
metabolomics
 Institute for system biology. Retrieved from https://www.systemsbiology.org/
 Bioinformatics, Retrieved from
http://www.philnat.unibe.ch/studies/study_programs/master_s_in_bioinformatics_and_c
omputational_biology/index_eng.html
 Implementation of a GPU-based image analysis pipeline for structural biology,
Retrieved from http://bidac.sci.utah.edu/projects/14-projects-past/10-implementation-
of-a-gpu-based-image-analysis-pipeline-for-structural-biology.html
 Divya Kumari , Ravi Kumar(2014,September). Impact of Biological Big Data in
Bioinformatics
 Edvard Pedersen, Lars Ailo Bongo (n.d.), Big Biological Data Management
 Big Data, Retrieved from ccs.miami.edu
 Lindsay Barone , Jason Williams, David Micklos (2017, October 19). Unmet needs for
analyzing biological big data: A survey of 704 NSF principal investigators, PLOS
Computational Biology
 Deepika Das(2015). Will Big Data Make Personalized Medicine a Reality? Retrieved
from http://blog.syntelinc.com/will-big-data-make-personalized-medicine-a-reality/

THANK YOU